diff --git a/.gitignore b/.gitignore index c5c397b..11e0cf8 100644 --- a/.gitignore +++ b/.gitignore @@ -11,3 +11,4 @@ _templates/* __pycache__/ .cache .python-version +venv/* diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..ef920c0 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 一叶知秋 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Schedule/demo.py b/Schedule/demo.py new file mode 100644 index 0000000..bc43054 --- /dev/null +++ b/Schedule/demo.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import time + +import schedule + + +def job(): + print("I'm working...") + + +# 每十分钟执行任务 +schedule.every(10).minutes.do(job) +# 每个小时执行任务 +schedule.every().hour.do(job) +# 每天的10:30执行任务 +schedule.every().day.at("10:30").do(job) +# 每个月执行任务 +schedule.every().monday.do(job) +# 每个星期三的13:15分执行任务 +schedule.every().wednesday.at("13:15").do(job) +# 每分钟的第17秒执行任务 +schedule.every().minute.at(":17").do(job) +# 每10秒执行任务 +schedule.every(10).seconds.do(job) +while True: + schedule.run_pending() + time.sleep(1) diff --git a/Schedule/demo10.py b/Schedule/demo10.py new file mode 100644 index 0000000..025a5a7 --- /dev/null +++ b/Schedule/demo10.py @@ -0,0 +1,44 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + + +import functools + +import schedule + +""" +Schedule 不会自动捕捉异常，它遇到异常会直接抛出，这会导致一个严重的问题：后续所有的作业都会被中断执行，因此我们需要捕捉到这些异常。 +""" + + +def catch_exceptions(cancel_on_failure=False): + def catch_exceptions_decorator(job_func): + @functools.wraps(job_func) + def wrapper(*args, **kwargs): + try: + return job_func(*args, **kwargs) + except: + import traceback + print(traceback.format_exc()) + if cancel_on_failure: + return schedule.CancelJob + + return wrapper + + return catch_exceptions_decorator + + +@catch_exceptions(cancel_on_failure=True) +def bad_task(): + return 1 / 0 + +@catch_exceptions(cancel_on_failure=True) +def job(): + print("i'm working...") + + +schedule.every(5).seconds.do(bad_task) +schedule.every(7).seconds.do(job) + +while True: + schedule.run_pending() diff --git a/Schedule/demo2.py b/Schedule/demo2.py new file mode 100644 index 0000000..c04edb6 --- /dev/null +++ b/Schedule/demo2.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import time +import schedule + +""" +只运行一次任务 +""" + + +def job_that_executes_once(): + print('i\'m working...') + # 此处编写的任务只会执行一次... + return schedule.CancelJob + + +schedule.every().day.at('22:30').do(job_that_executes_once) +while True: + schedule.run_pending() + time.sleep(1) diff --git a/Schedule/demo3.py b/Schedule/demo3.py new file mode 100644 index 0000000..7a2dbb5 --- /dev/null +++ b/Schedule/demo3.py @@ -0,0 +1,33 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import time +import schedule + +""" +获取目前所有的作业 +取消所有作业 +""" + +def greet(name): + print('Hello', name) + + +def hello(): + print('Hello world') + + +# do() 将额外的参数传递给job函数 +schedule.every(2).seconds.do(greet, 'Alice') +schedule.every(4).seconds.do(greet, 'Bob') +schedule.every(6).seconds.do(greet, name='jack') + +schedule.every().second.do(hello) +# 获取目前所有的作业 +all_jobs = schedule.get_jobs() +print(all_jobs) +# 取消所有作业 +schedule.clear() + +while True: + schedule.run_pending() + time.sleep(1) diff --git a/Schedule/demo4.py b/Schedule/demo4.py new file mode 100644 index 0000000..9c7d775 --- /dev/null +++ b/Schedule/demo4.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import time + +import schedule + +""" +标签功能 +""" + + +def greet(name): + print('Hello {}'.format(name)) + + +# .tag 打标签 +schedule.every().day.do(greet, 'Andrea').tag('daily-tasks', 'friend') +schedule.every().hour.do(greet, 'John').tag('hourly-tasks', 'friend') +schedule.every().hour.do(greet, 'Monica').tag('hourly-tasks', 'customer') +schedule.every().day.do(greet, 'Derek').tag('daily-tasks', 'guest') + +# get_jobs(标签)：可以获取所有该标签的任务 +friends = schedule.get_jobs('friend') + +# 取消所有 daily-tasks 标签的任务 +schedule.clear('daily-tasks') + +while True: + schedule.run_pending() + time.sleep(1) diff --git a/Schedule/demo5.py b/Schedule/demo5.py new file mode 100644 index 0000000..2fa323e --- /dev/null +++ b/Schedule/demo5.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from datetime import datetime, timedelta, time +import schedule + +""" +设定作业截止时间 +""" + + +def job(): + print('Boo') + + +# 每个小时运行作业，18:30后停止 +schedule.every(1).hours.until('18:30').do(job) + +# 每个小时运行作业，2030-01-01 18:33 today +schedule.every(1).hours.until('2030-01-01 18:33').do(job) + +# 每个小时运行作业，8个小时后停止 +schedule.every(1).hours.until(timedelta(hours=8)).do(job) + +# 每个小时运行作业，11:32:42后停止 +schedule.every(1).hours.until(time(11,32,42)).do(job) + +# 每个小时运行作业，2020-5-17 11:36:20后停止 +schedule.every(1).hours.until(datetime(2020, 5, 17, 11, 36, 20)).do(job) + +# 截止日期之后，该作业将无法运行。 +while True: + schedule.run_pending() diff --git a/Schedule/demo6.py b/Schedule/demo6.py new file mode 100644 index 0000000..971d806 --- /dev/null +++ b/Schedule/demo6.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import schedule + +""" +立即运行所有作业，而不管其安排如何 +""" + + +def job_1(): + print('Foo') + + +def job_2(): + print('Bar') + + +schedule.every().monday.at("12:40").do(job_1) +schedule.every().tuesday.at("16:40").do(job_2) + +schedule.run_all() +# 立即运行所有作业，每次作业间隔10秒 +schedule.run_all(delay_seconds=10) diff --git a/Schedule/demo7.py b/Schedule/demo7.py new file mode 100644 index 0000000..23ac386 --- /dev/null +++ b/Schedule/demo7.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import time +from schedule import every,run_pending,repeat + +""" +装饰器安排作业 +""" +# 此装饰器效果等同于 schedule.every(10).minutes.do(job) +@repeat(every(10).minutes) +def job(): + print("I am a scheduled job") + +while True: + run_pending() + time.sleep(1) \ No newline at end of file diff --git a/Schedule/demo8.py b/Schedule/demo8.py new file mode 100644 index 0000000..c4181e3 --- /dev/null +++ b/Schedule/demo8.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import threading +import time +import schedule +""" +并行执行 + +默认情况下，Schedule 按顺序执行所有作业。其背后的原因是，很难找到让每个人都高兴的并行执行模型。 + +不过你可以通过多线程的形式来运行每个作业以解决此限制： +""" +def job1(): + print("I'm running on threads %s" % threading.current_thread()) +def job2(): + print("I'm running on threads %s" % threading.current_thread()) +def job3(): + print("I'm running on threads %s" % threading.current_thread()) + +def run_threaded(job_func): + job_thread = threading.Thread(target=job_func) + job_thread.start() + +schedule.every(10).seconds.do(run_threaded, job1) +schedule.every(10).seconds.do(run_threaded, job2) +schedule.every(10).seconds.do(run_threaded, job3) + +while True: + schedule.run_pending() + time.sleep(1) diff --git a/Schedule/demo9.py b/Schedule/demo9.py new file mode 100644 index 0000000..604e74e --- /dev/null +++ b/Schedule/demo9.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import logging +import schedule +""" +日志记录 +""" +logging.basicConfig() +schedule_logger = logging.getLogger('schedule') +# 日志级别为DEBUG +schedule_logger.setLevel(level=logging.DEBUG) + + +def job(): + print("Hello, Logs") + + +schedule.every().second.do(job) +schedule.run_all() + +schedule.clear() diff --git "a/YouTubesphb/YouTube \351\237\263\350\247\206\351\242\221\345\220\210\345\271\266.bat" "b/YouTubesphb/YouTube \351\237\263\350\247\206\351\242\221\345\220\210\345\271\266.bat" new file mode 100644 index 0000000..fb61acd --- /dev/null +++ "b/YouTubesphb/YouTube \351\237\263\350\247\206\351\242\221\345\220\210\345\271\266.bat" @@ -0,0 +1,12 @@ +@echo off +title YouTube Ƶϲ 20140127 +echo ߣCrexyer +echo վhttp://www.crexyer.com/ +echo. +set /p audio=뽫Ƶļק˴»سȷϣ +set /p video=뽫Ƶļק˴»سȷϣ +ffmpeg.exe -i %audio% -i %video% -acodec copy -vcodec copy output.mp4 +echo. +echo ϲ +echo ļѾص output.mp4 ļ +pause \ No newline at end of file diff --git a/YouTubesphb/ffmpeg.exe b/YouTubesphb/ffmpeg.exe new file mode 100644 index 0000000..c6fc02e Binary files /dev/null and b/YouTubesphb/ffmpeg.exe differ diff --git a/algorithm_datastructure/dict_tools.py b/algorithm_datastructure/dict_tools.py index 0ae1541..316c67a 100644 --- a/algorithm_datastructure/dict_tools.py +++ b/algorithm_datastructure/dict_tools.py @@ -4,6 +4,7 @@ class ObjectDict(dict): """from tornado.util.ObjectDict, access dict by dot""" + def __getattr__(self, key): try: return self[key] @@ -16,7 +17,6 @@ def __setattr__(self, key, value): import collections - class TransformedDict(collections.MutableMapping): """A dictionary that applies an arbitrary key-altering function before accessing the keys""" @@ -43,10 +43,12 @@ def __len__(self): def __keytransform__(self, key): return key + # pip install fronzendict class FronzenDict(TransformedDict): """immutable dict, init by a dict""" + def __init__(self, d): self.store = d @@ -57,4 +59,7 @@ def __setitem__(self, key, value): # since python3.3, you can use immutable dict import types -d_proxy = types.MappingProxyType(d) # can add new key/value by assignment but can not assign key alrady exists + +d = {1: 'hello', 2: 'world'} +d_proxy = types.MappingProxyType(d) # can add new key/value by assignment but can not assign key alrady exists +print(d_proxy) \ No newline at end of file diff --git a/args/args_paser.py b/args/args_paser.py new file mode 100644 index 0000000..7a3c8f8 --- /dev/null +++ b/args/args_paser.py @@ -0,0 +1,33 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + + +''' +Hypothetical command-line tool for searching a collection of +files for one or more text patterns. +''' +import argparse + +parser = argparse.ArgumentParser(description='Search some files') +parser.add_argument(dest='filenames', metavar='filename', nargs='*') +parser.add_argument('-p', '--pat', metavar='pattern', required=True, + dest='patterns', action='append', + help='text pattern to search for') + +parser.add_argument('-v', dest='verbose', action='store_true', + help='verbose mode') + +parser.add_argument('-o', dest='outfile', action='store', + help='output file') + +parser.add_argument('--speed', dest='speed', action='store', + choices={'slow', 'fast'}, default='slow', + help='search speed') + +args = parser.parse_args() +# Output the collected arguments +print(args.filenames) +print(args.patterns) +print(args.verbose) +print(args.outfile) +print(args.speed) diff --git a/autotest/curl_to_request.py b/autotest/curl_to_request.py index 41fcfe4..4e157b8 100644 --- a/autotest/curl_to_request.py +++ b/autotest/curl_to_request.py @@ -49,5 +49,5 @@ def parse_curl_str(s): if __name__ == '__main__': - s = """'curl http://pre3.papayamobile.com:1267/shoptimize/product/get_category_ids' -H 'Cookie: language=zh_CN; _gat=1; ppysid="PMs5bkeZejk5DZ9YuE+XRySRFW58DvWlgNYfI2YP+SQ="; save=true; email=fan_ll%40qq.com; password=6be037423107205d176a1d0d174402fac6b1dcc3d1b99b894fdbdf1d52ea9935; login_type=pmd; Hm_lvt_4f18dfc7029aaa9ff0b37ede9b128ef4=1463108257; Hm_lpvt_4f18dfc7029aaa9ff0b37ede9b128ef4=1463587952; _ga=GA1.2.2053141674.1463108258' -H 'Origin: http://pre3.papayamobile.com:1267' -H 'Accept-Encoding: gzip, deflate' -H 'Accept-Language: zh-CN,zh;q=0.8,en-US;q=0.6,en;q=0.4' -H 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.94 Safari/537.36' -H 'Content-Type: application/x-www-form-urlencoded; charset=UTF-8' -H 'Accept: application/json, text/javascript, */*; q=0.01' -H 'Referer: http://pre3.papayamobile.com:1267/shoptimize/campaign?type=website' -H 'X-Requested-With: XMLHttpRequest' -H 'Connection: keep-alive' --data 'product_type=0&account_id=2' --compressed""" + s = """curl 'http://pre3.papayamobile.com:1267/shoptimize/product/get_category_ids' -H 'Cookie: language=zh_CN; _gat=1; ppysid="PMs5bkeZejk5DZ9YuE+XRySRFW58DvWlgNYfI2YP+SQ="; save=true; email=fan_ll%40qq.com; password=6be037423107205d176a1d0d174402fac6b1dcc3d1b99b894fdbdf1d52ea9935; login_type=pmd; Hm_lvt_4f18dfc7029aaa9ff0b37ede9b128ef4=1463108257; Hm_lpvt_4f18dfc7029aaa9ff0b37ede9b128ef4=1463587952; _ga=GA1.2.2053141674.1463108258' -H 'Origin: http://pre3.papayamobile.com:1267' -H 'Accept-Encoding: gzip, deflate' -H 'Accept-Language: zh-CN,zh;q=0.8,en-US;q=0.6,en;q=0.4' -H 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.94 Safari/537.36' -H 'Content-Type: application/x-www-form-urlencoded; charset=UTF-8' -H 'Accept: application/json, text/javascript, */*; q=0.01' -H 'Referer: http://pre3.papayamobile.com:1267/shoptimize/campaign?type=website' -H 'X-Requested-With: XMLHttpRequest' -H 'Connection: keep-alive' --data 'product_type=0&account_id=2' --compressed""" pprint(parse_curl_str(s)) diff --git a/boring_program/boring.py b/boring_program/boring.py new file mode 100644 index 0000000..baf910c --- /dev/null +++ b/boring_program/boring.py @@ -0,0 +1,13 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +""" +from:https://mp.weixin.qq.com/s/R9Z0LR9htZcgl7vVvY1sdQ +以下程序，不要发代码，要不实现不了你整蛊的目的。 +要打包成一个 exe 程序，发给朋友才有意思。 +使用 pip install pyinstaller。 +打包命令如下： +pyinstaller -F 文件名.py +""" +while True: + n = input("猜猜我在想啥？") + print("猜错喽") diff --git a/boring_program/boring2.py b/boring_program/boring2.py new file mode 100644 index 0000000..380baec --- /dev/null +++ b/boring_program/boring2.py @@ -0,0 +1,9 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +# from:https://mp.weixin.qq.com/s/R9Z0LR9htZcgl7vVvY1sdQ +# 死命弹窗 +# 运行之后，很就刺激了，如果对方不会杀进程，更刺激。 +import tkinter.messagebox + +while True: + tkinter.messagebox.showerror('Windows 错误', '你的电脑正在被攻击！') diff --git a/boring_program/boring3.py b/boring_program/boring3.py new file mode 100644 index 0000000..b7e6c3f --- /dev/null +++ b/boring_program/boring3.py @@ -0,0 +1,10 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +# from:https://mp.weixin.qq.com/s/R9Z0LR9htZcgl7vVvY1sdQ + +# 调用默认浏览器，无限打开 CSDN ，让他爱上学习。 +import webbrowser + +while True: + webbrowser.open('www.csdn.net') +# 额，使用之后，我自己的电脑死机了。 diff --git a/boring_program/boring4.py b/boring_program/boring4.py new file mode 100644 index 0000000..fa58d0e --- /dev/null +++ b/boring_program/boring4.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk +import random +import threading +import time + +# +# from:https://mp.weixin.qq.com/s/R9Z0LR9htZcgl7vVvY1sdQ +# 这个程序就动感多了，会随机出现弹窗。 +# 运行效果如下图所示，非常带劲，可以任意修改。 + +def boom(): + window = tk.Tk() + width = window.winfo_screenwidth() + height = window.winfo_screenheight() + a = random.randrange(0, width) + b = random.randrange(0, height) + window.title('你是一个傻狍子') + window.geometry("200x50" + "+" + str(a) + "+" + str(b)) + tk.Label(window, text='你是一个傻狍子', bg='green', + font=('宋体', 17), width=20, height=4).pack() + window.mainloop() + + +threads = [] +for i in range(100): + t = threading.Thread(target=boom) + threads.append(t) + time.sleep(0.1) + threads[i].start() diff --git a/boring_program/boring5.py b/boring_program/boring5.py new file mode 100644 index 0000000..9318e91 --- /dev/null +++ b/boring_program/boring5.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +# 该程序在我看来能排到第一，甚至可以和当下最火的枪茅台案例结合一下。 +# from:https://mp.weixin.qq.com/s/R9Z0LR9htZcgl7vVvY1sdQ + +import os +import time + +a = """ + + oooo oooooooooo. .oooooo..o oooo o8o oooo oooo + `888 `888' `Y8b d8P' `Y8 `888 `"' `888 `888 + 888 888 888 Y88bo. .ooooo. .ooooo. 888 oooo oooo 888 888 + 888 888 888 `"Y8888o. d88' `88b d88' `"Y8 888 .8P' `888 888 888 + 888 888 888 8888888 `"Y88b 888ooo888 888 888888. 888 888 888 + 888 888 d88' oo .d8P 888 .o 888 .o8 888 `88b. 888 888 888 +.o. 88P o888bood8P' 8""88888P' `Y8bod8P' `Y8bod8P' o888o o888o o888o o888o o888o +`Y888P + +功能列表： +1.预约商品 +2.秒杀抢购商品 +""" +print(a) + +key = input("请选择:") + +if key == "1": + time.sleep(1.5) + print('没有预约到\n') + time.sleep(3) + print('没事的，来抱一哈\n') + +else: + print("既然如此...") + time.sleep(3) + print("那你想得美~~~~~") + os.system('shutdown -r -t 10') +time.sleep(10) + +# 别运行，运行之后别怪我。 diff --git a/caipiao/daletou.py b/caipiao/daletou.py new file mode 100644 index 0000000..1a882dd --- /dev/null +++ b/caipiao/daletou.py @@ -0,0 +1,36 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from random import randint, sample + + +def display(balls): + """ + 输出列表中的双色球号码 + """ + for index, ball in enumerate(balls): + if index == len(balls) - 2: + print('|', end=' ') + print('%02d' % ball, end=' ') + print() + + +def random_select(): + """ + 随机选择一组号码 + """ + red_balls = list(range(1, 36)) + selected_balls = [] + selected_balls = sample(red_balls, 5) + selected_balls.sort() + selected_balls.extend(sorted(sample(range(1, 13), 2))) + return selected_balls + + +def main(): + n = int(input('机选几注: ')) + for _ in range(n): + display(random_select()) + + +if __name__ == '__main__': + main() diff --git a/caipiao/shuangseqiu.py b/caipiao/shuangseqiu.py new file mode 100644 index 0000000..7698097 --- /dev/null +++ b/caipiao/shuangseqiu.py @@ -0,0 +1,36 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from random import randint, sample + + +def display(balls): + """ + 输出列表中的双色球号码 + """ + for index, ball in enumerate(balls): + if index == len(balls) - 1: + print('|', end=' ') + print('%02d' % ball, end=' ') + print() + + +def random_select(): + """ + 随机选择一组号码 + """ + red_balls = list(range(1, 34)) + selected_balls = [] + selected_balls = sample(red_balls, 6) + selected_balls.sort() + selected_balls.append(randint(1, 16)) + return selected_balls + + +def main(): + n = int(input('机选几注: ')) + for _ in range(n): + display(random_select()) + + +if __name__ == '__main__': + main() diff --git "a/caipiao/\345\217\214\350\211\262\347\220\203.py" "b/caipiao/\345\217\214\350\211\262\347\220\203.py" new file mode 100644 index 0000000..18d286e --- /dev/null +++ "b/caipiao/\345\217\214\350\211\262\347\220\203.py" @@ -0,0 +1,36 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import numpy as np + + +def display(balls): + """ + 输出列表中的双色球号码 + """ + for index, ball in enumerate(balls): + if index == len(balls) - 1: + print('|', end=' ') + print('%02d' % ball, end=' ') + print() + + +def random_select(): + """ + 随机选择一组号码 + """ + red_balls = list(range(1, 34)) + selected_balls = np.random.choice(red_balls, 6,replace = False) + selected_balls = sorted(selected_balls) + selected_balls.append(np.random.randint(1, 16)) + return selected_balls + + +def main(): + n = int(input('机选几注: ')) + for _ in range(n): + display(random_select()) + + +if __name__ == '__main__': + main() diff --git "a/caipiao/\345\244\247\344\271\220\351\200\217.py" "b/caipiao/\345\244\247\344\271\220\351\200\217.py" new file mode 100644 index 0000000..f1e43dd --- /dev/null +++ "b/caipiao/\345\244\247\344\271\220\351\200\217.py" @@ -0,0 +1,35 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import numpy as np + + +def display(balls): + """ + 输出列表中的双色球号码 + """ + for index, ball in enumerate(balls): + if index == len(balls) - 2: + print('|', end=' ') + print('%02d' % ball, end=' ') + print() + + +def random_select(): + """ + 随机选择一组号码 + """ + red_balls = list(range(1, 36)) + selected_balls = np.random.choice(red_balls, 5, replace=False) + selected_balls = sorted(selected_balls) + selected_balls.extend(sorted(np.random.choice(range(1, 13), 2, replace=False))) + return selected_balls + + +def main(): + n = int(input('机选几注: ')) + for _ in range(n): + display(random_select()) + + +if __name__ == '__main__': + main() diff --git a/chrome/chrome.py b/chrome/chrome.py index f6c5b3f..6fa0769 100644 --- a/chrome/chrome.py +++ b/chrome/chrome.py @@ -22,7 +22,7 @@ def main(): url = "https://weread.qq.com/" url = "https://weread.qq.com/web/reader/b253292071697fe1b25cd24" - c = webdriver.Chrome() + c = webdriver.Chrome(executable_path='driver\chromedriver.exe') c.get(url) time.sleep(10) # 登陆一下 diff --git a/chrome/chrome_helper.py b/chrome/chrome_helper.py new file mode 100644 index 0000000..4dc6a69 --- /dev/null +++ b/chrome/chrome_helper.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import os +from log_handle import logger +import logging +import zipfile +import requests +import file_util + +# https://medium.com/drunk-wis/python-selenium-chrome-browser-%E8%88%87-driver-%E6%83%B1%E4%BA%BA%E7%9A%84%E7%89%88%E6%9C%AC%E7%AE%A1%E7%90%86-cbaf1d1861ce +CHROME_DRIVER_BASE_URL = "https://chromedriver.storage.googleapis.com" +CHROME_DRIVER_FOLDER = r".\chrome" +CHROME_DRIVER_MAPPING_FILE = r"{}\mapping.json".format(CHROME_DRIVER_FOLDER) +CHROME_DRIVER_EXE = r"{}\chromedriver.exe".format(CHROME_DRIVER_FOLDER) +CHROME_DRIVER_ZIP = r"{}\chromedriver_win32.zip".format(CHROME_DRIVER_FOLDER) + + +def init_dir(): + if not os.path.exists(CHROME_DRIVER_FOLDER): + os.makedirs(CHROME_DRIVER_FOLDER) + + +def get_chrome_driver_major_version(): + chrome_browser_path = r"C:\Program Files\Google\Chrome\Application\chrome.exe" + chrome_ver = file_util.get_file_version(chrome_browser_path) + logger.info(f'chrome version is {chrome_ver}') + chrome_major_ver = chrome_ver.split(".")[0] + logger.info(f'chrome driver major version is {chrome_major_ver}') + return chrome_major_ver + + +def get_latest_driver_version(browser_ver): + latest_api = "{}/LATEST_RELEASE_{}".format( + CHROME_DRIVER_BASE_URL, browser_ver) + logger.info(f'latest api is {latest_api}') + resp = requests.get(latest_api) + lastest_driver_version = resp.text.strip() + logger.info(f'latest driver version is {lastest_driver_version}') + return lastest_driver_version + + +def download_driver(driver_ver, dest_folder): + download_api = "{}/{}/chromedriver_win32.zip".format( + CHROME_DRIVER_BASE_URL, driver_ver) + logger.info(f'download api is {download_api}') + dest_path = os.path.join(dest_folder, os.path.basename(download_api)) + resp = requests.get(download_api, stream=True, timeout=300) + logger.info(f'download dest path is {dest_path}') + if resp.status_code == 200: + with open(dest_path, "wb") as f: + f.write(resp.content) + logger.info("Download driver completed") + else: + raise Exception("Download chrome driver failed") + + +def unzip_driver_to_target_path(src_file, dest_path): + with zipfile.ZipFile(src_file, 'r') as zip_ref: + zip_ref.extractall(dest_path) + logger.info("Unzip [{}] -> [{}]".format(src_file, dest_path)) + + +def read_driver_mapping_file(): + driver_mapping_dict = {} + if os.path.exists(CHROME_DRIVER_MAPPING_FILE): + driver_mapping_dict = file_util.read_json(CHROME_DRIVER_MAPPING_FILE) + logger.info(f'driver mapping dict is {driver_mapping_dict}') + return driver_mapping_dict + + +def check_browser_driver_available(): + chrome_major_ver = get_chrome_driver_major_version() + mapping_dict = read_driver_mapping_file() + driver_ver = get_latest_driver_version(chrome_major_ver) + + if chrome_major_ver not in mapping_dict: + download_driver(driver_ver, CHROME_DRIVER_FOLDER) + unzip_driver_to_target_path(CHROME_DRIVER_ZIP, CHROME_DRIVER_FOLDER) + + mapping_dict = { + chrome_major_ver: { + "driver_path": CHROME_DRIVER_EXE, + "driver_version": driver_ver + } + } + mapping_dict.update(mapping_dict) + file_util.write_json(CHROME_DRIVER_MAPPING_FILE, mapping_dict) + + +if __name__ == "__main__": + init_dir() + check_browser_driver_available() diff --git a/chrome/chrome_helper_v1.py b/chrome/chrome_helper_v1.py new file mode 100644 index 0000000..cb08b51 --- /dev/null +++ b/chrome/chrome_helper_v1.py @@ -0,0 +1,98 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import os +import zipfile +import requests + +from log_handle import logger +import file_util + +CHROME_DRIVER_BASE_URL = "https://storage.googleapis.com/chrome-for-testing-public" +CHROME_DRIVER_FOLDER = r".\chrome" +CHROME_DRIVER_MAPPING_URL = "https://googlechromelabs.github.io/chrome-for-testing/known-good-versions-with-downloads.json" +CHROME_DRIVER_MAPPING_FILE = f"{CHROME_DRIVER_FOLDER}\mapping.json" +CHROME_DRIVER_EXE = f"{CHROME_DRIVER_FOLDER}\chromedriver.exe" +CHROME_DRIVER_ZIP = f"{CHROME_DRIVER_FOLDER}\chromedriver-win64.zip" + +headers = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36'} + + +def get_chrome_driver_major_version(): + chrome_browser_path = r"C:\Program Files\Google\Chrome\Application\chrome.exe" + chrome_ver = file_util.get_file_version(chrome_browser_path) + logger.info(f'chrome version is {chrome_ver}') + chrome_major_ver = chrome_ver.split(".")[0] + logger.info(f'chrome driver major version is {chrome_major_ver}') + return chrome_ver + + +def download_driver(driver_ver, dest_folder): + download_api = f"{CHROME_DRIVER_BASE_URL}/{driver_ver}/win64/chromedriver-win64.zip" + logger.info(f'download api is {download_api}') + dest_path = os.path.join(dest_folder, os.path.basename(download_api)) + # urllib.request.urlretrieve(download_api,dest_path) + resp = requests.get(download_api) + logger.info(f'download dest path is {dest_path}') + if resp.status_code != 200: + raise Exception("Download chrome driver failed") + with open(dest_path, "wb") as f: + # for chunk in resp.iter_content(chunk_size=1024): + f.write(resp.content) + logger.info("Download driver completed") + + +def get_download_chrome_driver_ver(chrome_ver): + """get download chrome driver version""" + mapping_dict = read_driver_mapping_file() + start, end = chrome_ver.rsplit('.', 1) + for item in mapping_dict['versions']: + if start in item['version']: + return item['version'] + + +def unzip_driver_to_target_path(src_file, dest_path): + """unzip chrome driver to target path""" + with zipfile.ZipFile(src_file, 'r') as zip_ref: + zip_ref.extractall(dest_path) + logger.info(f"Unzip [{src_file}] -> [{dest_path}]") + + +def download_driver_mapping_file(): + """download driver mapping file""" + resp = requests.get(CHROME_DRIVER_MAPPING_URL, timeout=300) + logger.info(f'download dest path is {CHROME_DRIVER_MAPPING_FILE}') + if resp.status_code != 200: + raise Exception("Download chrome driver mapping file failed") + with open(CHROME_DRIVER_MAPPING_FILE, "w") as f: + f.write(resp.text) + logger.info("Download driver mapping file completed") + + +def read_driver_mapping_file(): + """read driver mapping file""" + driver_mapping_dict = {} + if os.path.exists(CHROME_DRIVER_MAPPING_FILE): + driver_mapping_dict = file_util.read_json(CHROME_DRIVER_MAPPING_FILE) + logger.info(f'driver mapping dict is {driver_mapping_dict}') + return driver_mapping_dict + + +def check_browser_driver_available(): + """check if the driver is available""" + chrome_ver = get_chrome_driver_major_version() + download_driver_mapping_file() + download_chrome_version = get_download_chrome_driver_ver(chrome_ver) + download_driver(download_chrome_version, CHROME_DRIVER_FOLDER) + unzip_driver_to_target_path(CHROME_DRIVER_ZIP, CHROME_DRIVER_FOLDER) + + +def main(): + """main function""" + if not os.path.exists(CHROME_DRIVER_FOLDER): + os.makedirs(CHROME_DRIVER_FOLDER) + check_browser_driver_available() + + +if __name__ == "__main__": + main() diff --git a/chrome/driver/chromedriver.exe b/chrome/driver/chromedriver.exe new file mode 100644 index 0000000..d51602d Binary files /dev/null and b/chrome/driver/chromedriver.exe differ diff --git a/chrome/file_util.py b/chrome/file_util.py new file mode 100644 index 0000000..204269e --- /dev/null +++ b/chrome/file_util.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import os +import json +from log_handle import logger + +from win32com import client as wincom_client + +""" +Environment Requirements: +Windows +1. Python 3+ +2. Google Chrome +Python Packages +pip install pypiwin32 +pip install requests +Folder Structure +--- chrome_helper.py + file_util.py + + +get_file_version() --- 取得版本號 +write_json() --- 寫 json file +read_json() --- 讀 json file +""" + + +def get_file_version(file_path): + logger.info('Get file version of [%s]', file_path) + if not os.path.isfile(file_path): + logger.error('{!r} is not found.'.format(file_path)) + raise FileNotFoundError('{!r} is not found.'.format(file_path)) + + wincom_obj = wincom_client.Dispatch('Scripting.FileSystemObject') + version = wincom_obj.GetFileVersion(file_path) + logger.info('The file version of [%s] is %s', file_path, version) + return version.strip() + + +def write_json(file_path, data): + with open(file_path, 'w', encoding='utf-8')as f: + json.dump(data, f, indent=2) + + +def read_json(file_path): + with open(file_path, encoding='utf-8')as f: + data = json.load(f) + return data diff --git a/chrome/log_handle.py b/chrome/log_handle.py new file mode 100644 index 0000000..0f3653e --- /dev/null +++ b/chrome/log_handle.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import logging +import logging.handlers +import os + +LOG_DIR = "log" +if not os.path.exists(LOG_DIR): + os.makedirs(LOG_DIR) +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) +file_handler = logging.handlers.TimedRotatingFileHandler('log/chrome.log', when='D', interval=1, backupCount=10) +file_handler.setLevel(logging.INFO) +file_handler.setFormatter( + logging.Formatter("%(asctime)s - %(levelname)s - %(filename)s[%(lineno)d] messages: %(message)s")) + +console_handler = logging.StreamHandler() +console_handler.setLevel(logging.INFO) +console_handler.setFormatter(logging.Formatter("%(asctime)s - %(levelname)s - %(filename)s[:%(lineno)d] - %(message)s")) + +logger.addHandler(file_handler) +logger.addHandler(console_handler) diff --git a/chrome/webdrive.py b/chrome/webdrive.py new file mode 100644 index 0000000..30661f4 --- /dev/null +++ b/chrome/webdrive.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from selenium import webdriver # 从selenium导入webdriver +import time + +""" +常用查找元素方法： + +find_element_by_id # ID +find_elements_by_class_name # class +find_elements_by_tag_name # 标签名 +find_elements_by_name # name +find_elements_by_link_text # a标签中的text查找（精确匹配） +find_elements_by_partial_link_text #a标签中的text查找（部分匹配即可） +find_elements_by_css_selector # css选择器查找 +find_elements_by_xpath # find_elements_by_xpath("//input")，请翻阅文档 +""" +driver = webdriver.Chrome('driver/chromedriver.exe') + +driver.get('https://www.baidu.com') + +input = driver.find_element_by_id('kw') +searchButton = driver.find_element_by_id('su') # 获取搜索按钮 +input.send_keys('python') +searchButton.click() +time.sleep(10) +driver.close() diff --git a/conditional_branching/try_else.py b/conditional_branching/try_else.py new file mode 100644 index 0000000..e87223d --- /dev/null +++ b/conditional_branching/try_else.py @@ -0,0 +1,32 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +def do_the_first_thing(): + pass + + +def do_the_second_thing(): + pass + + +def do_stuff(): + first_thing_successed = False + try: + do_the_first_thing() + first_thing_successed = True + except Exception as e: + print("Error while calling do_some_thing") + return + + # 仅当 first_thing 成功完成时，做第二件事 + if first_thing_successed: + return do_the_second_thing() + + +def do_stuff(): + try: + do_the_first_thing() + except Exception as e: + print("Error while calling do_some_thing") + return + else: + return do_the_second_thing() diff --git a/coroutine/subprocess_target.py b/coroutine/subprocess_target.py index b3be5e7..8aa49b8 100644 --- a/coroutine/subprocess_target.py +++ b/coroutine/subprocess_target.py @@ -1,5 +1,6 @@ #!/usr/bin/env python # -*- coding:utf-8 -*- +import pickle def coroutine(func): @@ -7,8 +8,10 @@ def start(*args, **kwargs): rc = func(*args, **kwargs) rc.next() return rc + return start + # bridge two coroutine over a file/pipe @coroutine @@ -30,5 +33,9 @@ def fecvfrom(f, target): except EOFError: target.close() + +def main(): + pass + if __name__ == '__main__': main() diff --git a/coroutine/thread_target.py b/coroutine/thread_target.py index e79fb58..609fafb 100644 --- a/coroutine/thread_target.py +++ b/coroutine/thread_target.py @@ -1,6 +1,6 @@ #!/usr/bin/env python # -*- coding:utf-8 -*- - +from queue import Queue def coroutine(func): @@ -8,19 +8,21 @@ def start(*args, **kwargs): rc = func(*args, **kwargs) rc.next() return rc + return start @coroutine def threaded(target): - messages = Queue() # message queue + messages = Queue() # message queue + def run_target(): while True: - item = messages.get() # A thread loop forever.pulling items out of - # the message queue and sending to the - # target + item = messages.get() # A threads loop forever.pulling items out of + # the message queue and sending to the + # target - if item is GeneratorExit: # handle close so that thread shuts down correctly + if item is GeneratorExit: # handle close so that threads shuts down correctly target.close() return else: @@ -29,12 +31,16 @@ def run_target(): try: while True: - item = yield # receive items and pass them into the - # thread (via the queue) + item = yield # receive items and pass them into the + # threads (via the queue) messages.put(item) except GeneratorExit: messages.put(GeneratorExit) +def main(): + pass + + if __name__ == '__main__': main() diff --git a/crawler/_env.py b/crawler/_env.py index dbacbaa..d90ec14 100644 --- a/crawler/_env.py +++ b/crawler/_env.py @@ -4,6 +4,9 @@ import sys -if sys.getdefaultencoding() != 'utf-8': - reload(sys) - sys.setdefaultencoding('utf-8') +if sys.version_info[0] == 2: + if sys.getdefaultencoding() != 'utf-8': + reload(sys) + sys.setdefaultencoding('utf-8') +else: + pass diff --git a/crawler/proxy/proxy.py b/crawler/proxy/proxy.py index 238b733..42c0794 100644 --- a/crawler/proxy/proxy.py +++ b/crawler/proxy/proxy.py @@ -1,1063 +1,1061 @@ -# -*- coding: gb2312 -*- -# vi:ts=4:et - -""" -Ŀǰܴվץȡб - -http://www.cybersyndrome.net/ -http://www.pass-e.com/ -http://www.cnproxy.com/ -http://www.proxylists.net/ -http://www.my-proxy.com/ -http://www.samair.ru/proxy/ -http://proxy4free.com/ -http://proxylist.sakura.ne.jp/ -http://www.ipfree.cn/ -http://www.publicproxyservers.com/ -http://www.digitalcybersoft.com/ -http://www.checkedproxylists.com/ - -:ԼվԶóȥץȡ? -: - -עԴºĶ.Ӻһִ1ʼĿǰѾ13 - -def build_list_urls_1(page=5): -def parse_page_2(html=''): - -def build_list_urls_2(page=5): -def parse_page_2(html=''): - -....... - -def build_list_urls_13(page=5): -def parse_page_13(html=''): - - -Ҫľ build_list_urls_14 parse_page_14 -Ҫ www.somedomain.com ץȡ - /somepath/showlist.asp?page=1 - ... - /somepath/showlist.asp?page=8 蹲8ҳ - -ô build_list_urls_14 Ӧ -ҪpageĬֵΪҪץȡҳ8ȷץ8ҳ -def build_list_urls_14(page=8): - ..... - return [ #صһһά飬ÿԪضҪץȡҳľԵַ - 'http://www.somedomain.com/somepath/showlist.asp?page=1', - 'http://www.somedomain.com/somepath/showlist.asp?page=2', - 'http://www.somedomain.com/somepath/showlist.asp?page=3', - .... - 'http://www.somedomain.com/somepath/showlist.asp?page=8' - ] - -дһ parse_page_14(html='')ǸصЩҳhtml -htmlȡַ -ע⣺ ѭ parse_page_14 еҳ棬htmlЩҳhtmlı - -ip: Ϊ xxx.xxx.xxx.xxx ipʽΪ www.xxx.com ʽ -port: Ϊ 2-5λ -type: Ϊ 2,1,0,-1 еһЩִ - 2:߶ 1: ͨ 0:͸ -1: ޷ȷĴ - #area: ڹһߵ תΪ utf8ʽ - -def parse_page_14(html=''): - .... - return [ - [ip,port,type,area] - [ip,port,type,area] - ..... - .... - [ip,port,type,area] - ] - -Ҫһ:޸ȫֱ web_site_countֵӵ1 web_site_count=14 - - - -ʣѾ˵ɹһԶվ㣬Ҫһô? -𣺼ȻѾ֪ô build_list_urls_14 parse_page_14 - -ôͰͬİ취 -def build_list_urls_15(page=5): -def parse_page_15(html=''): - - ȫֱ web_site_count=15 - -""" - - -import urllib,time,random,re,threading,string - -web_site_count=13 #ҪץȡվĿ -day_keep=2 #ɾݿбʱday_keep Ч -indebug=1 - -thread_num=100 # thread_num ̼߳ -check_in_one_call=thread_num*10 # γʱ Ĵ - - -skip_check_in_hour=1 # ʱ skip_check_in_hour,ͬһַٴ֤ -skip_get_in_hour=8 # ÿβɼ´ʱ (Сʱ) - -proxy_array=[] # 鱣潫ҪӵݿĴб -update_array=[] # 鱣潫ҪµĴ - -db=None #ݿȫֶ -conn=None -dbfile='proxier.db' #ݿļ - -target_url="http://www.baidu.com/" # ֤ʱַͨ -target_string="030173" # صhtmlаַ -target_timeout=30 # ӦʱС target_timeout - #ôǾΪЧ - - - -#ݵļʽ뵼ݣΪ output_type='' - -output_type='xml' #¸ʽѡ, Ĭxml - # xml - # htm - # tab Ʊָ, excel - # csv ŷָ, excel - # txt xxx.xxx.xxx.xxx:xx ʽ - -# ļ 뱣֤麬Ԫ -output_filename=[ - 'uncheck', # δĴ,浽ļ - 'checkfail', # Ѿ飬DZΪЧĴ,浽ļ - 'ok_high_anon', # (Ч)Ĵ,speedķǰ - 'ok_anonymous', # ͨ(Ч)Ĵ,speedķǰ - 'ok_transparent', # ͸(Ч)Ĵ,speedķǰ - 'ok_other' # δ֪(Ч)Ĵ,speed - ] - - -#ݵĸʽ ֵ֧ -# _ip_ , _port_ , _type_ , _status_ , _active_ , -#_time_added_, _time_checked_ ,_time_used_ , _speed_, _area_ - -output_head_string='' # ļͷַ -output_format='' # ļݵĸʽ -output_foot_string='' # ļĵײַ - - - -if output_type=='xml': - output_head_string="\n" - output_format=""" - _ip_ - _port_ - _speed_ - _time_checked_ - _area_ - - """ - output_foot_string="" -elif output_type=='htm': - output_head_string=""" - - """ - output_format=""" - - - """ - output_foot_string="
ٶ
_ip_:_port__time_checked__speed__area_
" -else: - output_head_string='' - output_foot_string='' - -if output_type=="csv": - output_format="_ip_, _port_, _type_, _speed_, _time_checked_, _area_\n" - -if output_type=="tab": - output_format="_ip_\t_port_\t_speed_\t_time_checked_\t_area_\n" - -if output_type=="txt": - output_format="_ip_:_port_\n" - - -# ļĺ -def output_file(): - global output_filename,output_head_string,output_foot_string,output_type - if output_type=='': - return - fnum=len(output_filename) - content=[] - for i in range(fnum): - content.append([output_head_string]) - - conn.execute("select * from `proxier` order by `active`,`type`,`speed` asc") - rs=conn.fetchall() - - for item in rs: - type,active=item[2],item[4] - if active is None: - content[0].append(formatline(item)) #δ - elif active==0: - content[1].append(formatline(item)) #ǷĴ - elif active==1 and type==2: - content[2].append(formatline(item)) # - elif active==1 and type==1: - content[3].append(formatline(item)) #ͨ - elif active==1 and type==0: - content[4].append(formatline(item)) #͸ - elif active==1 and type==-1: - content[5].append(formatline(item)) #δ֪͵Ĵ - else: - pass - - for i in range(fnum): - content[i].append(output_foot_string) - f=open(output_filename[i]+"."+output_type,'w') - f.write(string.join(content[i],'')) - f.close() - -#ʽÿ¼ -def formatline(item): - global output_format - arr=['_ip_','_port_','_type_','_status_','_active_', - '_time_added_','_time_checked_','_time_used_', - '_speed_','_area_'] - s=output_format - for i in range(len(arr)): - s=string.replace(s,arr[i],str(formatitem(item[i],i))) - return s - - -#ݿеÿֶͬΣҪһ£Ҫ룬ֶҪת -def formatitem(value,colnum): - global output_type - if (colnum==9): - value=value.encode('cp936') - elif value is None: - value='' - - if colnum==5 or colnum==6 or colnum==7: #time_xxxed - value=string.atof(value) - if value<1: - value='' - else: - value=formattime(value) - - if value=='' and output_type=='htm':value=' ' - return value - - - -def check_one_proxy(ip,port): - global update_array - global check_in_one_call - global target_url,target_string,target_timeout - - url=target_url - checkstr=target_string - timeout=target_timeout - ip=string.strip(ip) - proxy=ip+':'+str(port) - proxies = {'http': 'http://'+proxy+'/'} - opener = urllib.FancyURLopener(proxies) - opener.addheaders = [ - ('User-agent','Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)') - ] - t1=time.time() - - if (url.find("?")==-1): - url=url+'?rnd='+str(random.random()) - else: - url=url+'&rnd='+str(random.random()) - - try: - f = opener.open(url) - s= f.read() - pos=s.find(checkstr) - except: - pos=-1 - pass - t2=time.time() - timeused=t2-t1 - if (timeused0): - active=1 - else: - active=0 - update_array.append([ip,port,active,timeused]) - print len(update_array),' of ',check_in_one_call," ",ip,':',port,'--',int(timeused) - - -def get_html(url=''): - opener = urllib.FancyURLopener({}) #ʹô - #www.my-proxy.com ҪCookieץȡ - opener.addheaders = [ - ('User-agent','Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)'), - ('Cookie','permission=1') - ] - t=time.time() - if (url.find("?")==-1): - url=url+'?rnd='+str(random.random()) - else: - url=url+'&rnd='+str(random.random()) - try: - f = opener.open(url) - return f.read() - except: - return '' - - - - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - -def build_list_urls_1(page=5): - page=page+1 - ret=[] - for i in range(1,page): - ret.append('http://proxy4free.com/page%(num)01d.html'%{'num':i}) - return ret - -def parse_page_1(html=''): - matches=re.findall(r''' - ([\d\.]+)<\/td>[\s\n\r]* #ip - ([\d]+)<\/td>[\s\n\r]* #port - ([^\<]*)<\/td>[\s\n\r]* #type - ([^\<]*)<\/td> #area - ''',html,re.VERBOSE) - ret=[] - for match in matches: - ip=match[0] - port=match[1] - type=match[2] - area=match[3] - if (type=='anonymous'): - type=1 - elif (type=='high anonymity'): - type=2 - elif (type=='transparent'): - type=0 - else: - type=-1 - ret.append([ip,port,type,area]) - if indebug:print '1',ip,port,type,area - return ret - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - - -def build_list_urls_2(page=1): - return ['http://www.digitalcybersoft.com/ProxyList/fresh-proxy-list.shtml'] - -def parse_page_2(html=''): - matches=re.findall(r''' - ((?:[\d]{1,3}\.){3}[\d]{1,3})\:([\d]+) #ip:port - \s+(Anonymous|Elite Proxy)[+\s]+ #type - (.+)\r?\n #area - ''',html,re.VERBOSE) - ret=[] - for match in matches: - ip=match[0] - port=match[1] - type=match[2] - area=match[3] - if (type=='Anonymous'): - type=1 - else: - type=2 - ret.append([ip,port,type,area]) - if indebug:print '2',ip,port,type,area - return ret - - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - - -def build_list_urls_3(page=15): - page=page+1 - ret=[] - for i in range(1,page): - ret.append('http://www.samair.ru/proxy/proxy-%(num)02d.htm'%{'num':i}) - return ret - -def parse_page_3(html=''): - matches=re.findall(r''' - (\d{1,3})<\/span>\. #ip(part1) - - (\d{1,3})<\/span> #ip(part2) - (\.\d{1,3}\.\d{1,3}) #ip(part3,part4) - - \:\r?\n(\d{2,5})<\/td> #port - ([^<]+) #type - [^<]+<\/td> - ([^<]+)<\/td> #area - <\/tr>''',html,re.VERBOSE) - ret=[] - for match in matches: - ip=match[0]+"."+match[1]+match[2] - port=match[3] - type=match[4] - area=match[5] - if (type=='anonymous proxy server'): - type=1 - elif (type=='high-anonymous proxy server'): - type=2 - elif (type=='transparent proxy'): - type=0 - else: - type=-1 - ret.append([ip,port,type,area]) - if indebug:print '3',ip,port,type,area - return ret - - - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - -def build_list_urls_4(page=3): - page=page+1 - ret=[] - for i in range(1,page): - ret.append('http://www.pass-e.com/proxy/index.php?page=%(n)01d'%{'n':i}) - return ret - -def parse_page_4(html=''): - matches=re.findall(r""" - list - \('(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' #ip - \,'(\d{2,5})' #port - \,'(\d)' #type - \,'([^']+)'\) #area - \;\r?\n""",html,re.VERBOSE) - ret=[] - for match in matches: - ip=match[0] - port=match[1] - type=match[2] - area=match[3] - if (type=='1'): #typeжϿԲ鿴ץҳjavascript - type=1 - elif (type=='3'): - type=2 - elif (type=='2'): - type=0 - else: - type=-1 - if indebug:print '4',ip,port,type,area - area=unicode(area, 'cp936') - area=area.encode('utf8') - ret.append([ip,port,type,area]) - return ret - - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - - -def build_list_urls_5(page=12): - page=page+1 - ret=[] - for i in range(1,page): - ret.append('http://www.ipfree.cn/index2.asp?page=%(num)01d'%{'num':i}) - return ret - -def parse_page_5(html=''): - matches=re.findall(r"([^<]*)",html) - ret=[] - for index, match in enumerate(matches): - if (index%3==0): - ip=matches[index+1] - port=matches[index+2] - type=-1 #վδṩ - if indebug:print '5',ip,port,type,match - area=unicode(match, 'cp936') - area=area.encode('utf8') - ret.append([ip,port,type,area]) - else: - continue - return ret - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - - -def build_list_urls_6(page=3): - page=page+1 - ret=[] - for i in range(1,page): - ret.append('http://www.cnproxy.com/proxy%(num)01d.html'%{'num':i}) - return ret - -def parse_page_6(html=''): - matches=re.findall(r''' - ([^&]+) #ip - ‌‍ - \:([^<]+) #port - - HTTP - [^<]+ - ([^<]+) #area - ''',html,re.VERBOSE) - ret=[] - for match in matches: - ip=match[0] - port=match[1] - type=-1 #վδṩ - area=match[2] - if indebug:print '6',ip,port,type,area - area=unicode(area, 'cp936') - area=area.encode('utf8') - ret.append([ip,port,type,area]) - - return ret - - - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - - - -def build_list_urls_7(page=1): - return ['http://www.proxylists.net/http_highanon.txt'] - -def parse_page_7(html=''): - matches=re.findall(r'(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})\:(\d{2,5})',html) - ret=[] - for match in matches: - ip=match[0] - port=match[1] - type=2 - area='--' - ret.append([ip,port,type,area]) - if indebug:print '7',ip,port,type,area - return ret - - - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - - - - -def build_list_urls_8(page=1): - return ['http://www.proxylists.net/http.txt'] - -def parse_page_8(html=''): - matches=re.findall(r'(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})\:(\d{2,5})',html) - ret=[] - for match in matches: - ip=match[0] - port=match[1] - type=-1 - area='--' - ret.append([ip,port,type,area]) - if indebug:print '8',ip,port,type,area - return ret - - - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - - -def build_list_urls_9(page=6): - page=page+1 - ret=[] - for i in range(0,page): - ret.append('http://proxylist.sakura.ne.jp/index.htm?pages=%(n)01d'%{'n':i}) - return ret - -def parse_page_9(html=''): - matches=re.findall(r''' - (\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}) #ip - \:(\d{2,5}) #port - <\/TD>[\s\r\n]* - ([^<]+) #area - [\s\r\n]* - ([^<]+) #type - ''',html,re.VERBOSE) - ret=[] - for match in matches: - ip=match[0] - port=match[1] - type=match[3] - area=match[2] - if (type=='Anonymous'): - type=1 - else: - type=-1 - ret.append([ip,port,type,area]) - if indebug:print '9',ip,port,type,area - return ret - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - -def build_list_urls_10(page=5): - page=page+1 - ret=[] - for i in range(1,page): - ret.append('http://www.publicproxyservers.com/page%(n)01d.html'%{'n':i}) - return ret - -def parse_page_10(html=''): - matches=re.findall(r''' - (\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}) #ip - <\/td>[\s\r\n]* - ]+>(\d{2,5})<\/td> #port - [\s\r\n]* - ([^<]+)<\/td> #type - [\s\r\n]* - ([^<]+)<\/td> #area - ''',html,re.VERBOSE) - ret=[] - for match in matches: - ip=match[0] - port=match[1] - type=match[2] - area=match[3] - if (type=='high anonymity'): - type=2 - elif (type=='anonymous'): - type=1 - elif (type=='transparent'): - type=0 - else: - type=-1 - ret.append([ip,port,type,area]) - if indebug:print '10',ip,port,type,area - return ret - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - - - -def build_list_urls_11(page=10): - page=page+1 - ret=[] - for i in range(1,page): - ret.append('http://www.my-proxy.com/list/proxy.php?list=%(n)01d'%{'n':i}) - - ret.append('http://www.my-proxy.com/list/proxy.php?list=s1') - ret.append('http://www.my-proxy.com/list/proxy.php?list=s2') - ret.append('http://www.my-proxy.com/list/proxy.php?list=s3') - return ret - -def parse_page_11(html=''): - matches=re.findall(r'(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})\:(\d{2,5})',html) - ret=[] - - if (html.find('(Level 1)')>0): - type=2 - elif (html.find('(Level 2)')>0): - type=1 - elif (html.find('(Level 3)')>0): - type=0 - else: - type=-1 - - for match in matches: - ip=match[0] - port=match[1] - area='--' - ret.append([ip,port,type,area]) - if indebug:print '11',ip,port,type,area - return ret - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - - - -def build_list_urls_12(page=4): - ret=[] - ret.append('http://www.cybersyndrome.net/plr4.html') - ret.append('http://www.cybersyndrome.net/pla4.html') - ret.append('http://www.cybersyndrome.net/pld4.html') - ret.append('http://www.cybersyndrome.net/pls4.html') - return ret - -def parse_page_12(html=''): - matches=re.findall(r''' - onMouseOver\= - "s\(\'(\w\w)\'\)" #area - \sonMouseOut\="d\(\)"\s?c?l?a?s?s?\=?"? - (\w?) #type - "?> - (\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}) #ip - \:(\d{2,5}) #port - ''',html,re.VERBOSE) - ret=[] - for match in matches: - ip=match[2] - port=match[3] - area=match[0] - type=match[1] - if (type=='A'): - type=2 - elif (type=='B'): - type=1 - else: - type=0 - ret.append([ip,port,type,area]) - if indebug:print '12',ip,port,type,area - return ret - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - - -def build_list_urls_13(page=3): - url='http://www.checkedproxylists.com/' - html=get_html(url) - matchs=re.findall(r""" - href\='([^']+)'>(?:high_anonymous|anonymous|transparent) - \sproxy\slist<\/a>""",html,re.VERBOSE) - return map(lambda x: url+x, matchs) - -def parse_page_13(html=''): - html_matches=re.findall(r"eval\(unescape\('([^']+)'\)",html) - if (len(html_matches)>0): - conent=urllib.unquote(html_matches[0]) - matches=re.findall(r"""(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})<\/td> - (\d{2,5})<\/td><\/tr>""",conent,re.VERBOSE) - ret=[] - if (html.find('Checked Proxy Lists - proxylist_high_anonymous_')>0): - type=2 - elif (html.find('<title>Checked Proxy Lists - proxylist_anonymous_')>0): - type=1 - elif (html.find('<title>Checked Proxy Lists - proxylist_transparent_')>0): - type=0 - else: - type=-1 - - for match in matches: - ip=match[0] - port=match[1] - area='--' - ret.append([ip,port,type,area]) - if indebug:print '13',ip,port,type,area - return ret - -################################################################################ -# -## by Go_Rush(˴) from http://ashun.cnblogs.com/ -# -################################################################################ - - - - -#߳ - -class TEST(threading.Thread): - def __init__(self,action,index=None,checklist=None): - threading.Thread.__init__(self) - self.index =index - self.action=action - self.checklist=checklist - - def run(self): - if (self.action=='getproxy'): - get_proxy_one_website(self.index) - else: - check_proxy(self.index,self.checklist) - - -def check_proxy(index,checklist=[]): - for item in checklist: - check_one_proxy(item[0],item[1]) - - -def patch_check_proxy(threadCount,action=''): - global check_in_one_call,skip_check_in_hour,conn - threads=[] - if (action=='checknew'): #¼룬Ҵδ - orderby=' `time_added` desc ' - strwhere=' `active` is null ' - elif (action=='checkok'): #ٴμ ǰѾ֤ɹ - orderby=' `time_checked` asc ' - strwhere=' `active`=1 ' - elif (action=='checkfail'): #ٴμǰ֤ʧܵĴ - orderby=' `time_checked` asc ' - strwhere=' `active`=0 ' - else: #е - orderby=' `time_checked` asc ' - strwhere=' 1=1 ' - sql=""" - select `ip`,`port` FROM `proxier` where - `time_checked` < (unix_timestamp()-%(skip_time)01s) - and %(strwhere)01s - order by %(order)01s - limit %(num)01d - """%{ 'num':check_in_one_call, - 'strwhere':strwhere, - 'order':orderby, - 'skip_time':skip_check_in_hour*3600} - conn.execute(sql) - rows = conn.fetchall() - - check_in_one_call=len(rows) - - #ÿ߳̽ҪĴ - if len(rows)>=threadCount: - num_in_one_thread=len(rows)/threadCount - else: - num_in_one_thread=1 - - threadCount=threadCount+1 - print "ڿʼ֤´....." - for index in range(1,threadCount): - #ÿ߳Ҫchecklist,Щʣһ߳ - checklist=rows[(index-1)*num_in_one_thread:index*num_in_one_thread] - if (index+1==threadCount): - checklist=rows[(index-1)*num_in_one_thread:] - - t=TEST(action,index,checklist) - t.setDaemon(True) - t.start() - threads.append((t)) - for thread in threads: - thread.join(60) - update_proxies() #еļµݿ - - -def get_proxy_one_website(index): - global proxy_array - func='build_list_urls_'+str(index) - parse_func=eval('parse_page_'+str(index)) - urls=eval(func+'()') - for url in urls: - html=get_html(url) - print url - proxylist=parse_func(html) - for proxy in proxylist: - ip=string.strip(proxy[0]) - port=string.strip(proxy[1]) - if (re.compile("^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}$").search(ip)): - type=str(proxy[2]) - area=string.strip(proxy[3]) - proxy_array.append([ip,port,type,area]) - - -def get_all_proxies(): - global web_site_count,conn,skip_get_in_hour - - #Ӵʲôʱ򣬱ʱڶץȡ - rs=conn.execute("select max(`time_added`) from `proxier` limit 1") - last_add=rs.fetchone()[0] - if (last_add and my_unix_timestamp()-last_add<skip_get_in_hour*3600): - print """ - ץȡб! - Ϊһץȡʱ: %(t)1s - ʱڵʱСץȡСʱ: %(n)1d Сʱ - һҪץȡ޸ȫֱ: skip_get_in_hour ֵ - """%{'t':formattime(last_add),'n':skip_get_in_hour} - return - - print "ڿʼ"+str(web_site_count)+"վץȡб...." - threads=[] - count=web_site_count+1 - for index in range(1,count): - t=TEST('getproxy',index) - t.setDaemon(True) - t.start() - threads.append((t)) - for thread in threads: - thread.join(60) - add_proxies_to_db() - -def add_proxies_to_db(): - global proxy_array - count=len(proxy_array) - for i in range(count): - item=proxy_array[i] - sql="""insert into `proxier` (`ip`,`port`,`type`,`time_added`,`area`) values - ('"""+item[0]+"',"+item[1]+","+item[2]+",unix_timestamp(),'"+clean_string(item[3])+"')" - try: - conn.execute(sql) - print "%(num)2.1f\%\t"%{'num':100*(i+1)/count},item[0],":",item[1] - except: - pass - - -def update_proxies(): - global update_array - for item in update_array: - sql=''' - update `proxier` set `time_checked`=unix_timestamp(), - `active`=%(active)01d, - `speed`=%(speed)02.3f - where `ip`='%(ip)01s' and `port`=%(port)01d - '''%{'active':item[2],'speed':item[3],'ip':item[0],'port':item[1]} - try: - conn.execute(sql) - except: - pass - -#sqlite ֧ unix_timestamp,ҪԼʵ -def my_unix_timestamp(): - return int(time.time()) - -def clean_string(s): - tmp=re.sub(r"['\,\s\\\/]", ' ', s) - return re.sub(r"\s+", ' ', tmp) - -def formattime(t): - return time.strftime('%c',time.gmtime(t+8*3600)) - - -def open_database(): - global db,conn,day_keep,dbfile - - try: - from pysqlite2 import dbapi2 as sqlite - except: - print """ - ʹ sqlite ݿݣбҪ pysqlite֧ - python sqlite Ҫַģ pysqlite, 272kb - http://initd.org/tracker/pysqlite/wiki/pysqlite#Downloads - (Windows binaries for Python 2.x) - """ - raise SystemExit - - try: - db = sqlite.connect(dbfile,isolation_level=None) - db.create_function("unix_timestamp", 0, my_unix_timestamp) - conn = db.cursor() - except: - print "sqliteݿʧܣȷűĿ¼дȨ" - raise SystemExit - - sql=""" - /* ip: ֻҪipַ(xxx.xxx.xxx.xxx)Ĵ */ - /* type: 2: 1: 0:͸ -1: δ֪ */ - /* status: ֶαûõԺչ*/ - /* active: Ƿ 1: 0: */ - /* speed: Ӧʱ䣬speedԽС˵ٶԽ */ - - CREATE TABLE IF NOT EXISTS `proxier` ( - `ip` varchar(15) NOT NULL default '', - `port` int(6) NOT NULL default '0', - `type` int(11) NOT NULL default '-1', - `status` int(11) default '0', - `active` int(11) default NULL, - `time_added` int(11) NOT NULL default '0', - `time_checked` int(11) default '0', - `time_used` int(11) default '0', - `speed` float default NULL, - `area` varchar(120) default '--', /* λ */ - PRIMARY KEY (`ip`) - ); - /* - CREATE INDEX IF NOT EXISTS `type` ON proxier(`type`); - CREATE INDEX IF NOT EXISTS `time_used` ON proxier(`time_used`); - CREATE INDEX IF NOT EXISTS `speed` ON proxier(`speed`); - CREATE INDEX IF NOT EXISTS `active` ON proxier(`active`); - */ - PRAGMA encoding = "utf-8"; /* ݿ utf-8뱣 */ - """ - conn.executescript(sql) - conn.execute("""DELETE FROM `proxier` - where `time_added`< (unix_timestamp()-?) - and `active`=0""",(day_keep*86400,)) - - conn.execute("select count(`ip`) from `proxier`") - m1=conn.fetchone()[0] - if m1 is None:return - - conn.execute("""select count(`time_checked`) - from `proxier` where `time_checked`>0""") - m2=conn.fetchone()[0] - - if m2==0: - m3,m4,m5=0,"δ","δ" - else: - conn.execute("select count(`active`) from `proxier` where `active`=1") - m3=conn.fetchone()[0] - conn.execute("""select max(`time_checked`), min(`time_checked`) - from `proxier` where `time_checked`>0 limit 1""") - rs=conn.fetchone() - m4,m5=rs[0],rs[1] - m4=formattime(m4) - m5=formattime(m5) - print """ - %(m1)1d%(m2)1d֤%(m3)1d֤Ч - һμʱǣ%(m4)1s - Զһμʱ: %(m5)1s - ʾڼʱ䳬24СʱĴӦ¼Ч - """%{'m1':m1,'m2':m2,'m3':m3,'m4':m4,'m5':m5} - - - -def close_database(): - global db,conn - conn.close() - db.close() - conn=None - db=None - -if __name__ == '__main__': - open_database() - get_all_proxies() - patch_check_proxy(thread_num) - output_file() - close_database() - print "йѾ" +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +# -*- coding: gb2312 -*- +# vi:ts=4:et +目前程序能从下列网站抓取代理列表 + +http://www.cybersyndrome.net/ +http://www.pass-e.com/ +http://www.cnproxy.com/ +http://www.proxylists.net/ +http://www.my-proxy.com/ +http://www.samair.ru/proxy/ +http://proxy4free.com/ +http://proxylist.sakura.ne.jp/ +http://www.ipfree.cn/ +http://www.publicproxyservers.com/ +http://www.digitalcybersoft.com/ +http://www.checkedproxylists.com/ + +问:怎样才能添加自己的新网站，并自动让程序去抓取? +答: + +请注意源代码中以下函数的定义.从函数名的最后一个数字从1开始递增，目前已经到了13 + +def build_list_urls_1(page=5): +def parse_page_2(html=''): + +def build_list_urls_2(page=5): +def parse_page_2(html=''): + +....... + +def build_list_urls_13(page=5): +def parse_page_13(html=''): + + +你要做的就是添加 build_list_urls_14 和 parse_page_14 这两个函数 +比如你要从 www.somedomain.com 抓取 + /somepath/showlist.asp?page=1 + ... 到 + /somepath/showlist.asp?page=8 假设共8页 + +那么 build_list_urls_14 就应该这样定义 +要定义这个page这个参数的默认值为你要抓取的页面数8，这样才能正确到抓到8个页面 +def build_list_urls_14(page=8): + ..... + return [ #返回的是一个一维数组，数组每个元素都是你要抓取的页面的绝对地址 + 'http://www.somedomain.com/somepath/showlist.asp?page=1', + 'http://www.somedomain.com/somepath/showlist.asp?page=2', + 'http://www.somedomain.com/somepath/showlist.asp?page=3', + .... + 'http://www.somedomain.com/somepath/showlist.asp?page=8' + ] + +接下来再写一个函数 parse_page_14(html='')用来分析上面那个函数返回的那些页面html的内容 +并从html中提取代理地址 +注意： 这个函数会循环处理 parse_page_14 中的所有页面，传入的html就是那些页面的html文本 + +ip: 必须为 xxx.xxx.xxx.xxx 数字ip格式，不能为 www.xxx.com 格式 +port: 必须为 2-5位的数字 +type: 必须为 数字 2,1,0,-1 中的其中一个。这些数字代表代理服务器的类型 + 2:高度匿名代理 1: 普通匿名代理 0:透明代理 -1: 无法确定的代理类型 + #area: 代理所在国家或者地区， 必须转化为 utf8编码格式 + +def parse_page_14(html=''): + .... + return [ + [ip,port,type,area] + [ip,port,type,area] + ..... + .... + [ip,port,type,area] + ] + +最后，最重要的一点:修改全局变量 web_site_count的值，让他加递增1 web_site_count=14 + + + +问：我已经按照上面的说明成功的添加了一个自定义站点，我要再添加一个，怎么办? +答：既然已经知道怎么添加 build_list_urls_14 和 parse_page_14了 + +那么就按照同样的办法添加 +def build_list_urls_15(page=5): +def parse_page_15(html=''): + +这两个函数，并 更新全局变量 web_site_count=15 + +""" + +import urllib, time, random, re, threading, string + +web_site_count = 13 # 要抓取的网站数目 +day_keep = 2 # 删除数据库中保存时间大于day_keep天的 无效代理 +indebug = 1 + +thread_num = 100 # 开 thread_num 个线程检查代理 +check_in_one_call = thread_num * 10 # 本次程序运行时 最多检查的代理个数 + +skip_check_in_hour = 1 # 在时间 skip_check_in_hour内,不对同一个代理地址再次验证 +skip_get_in_hour = 8 # 每次采集新代理的最少时间间隔 (小时) + +proxy_array = [] # 这个数组保存将要添加到数据库的代理列表 +update_array = [] # 这个数组保存将要更新的代理的数据 + +db = None # 数据库全局对象 +conn = None +dbfile = 'proxier.db' # 数据库文件名 + +target_url = "http://www.baidu.com/" # 验证代理的时候通过代理访问这个地址 +target_string = "030173" # 如果返回的html中包含这个字符串， +target_timeout = 30 # 并且响应时间小于 target_timeout 秒 +# 那么我们就认为这个代理是有效的 + + +# 到处代理数据的文件格式，如果不想导出数据，请让这个变量为空 output_type='' + +output_type = 'xml' # 以下格式可选, 默认xml +# xml +# htm +# tab 制表符分隔, 兼容 excel +# csv 逗号分隔, 兼容 excel +# txt xxx.xxx.xxx.xxx:xx 格式 + +# 输出文件名 请保证这个数组含有六个元素 +output_filename = [ + 'uncheck', # 对于未检查的代理,保存到这个文件 + 'checkfail', # 已经检查，但是被标记为无效的代理,保存到这个文件 + 'ok_high_anon', # 高匿代理(且有效)的代理,按speed排序，最块的放前面 + 'ok_anonymous', # 普通匿名(且有效)的代理,按speed排序，最块的放前面 + 'ok_transparent', # 透明代理(且有效)的代理,按speed排序，最块的放前面 + 'ok_other' # 其他未知类型(且有效)的代理,按speed排序 +] + +# 输出数据的格式 支持的数据列有 +# _ip_ , _port_ , _type_ , _status_ , _active_ , +# _time_added_, _time_checked_ ,_time_used_ , _speed_, _area_ + +output_head_string = '' # 输出文件的头部字符串 +output_format = '' # 文件数据的格式 +output_foot_string = '' # 输出文件的底部字符串 + +if output_type == 'xml': + output_head_string = "<?xml version='1.0' encoding='gb2312'?><proxylist>\n" + output_format = """<item> + <ip>_ip_</ip> + <port>_port_</port> + <speed>_speed_</speed> + <last_check>_time_checked_</last_check> + <area>_area_</area> + </item> + """ + output_foot_string = "</proxylist>" +elif output_type == 'htm': + output_head_string = """<table border=1 width='100%'> + <tr><td>代理</td><td>最后检查</td><td>速度</td><td>地区</td></tr> + """ + output_format = """<tr> + <td>_ip_:_port_</td><td>_time_checked_</td><td>_speed_</td><td>_area_</td> + </tr> + """ + output_foot_string = "</table>" +else: + output_head_string = '' + output_foot_string = '' + +if output_type == "csv": + output_format = "_ip_, _port_, _type_, _speed_, _time_checked_, _area_\n" + +if output_type == "tab": + output_format = "_ip_\t_port_\t_speed_\t_time_checked_\t_area_\n" + +if output_type == "txt": + output_format = "_ip_:_port_\n" + + +# 输出文件的函数 +def output_file(): + global output_filename, output_head_string, output_foot_string, output_type + if output_type == '': + return + fnum = len(output_filename) + content = [] + for i in range(fnum): + content.append([output_head_string]) + + conn.execute("select * from `proxier` order by `active`,`type`,`speed` asc") + rs = conn.fetchall() + + for item in rs: + type, active = item[2], item[4] + if active is None: + content[0].append(formatline(item)) # 未检查 + elif active == 0: + content[1].append(formatline(item)) # 非法的代理 + elif active == 1 and type == 2: + content[2].append(formatline(item)) # 高匿 + elif active == 1 and type == 1: + content[3].append(formatline(item)) # 普通匿名 + elif active == 1 and type == 0: + content[4].append(formatline(item)) # 透明代理 + elif active == 1 and type == -1: + content[5].append(formatline(item)) # 未知类型的代理 + else: + pass + + for i in range(fnum): + content[i].append(output_foot_string) + f = open(output_filename[i] + "." + output_type, 'w') + f.write(string.join(content[i], '')) + f.close() + + +# 格式化输出每条记录 +def formatline(item): + global output_format + arr = ['_ip_', '_port_', '_type_', '_status_', '_active_', + '_time_added_', '_time_checked_', '_time_used_', + '_speed_', '_area_'] + s = output_format + for i in range(len(arr)): + s = string.replace(s, arr[i], str(formatitem(item[i], i))) + return s + + +# 对于数据库中的每个不同字段，要处理一下，中文要编码，日期字段要转化 +def formatitem(value, colnum): + global output_type + if (colnum == 9): + value = value.encode('cp936') + elif value is None: + value = '' + + if colnum == 5 or colnum == 6 or colnum == 7: # time_xxxed + value = string.atof(value) + if value < 1: + value = '' + else: + value = formattime(value) + + if value == '' and output_type == 'htm': value = ' ' + return value + + +def check_one_proxy(ip, port): + global update_array + global check_in_one_call + global target_url, target_string, target_timeout + + url = target_url + checkstr = target_string + timeout = target_timeout + ip = string.strip(ip) + proxy = ip + ':' + str(port) + proxies = {'http': 'http://' + proxy + '/'} + opener = urllib.FancyURLopener(proxies) + opener.addheaders = [ + ('User-agent', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)') + ] + t1 = time.time() + + if (url.find("?") == -1): + url = url + '?rnd=' + str(random.random()) + else: + url = url + '&rnd=' + str(random.random()) + + try: + f = opener.open(url) + s = f.read() + pos = s.find(checkstr) + except: + pos = -1 + pass + t2 = time.time() + timeused = t2 - t1 + if (timeused < timeout and pos > 0): + active = 1 + else: + active = 0 + update_array.append([ip, port, active, timeused]) + print (len(update_array), ' of ', check_in_one_call, " ", ip, ':', port, '--', int(timeused)) + + +def get_html(url=''): + opener = urllib.FancyURLopener({}) # 不使用代理 + # www.my-proxy.com 需要下面这个Cookie才能正常抓取 + opener.addheaders = [ + ('User-agent', 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1)'), + ('Cookie', 'permission=1') + ] + t = time.time() + if (url.find("?") == -1): + url = url + '?rnd=' + str(random.random()) + else: + url = url + '&rnd=' + str(random.random()) + try: + f = opener.open(url) + return f.read() + except: + return '' + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_1(page=5): + page = page + 1 + ret = [] + for i in range(1, page): + ret.append('http://proxy4free.com/page%(num)01d.html' % {'num': i}) + return ret + + +def parse_page_1(html=''): + matches = re.findall(r''' + <td>([\d\.]+)<\/td>[\s\n\r]* #ip + <td>([\d]+)<\/td>[\s\n\r]* #port + <td>([^\<]*)<\/td>[\s\n\r]* #type + <td>([^\<]*)<\/td> #area + ''', html, re.VERBOSE) + ret = [] + for match in matches: + ip = match[0] + port = match[1] + type = match[2] + area = match[3] + if (type == 'anonymous'): + type = 1 + elif (type == 'high anonymity'): + type = 2 + elif (type == 'transparent'): + type = 0 + else: + type = -1 + ret.append([ip, port, type, area]) + if indebug: print ('1', ip, port, type, area) + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_2(page=1): + return ['http://www.digitalcybersoft.com/ProxyList/fresh-proxy-list.shtml'] + + +def parse_page_2(html=''): + matches = re.findall(r''' + ((?:[\d]{1,3}\.){3}[\d]{1,3})\:([\d]+) #ip:port + \s+(Anonymous|Elite Proxy)[+\s]+ #type + (.+)\r?\n #area + ''', html, re.VERBOSE) + ret = [] + for match in matches: + ip = match[0] + port = match[1] + type = match[2] + area = match[3] + if (type == 'Anonymous'): + type = 1 + else: + type = 2 + ret.append([ip, port, type, area]) + if indebug: print ('2', ip, port, type, area) + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_3(page=15): + page = page + 1 + ret = [] + for i in range(1, page): + ret.append('http://www.samair.ru/proxy/proxy-%(num)02d.htm' % {'num': i}) + return ret + + +def parse_page_3(html=''): + matches = re.findall(r''' + <tr><td><span\sclass\="\w+">(\d{1,3})<\/span>\. #ip(part1) + <span\sclass\="\w+"> + (\d{1,3})<\/span> #ip(part2) + (\.\d{1,3}\.\d{1,3}) #ip(part3,part4) + + \:\r?\n(\d{2,5})<\/td> #port + <td>([^<]+)</td> #type + <td>[^<]+<\/td> + <td>([^<]+)<\/td> #area + <\/tr>''', html, re.VERBOSE) + ret = [] + for match in matches: + ip = match[0] + "." + match[1] + match[2] + port = match[3] + type = match[4] + area = match[5] + if (type == 'anonymous proxy server'): + type = 1 + elif (type == 'high-anonymous proxy server'): + type = 2 + elif (type == 'transparent proxy'): + type = 0 + else: + type = -1 + ret.append([ip, port, type, area]) + if indebug: print ('3', ip, port, type, area) + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_4(page=3): + page = page + 1 + ret = [] + for i in range(1, page): + ret.append('http://www.pass-e.com/proxy/index.php?page=%(n)01d' % {'n': i}) + return ret + + +def parse_page_4(html=''): + matches = re.findall(r""" + list + \('(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' #ip + \,'(\d{2,5})' #port + \,'(\d)' #type + \,'([^']+)'\) #area + \;\r?\n""", html, re.VERBOSE) + ret = [] + for match in matches: + ip = match[0] + port = match[1] + type = match[2] + area = match[3] + if (type == '1'): # type的判断可以查看抓回来的网页的javascript部分 + type = 1 + elif (type == '3'): + type = 2 + elif (type == '2'): + type = 0 + else: + type = -1 + if indebug: print ('4', ip, port, type, area) + area = unicode(area, 'cp936') + area = area.encode('utf8') + ret.append([ip, port, type, area]) + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_5(page=12): + page = page + 1 + ret = [] + for i in range(1, page): + ret.append('http://www.ipfree.cn/index2.asp?page=%(num)01d' % {'num': i}) + return ret + + +def parse_page_5(html=''): + matches = re.findall(r"<font color=black>([^<]*)</font>", html) + ret = [] + for index, match in enumerate(matches): + if (index % 3 == 0): + ip = matches[index + 1] + port = matches[index + 2] + type = -1 # 该网站未提供代理服务器类型 + if indebug: print ('5', ip, port, type, match) + area = unicode(match, 'cp936') + area = area.encode('utf8') + ret.append([ip, port, type, area]) + else: + continue + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_6(page=3): + page = page + 1 + ret = [] + for i in range(1, page): + ret.append('http://www.cnproxy.com/proxy%(num)01d.html' % {'num': i}) + return ret + + +def parse_page_6(html=''): + matches = re.findall(r'''<tr> + <td>([^&]+) #ip + ‌‍ + \:([^<]+) #port + </td> + <td>HTTP</td> + <td>[^<]+</td> + <td>([^<]+)</td> #area + </tr>''', html, re.VERBOSE) + ret = [] + for match in matches: + ip = match[0] + port = match[1] + type = -1 # 该网站未提供代理服务器类型 + area = match[2] + if indebug: print ('6', ip, port, type, area) + area = unicode(area, 'cp936') + area = area.encode('utf8') + ret.append([ip, port, type, area]) + + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_7(page=1): + return ['http://www.proxylists.net/http_highanon.txt'] + + +def parse_page_7(html=''): + matches = re.findall(r'(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})\:(\d{2,5})', html) + ret = [] + for match in matches: + ip = match[0] + port = match[1] + type = 2 + area = '--' + ret.append([ip, port, type, area]) + if indebug: print ('7', ip, port, type, area) + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_8(page=1): + return ['http://www.proxylists.net/http.txt'] + + +def parse_page_8(html=''): + matches = re.findall(r'(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})\:(\d{2,5})', html) + ret = [] + for match in matches: + ip = match[0] + port = match[1] + type = -1 + area = '--' + ret.append([ip, port, type, area]) + if indebug: print ('8', ip, port, type, area) + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_9(page=6): + page = page + 1 + ret = [] + for i in range(0, page): + ret.append('http://proxylist.sakura.ne.jp/index.htm?pages=%(n)01d' % {'n': i}) + return ret + + +def parse_page_9(html=''): + matches = re.findall(r''' + (\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}) #ip + \:(\d{2,5}) #port + <\/TD>[\s\r\n]* + <TD>([^<]+)</TD> #area + [\s\r\n]* + <TD>([^<]+)</TD> #type + ''', html, re.VERBOSE) + ret = [] + for match in matches: + ip = match[0] + port = match[1] + type = match[3] + area = match[2] + if (type == 'Anonymous'): + type = 1 + else: + type = -1 + ret.append([ip, port, type, area]) + if indebug: print ('9', ip, port, type, area) + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_10(page=5): + page = page + 1 + ret = [] + for i in range(1, page): + ret.append('http://www.publicproxyservers.com/page%(n)01d.html' % {'n': i}) + return ret + + +def parse_page_10(html=''): + matches = re.findall(r''' + (\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}) #ip + <\/td>[\s\r\n]* + <td[^>]+>(\d{2,5})<\/td> #port + [\s\r\n]* + <td>([^<]+)<\/td> #type + [\s\r\n]* + <td>([^<]+)<\/td> #area + ''', html, re.VERBOSE) + ret = [] + for match in matches: + ip = match[0] + port = match[1] + type = match[2] + area = match[3] + if (type == 'high anonymity'): + type = 2 + elif (type == 'anonymous'): + type = 1 + elif (type == 'transparent'): + type = 0 + else: + type = -1 + ret.append([ip, port, type, area]) + if indebug: print ('10', ip, port, type, area) + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_11(page=10): + page = page + 1 + ret = [] + for i in range(1, page): + ret.append('http://www.my-proxy.com/list/proxy.php?list=%(n)01d' % {'n': i}) + + ret.append('http://www.my-proxy.com/list/proxy.php?list=s1') + ret.append('http://www.my-proxy.com/list/proxy.php?list=s2') + ret.append('http://www.my-proxy.com/list/proxy.php?list=s3') + return ret + + +def parse_page_11(html=''): + matches = re.findall(r'(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})\:(\d{2,5})', html) + ret = [] + + if (html.find('(Level 1)') > 0): + type = 2 + elif (html.find('(Level 2)') > 0): + type = 1 + elif (html.find('(Level 3)') > 0): + type = 0 + else: + type = -1 + + for match in matches: + ip = match[0] + port = match[1] + area = '--' + ret.append([ip, port, type, area]) + if indebug: print ('11', ip, port, type, area) + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_12(page=4): + ret = [] + ret.append('http://www.cybersyndrome.net/plr4.html') + ret.append('http://www.cybersyndrome.net/pla4.html') + ret.append('http://www.cybersyndrome.net/pld4.html') + ret.append('http://www.cybersyndrome.net/pls4.html') + return ret + + +def parse_page_12(html=''): + matches = re.findall(r''' + onMouseOver\= + "s\(\'(\w\w)\'\)" #area + \sonMouseOut\="d\(\)"\s?c?l?a?s?s?\=?"? + (\w?) #type + "?> + (\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}) #ip + \:(\d{2,5}) #port + ''', html, re.VERBOSE) + ret = [] + for match in matches: + ip = match[2] + port = match[3] + area = match[0] + type = match[1] + if (type == 'A'): + type = 2 + elif (type == 'B'): + type = 1 + else: + type = 0 + ret.append([ip, port, type, area]) + if indebug: print ('12', ip, port, type, area) + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +def build_list_urls_13(page=3): + url = 'http://www.checkedproxylists.com/' + html = get_html(url) + matchs = re.findall(r""" + href\='([^']+)'>(?:high_anonymous|anonymous|transparent) + \sproxy\slist<\/a>""", html, re.VERBOSE) + return map(lambda x: url + x, matchs) + + +def parse_page_13(html=''): + html_matches = re.findall(r"eval\(unescape\('([^']+)'\)", html) + if (len(html_matches) > 0): + conent = urllib.unquote(html_matches[0]) + matches = re.findall(r"""<td>(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})<\/td> + <td>(\d{2,5})<\/td><\/tr>""", conent, re.VERBOSE) + ret = [] + if (html.find('<title>Checked Proxy Lists - proxylist_high_anonymous_') > 0): + type = 2 + elif (html.find('<title>Checked Proxy Lists - proxylist_anonymous_') > 0): + type = 1 + elif (html.find('<title>Checked Proxy Lists - proxylist_transparent_') > 0): + type = 0 + else: + type = -1 + + for match in matches: + ip = match[0] + port = match[1] + area = '--' + ret.append([ip, port, type, area]) + if indebug: print ('13', ip, port, type, area) + return ret + + +################################################################################ +# +## by Go_Rush(阿舜) from http://ashun.cnblogs.com/ +# +################################################################################ + + +# 线程类 + +class TEST(threading.Thread): + def __init__(self, action, index=None, checklist=None): + threading.Thread.__init__(self) + self.index = index + self.action = action + self.checklist = checklist + + def run(self): + if (self.action == 'getproxy'): + get_proxy_one_website(self.index) + else: + check_proxy(self.index, self.checklist) + + +def check_proxy(index, checklist=[]): + for item in checklist: + check_one_proxy(item[0], item[1]) + + +def patch_check_proxy(threadCount, action=''): + global check_in_one_call, skip_check_in_hour, conn + threads = [] + if (action == 'checknew'): # 检查所有新加入，并且从未被检查过的 + orderby = ' `time_added` desc ' + strwhere = ' `active` is null ' + elif (action == 'checkok'): # 再次检查 以前已经验证成功的 代理 + orderby = ' `time_checked` asc ' + strwhere = ' `active`=1 ' + elif (action == 'checkfail'): # 再次检查以前验证失败的代理 + orderby = ' `time_checked` asc ' + strwhere = ' `active`=0 ' + else: # 检查所有的 + orderby = ' `time_checked` asc ' + strwhere = ' 1=1 ' + sql = """ + select `ip`,`port` FROM `proxier` where + `time_checked` < (unix_timestamp()-%(skip_time)01s) + and %(strwhere)01s + order by %(order)01s + limit %(num)01d + """ % {'num': check_in_one_call, + 'strwhere': strwhere, + 'order': orderby, + 'skip_time': skip_check_in_hour * 3600} + conn.execute(sql) + rows = conn.fetchall() + + check_in_one_call = len(rows) + + # 计算每个线程将要检查的代理个数 + if len(rows) >= threadCount: + num_in_one_thread = len(rows) / threadCount + else: + num_in_one_thread = 1 + + threadCount = threadCount + 1 + print ("现在开始验证以下代理服务器.....") + for index in range(1, threadCount): + # 分配每个线程要检查的checklist,并把那些剩余任务留给最后一个线程 + checklist = rows[(index - 1) * num_in_one_thread:index * num_in_one_thread] + if (index + 1 == threadCount): + checklist = rows[(index - 1) * num_in_one_thread:] + + t = TEST(action, index, checklist) + t.setDaemon(True) + t.start() + threads.append((t)) + for thread in threads: + thread.join(60) + update_proxies() # 把所有的检查结果更新到数据库 + + +def get_proxy_one_website(index): + global proxy_array + func = 'build_list_urls_' + str(index) + parse_func = eval('parse_page_' + str(index)) + urls = eval(func + '()') + for url in urls: + html = get_html(url) + print (url) + proxylist = parse_func(html) + for proxy in proxylist: + ip = string.strip(proxy[0]) + port = string.strip(proxy[1]) + if (re.compile("^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}$").search(ip)): + type = str(proxy[2]) + area = string.strip(proxy[3]) + proxy_array.append([ip, port, type, area]) + + +def get_all_proxies(): + global web_site_count, conn, skip_get_in_hour + + # 检查最近添加代理是什么时候，避免短时间内多次抓取 + rs = conn.execute("select max(`time_added`) from `proxier` limit 1") + last_add = rs.fetchone()[0] + if (last_add and my_unix_timestamp() - last_add < skip_get_in_hour * 3600): + print (""" + 放弃抓取代理列表! + 因为最近一次抓取代理的时间是: %(t)1s + 这个时间距离现在的时间小于抓取代理的最小时间间隔: %(n)1d 小时 + 如果一定要现在抓取代理，请修改全局变量: skip_get_in_hour 的值 + """ % {'t': formattime(last_add), 'n': skip_get_in_hour}) + return + + print ("现在开始从以下" + str(web_site_count) + "个网站抓取代理列表....") + threads = [] + count = web_site_count + 1 + for index in range(1, count): + t = TEST('getproxy', index) + t.setDaemon(True) + t.start() + threads.append((t)) + for thread in threads: + thread.join(60) + add_proxies_to_db() + + +def add_proxies_to_db(): + global proxy_array + count = len(proxy_array) + for i in range(count): + item = proxy_array[i] + sql = """insert into `proxier` (`ip`,`port`,`type`,`time_added`,`area`) values + ('""" + item[0] + "'," + item[1] + "," + item[2] + ",unix_timestamp(),'" + clean_string(item[3]) + "')" + try: + conn.execute(sql) + print ("%(num)2.1f\%\t" % {'num': 100 * (i + 1) / count}, item[0], ":", item[1]) + except: + pass + + +def update_proxies(): + global update_array + for item in update_array: + sql = ''' + update `proxier` set `time_checked`=unix_timestamp(), + `active`=%(active)01d, + `speed`=%(speed)02.3f + where `ip`='%(ip)01s' and `port`=%(port)01d + ''' % {'active': item[2], 'speed': item[3], 'ip': item[0], 'port': item[1]} + try: + conn.execute(sql) + except: + pass + + # sqlite 不支持 unix_timestamp这个函数,所以我们要自己实现 + + +def my_unix_timestamp(): + return int(time.time()) + + +def clean_string(s): + tmp = re.sub(r"['\,\s\\\/]", ' ', s) + return re.sub(r"\s+", ' ', tmp) + + +def formattime(t): + return time.strftime('%c', time.gmtime(t + 8 * 3600)) + + +def open_database(): + global db, conn, day_keep, dbfile + + try: + from sqlite3 import dbapi2 as sqlite + except: + print (""" + 本程序使用 sqlite 做数据库来保存数据，运行本程序需要 pysqlite的支持 + python 访问 sqlite 需要到下面地址下载这个模块 pysqlite, 272kb + http://initd.org/tracker/pysqlite/wiki/pysqlite#Downloads + 下载(Windows binaries for Python 2.x) + """) + raise SystemExit + + try: + db = sqlite.connect(dbfile, isolation_level=None) + db.create_function("unix_timestamp", 0, my_unix_timestamp) + conn = db.cursor() + except: + print ("操作sqlite数据库失败，请确保脚本所在目录具有写权限") + raise SystemExit + + sql = """ + /* ip: 只要纯ip地址(xxx.xxx.xxx.xxx)的代理 */ + /* type: 代理类型 2:高匿 1:普匿 0:透明 -1: 未知 */ + /* status: 这个字段本程序还没有用到，留在这里作以后扩展*/ + /* active: 代理是否可用 1:可用 0:不可用 */ + /* speed: 请求相应时间，speed越小说明速度越快 */ + + CREATE TABLE IF NOT EXISTS `proxier` ( + `ip` varchar(15) NOT NULL default '', + `port` int(6) NOT NULL default '0', + `type` int(11) NOT NULL default '-1', + `status` int(11) default '0', + `active` int(11) default NULL, + `time_added` int(11) NOT NULL default '0', + `time_checked` int(11) default '0', + `time_used` int(11) default '0', + `speed` float default NULL, + `area` varchar(120) default '--', /* 代理服务器所在位置 */ + PRIMARY KEY (`ip`) + ); + /* + CREATE INDEX IF NOT EXISTS `type` ON proxier(`type`); + CREATE INDEX IF NOT EXISTS `time_used` ON proxier(`time_used`); + CREATE INDEX IF NOT EXISTS `speed` ON proxier(`speed`); + CREATE INDEX IF NOT EXISTS `active` ON proxier(`active`); + */ + PRAGMA encoding = "utf-8"; /* 数据库用 utf-8编码保存 */ + """ + conn.executescript(sql) + conn.execute("""DELETE FROM `proxier` + where `time_added`< (unix_timestamp()-?) + and `active`=0""", (day_keep * 86400,)) + + conn.execute("select count(`ip`) from `proxier`") + m1 = conn.fetchone()[0] + if m1 is None: return + + conn.execute("""select count(`time_checked`) + from `proxier` where `time_checked`>0""") + m2 = conn.fetchone()[0] + + if m2 == 0: + m3, m4, m5 = 0, "尚未检查", "尚未检查" + else: + conn.execute("select count(`active`) from `proxier` where `active`=1") + m3 = conn.fetchone()[0] + conn.execute("""select max(`time_checked`), min(`time_checked`) + from `proxier` where `time_checked`>0 limit 1""") + rs = conn.fetchone() + m4, m5 = rs[0], rs[1] + m4 = formattime(m4) + m5 = formattime(m5) + print (""" + 共%(m1)1d条代理，其中%(m2)1d个代理被验证过，%(m3)1d个代理验证有效。 + 最近一次检查时间是：%(m4)1s + 最远一次检查时间是: %(m5)1s + 提示：对于检查时间超过24小时的代理，应该重新检查其有效性 + """ % {'m1': m1, 'm2': m2, 'm3': m3, 'm4': m4, 'm5': m5}) + + +def close_database(): + global db, conn + conn.close() + db.close() + conn = None + db = None + + +if __name__ == '__main__': + open_database() + get_all_proxies() + patch_check_proxy(thread_num) + output_file() + close_database() + print ("所有工作已经完成") diff --git a/crawler/src/crawler_utils.py b/crawler/src/crawler_utils.py index 07cec5a..16e3cb7 100755 --- a/crawler/src/crawler_utils.py +++ b/crawler/src/crawler_utils.py @@ -6,7 +6,7 @@ 模拟发送请求。现在需要把此curl字符串处理成requests库可以传入的参数格式， http://stackoverflow.com/questions/23118249/whats-the-difference-between-request-payload-vs-form-data-as-seen-in-chrome """ - +import os import re import traceback import requests diff --git a/crawler/src/gevent_cralwer.py b/crawler/src/gevent_cralwer.py index 79830b4..e4b50d7 100644 --- a/crawler/src/gevent_cralwer.py +++ b/crawler/src/gevent_cralwer.py @@ -129,7 +129,7 @@ def shutdown(self): def worker(self, job, logger=logging.getLogger(__name__ + '.worker')): """A simple worker that fetches urls based on jobs and puts the - ammended jobs on the outq for processing in the pipeline thread. + ammended jobs on the outq for processing in the pipeline threads. After each job is fetched, but before the worker sets the finished event, the spider's preprocess method will be called on the job. This is its opportunity to add urls to the job queue. Heavy processing @@ -177,9 +177,9 @@ def fetch(url): def asy(): threads = [] - for i in range(1000): - # url = 'http://baidu.com' + '?a=' + str(i) - url = 'http://localhost:8080' + '?a=' + str(i) + for i in range(10): + url = 'http://baidu.com' + '?a=' + str(i) + # url = 'http://localhost:8080' + '?a=' + str(i) threads.append(gevent.spawn(fetch, url)) gevent.joinall(threads) diff --git a/crawler/src/grequests_crawler.py b/crawler/src/grequests_crawler.py index 5760a72..04f23b3 100644 --- a/crawler/src/grequests_crawler.py +++ b/crawler/src/grequests_crawler.py @@ -8,4 +8,4 @@ cs = grequests.map(rs) for i in cs: - print i.content + print(i.content.decode()) diff --git a/crawler/src/mul_spider.py b/crawler/src/mul_spider.py index 8c6e02b..91350cf 100644 --- a/crawler/src/mul_spider.py +++ b/crawler/src/mul_spider.py @@ -9,6 +9,7 @@ class AsySpider(object): """A simple class of asynchronous spider.""" + def __init__(self, urls, concurrency): urls.reverse() self.urls = urls @@ -18,7 +19,7 @@ def __init__(self, urls, concurrency): self._fetched = set() def handle_page(self, url, html): - #print(url, html) + # print(url, html) print(url) @gen.coroutine @@ -85,21 +86,21 @@ def main(): _st = time.time() p = Pool() all_num = 73000 - num = 4 # number of cpu cores + num = 4 # number of cpu cores per_num, left = divmod(all_num, num) s = range(0, all_num, per_num) res = [] - for i in range(len(s)-1): - res.append((s[i], s[i+1])) - res.append((s[len(s)-1], all_num)) - print res + for i in range(len(s) - 1): + res.append((s[i], s[i + 1])) + res.append((s[len(s) - 1], all_num)) + print(res) for i in res: p.apply_async(run_spider, args=(i[0], i[1],)) p.close() p.join() - print time.time()-_st + print(time.time() - _st) if __name__ == '__main__': diff --git a/crawler/src/parse_header.py b/crawler/src/parse_header.py index 0eb5afb..1f9e979 100644 --- a/crawler/src/parse_header.py +++ b/crawler/src/parse_header.py @@ -6,10 +6,10 @@ # 如果不用cookies参数，使用headers参数也可以这样加上cookie，注意不是cookie(s) -headers = { - 'cookie': cookies_str -} -r = requests.get(url, headers=headers).content +# headers = { +# 'cookie': cookies_str +# } +# r = requests.get(url, headers=headers).content def headers_to_dict(s): @@ -74,10 +74,11 @@ def to_dict(s, s_type): def print_li(li): if isinstance(li, dict): for k, v in li.items(): - print k, v + print(k,':', v) else: for i in li: - print i + print(i) + # for test @@ -104,6 +105,7 @@ def print_li(li): first=false&pn=1&sortField=0&havemark=0 """ + def test_headers_to_dict(): d = headers_to_dict(headers_string) print_li(d) @@ -127,7 +129,7 @@ def test_to_dict(): print_li(to_dict(form_string, 'form')) -#test_headers_to_dict() -#test_cookies_to_dict() -#test_form_to_dict() +# test_headers_to_dict() +# test_cookies_to_dict() +# test_form_to_dict() test_to_dict() diff --git a/crawler/src/proxy_req.py b/crawler/src/proxy_req.py index 75d332d..284ba23 100644 --- a/crawler/src/proxy_req.py +++ b/crawler/src/proxy_req.py @@ -19,39 +19,38 @@ def use_lantern(): def user_socks5(): - # requests from version 2.10.0 support socks proxy - # pip install -U requests[socks] - proxies = {'http': "socks5://myproxy:9191"} - requests.get('http://example.org', proxies=proxies) + # requests from version 2.10.0 support socks proxy + # pip install -U requests[socks] + proxies = {'http': "socks5://myproxy:9191"} + requests.get('http://example.org', proxies=proxies) - # tornado proxy demo - # sudo apt-get install libcurl-dev librtmp-dev - # pip install tornado pycurl + # tornado proxy demo + # sudo apt-get install libcurl-dev librtmp-dev + # pip install tornado pycurl def tornado_proxy(): - from tornado import httpclient, ioloop + from tornado import httpclient, ioloop - config = { - 'proxy_host': 'YOUR_PROXY_HOSTNAME_OR_IP_ADDRESS', - 'proxy_port': 3128 - } + config = { + 'proxy_host': 'YOUR_PROXY_HOSTNAME_OR_IP_ADDRESS', + 'proxy_port': 3128 + } - httpclient.AsyncHTTPClient.configure( - "tornado.curl_httpclient.CurlAsyncHTTPClient") + httpclient.AsyncHTTPClient.configure( + "tornado.curl_httpclient.CurlAsyncHTTPClient") - def handle_request(response): - if response.error: - print("Error:", response.error) - else: - print(response.body) - ioloop.IOLoop.instance().stop() + def handle_request(response): + if response.error: + print("Error:", response.error) + else: + print(response.body) + ioloop.IOLoop.instance().stop() - http_client = httpclient.AsyncHTTPClient() - http_client.fetch("http://twitter.com/", - handle_request, **config) - ioloop.IOLoop.instance().start() + http_client = httpclient.AsyncHTTPClient() + http_client.fetch("http://twitter.com/",handle_request, **config) + ioloop.IOLoop.instance().start() def get_proxy_dict(ip, port, proxy_type='http' or 'socks5'): diff --git a/crawler/src/search_engine_header.py b/crawler/src/search_engine_header.py index 371f2cd..1fe217c 100644 --- a/crawler/src/search_engine_header.py +++ b/crawler/src/search_engine_header.py @@ -2,12 +2,14 @@ # -*- coding:utf-8 -*- # 模仿百度蜘蛛 +import requests + +url = 'https://www.baidu.com/' headers = { 'User-Agent': 'Mozilla/5.0 (compatible; Baiduspider/2.0; +http://www.baidu.com/search/spider.html)', } r = requests.get(url, headers=headers) - - +print(r.text) ''' Baiduspider: Mozilla/5.0 (compatible; Baiduspider/2.0; +http://www.baidu.com/search/spider.html) @@ -17,8 +19,6 @@ Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html) ''' - - UA_LIST = [ 'Mozilla/5.0 (compatible; Baiduspider/2.0; +http://www.baidu.com/search/spider.html)', 'Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)', diff --git a/crawler/src/sync_spider.py b/crawler/src/sync_spider.py index 275b29c..e92e038 100644 --- a/crawler/src/sync_spider.py +++ b/crawler/src/sync_spider.py @@ -4,6 +4,8 @@ import time from datetime import timedelta import traceback + +from crawler.src.req import MySpider from extract import extract from requests import get diff --git a/crawler/src/test.py b/crawler/src/test.py index 1184b0b..3841fa7 100644 --- a/crawler/src/test.py +++ b/crawler/src/test.py @@ -2,8 +2,10 @@ # -*- coding:utf-8 -*- import time + +from crawler.src.req import AsyncSpider from extract import * -from async_spider import AsyncSpider +# from async_spider import AsyncSpider from sync_spider import SyncSpider diff --git a/crawler/src/tor_ip.py b/crawler/src/tor_ip.py index 51b19a5..56cf39f 100644 --- a/crawler/src/tor_ip.py +++ b/crawler/src/tor_ip.py @@ -5,7 +5,7 @@ import requests import requesocks -#url = 'https://api.ipify.org?format=json' +# url = 'https://api.ipify.org?format=json' url = 'http://httpbin.org/ip' @@ -15,18 +15,18 @@ def get_ip_socks_tor(): def getip_requests(url): - print "(+) Sending request with plain requests..." + print("(+) Sending request with plain requests...") r = requests.get(url) - print "(+) IP is: " + r.text.replace("\n", "") + print("(+) IP is: " + r.text.replace("\n", "")) def getip_requesocks(url): - print "(+) Sending request with requesocks..." + print("(+) Sending request with requesocks...") session = requesocks.session() session.proxies = {'http': 'socks5://127.0.0.1:9050', 'https': 'socks5://127.0.0.1:9050'} r = session.get(url) - print "(+) IP is: " + r.text.replace("\n", "") + print("(+) IP is: " + r.text.replace("\n", "")) def tor_requests(): @@ -35,11 +35,11 @@ def tor_requests(): 'https': 'socks5://127.0.0.1:9050', } r = requests.get(url, proxies=proxies) - print r.text + print(r.text) def main(): - print "Running tests..." + print("Running tests...") getip_requests(url) getip_requesocks(url) os.system("""(echo authenticate '"yourpassword"'; echo signal newnym; echo quit) | nc localhost 9051""") @@ -48,4 +48,4 @@ def main(): if __name__ == "__main__": main() - #tor_requests() + # tor_requests() diff --git a/crawler/src/tt.py b/crawler/src/tt.py index f8cb3ee..0dd9b86 100755 --- a/crawler/src/tt.py +++ b/crawler/src/tt.py @@ -6,7 +6,7 @@ 模拟发送请求。现在需要把此curl字符串处理成requests库可以传入的参数格式， http://stackoverflow.com/questions/23118249/whats-the-difference-between-request-payload-vs-form-data-as-seen-in-chrome """ - +import os import re import traceback import requests @@ -213,7 +213,7 @@ def form_data_to_dict(s): def change_ip(): """change_ip use tor as socks proxy, this command can change tor ip""" - os.system("""(echo authenticate '"%s"'; echo signal newnym; echo quit) | nc localhost 9051"""%CONFIG.CRAWLER.PROXIES_PASSWORD) + os.system("""(echo authenticate '"%s"'; echo signal newnym; echo quit) | nc localhost 9051"""%'CONFIG.CRAWLER.PROXIES_PASSWORD)' print(my_ip()) @@ -268,17 +268,17 @@ def random_ip(): headers = {'X-Forwarded-For': '192.155.212.33', 'REMOTE_ADDR': '192.155.212.4', 'X-Real-Ip': '192.155.323.4'} - print requests.get(url, headers=headers).text + print (requests.get(url, headers=headers).text) url = 'http://httpbin.org/ip' headers = {'X-Forwarded-For': '192.155.212.33', 'REMOTE_ADDR': '192.155.212.4', 'X-Real-Ip': '192.155.323.4'} - print requests.get(url, headers=headers).text + print (requests.get(url, headers=headers).text) url = 'https://api.ipify.org?format=json' headers = {'X-Forwarded-For': '192.155.212.33', 'REMOTE_ADDR': '192.155.212.4', 'X-Real-Ip': '192.155.323.4'} - print requests.get(url, headers=headers).text + print (requests.get(url, headers=headers).text) diff --git a/crawler/src/xpath_utils.py b/crawler/src/xpath_utils.py index 2daa1cb..f62e87e 100644 --- a/crawler/src/xpath_utils.py +++ b/crawler/src/xpath_utils.py @@ -1,7 +1,12 @@ # -*- coding: utf-8 -*- import time -from urlparse import urljoin +import sys +if sys.version_info[0]==2: + from urlparse import urljoin +else: + from urllib.parse import urljoin + import concurrent.futures from lxml import etree from crawler_utils import (logged_class, retry_get_html, retry_get, diff --git a/crawler/toutiao/toutiao_crawler.py b/crawler/toutiao/toutiao_crawler.py index d68575d..ba94587 100644 --- a/crawler/toutiao/toutiao_crawler.py +++ b/crawler/toutiao/toutiao_crawler.py @@ -25,8 +25,9 @@ def gid(): return redis.incr(R_GID) """ + def get_article(html): - article = extract('<div class="article-content">', '</div>',html) + article = extract('<div class="article-content">', '</div>', html) return article @@ -35,45 +36,46 @@ def get_logo_url(html): logo = extract('<img src="', '"', logo) return logo + class ToutiaoSpider(object): def __init__(self, db): - ¦ self._db = db + self._db = db def fetch(self, url): - ¦ try: - ¦ ¦ html = requests.get(url, timeout=10).text - ¦ except: - ¦ ¦ html = '' - ¦ ¦ traceback.print_exc() - ¦ return html - + try: + html = requests.get(url, timeout=10).text + except: + html = '' + traceback.print_exc() + return html def parse_data(self, json_str): - ¦ data = json.loads(json_str).get('data') - ¦ site_to_get_field = ['media_name', 'media_url', 'url', 'display_url'] - ¦ post_to_get_field = ['title', 'abstract', 'keywords', 'digg_count', 'bury_count', 'comment_count', 'article-url'] - ¦ res_site = [] - ¦ res_post = [] - - ¦ for each in data: - ¦ ¦ media_name = each.get('media_name') - ¦ ¦ if not media_name: - ¦ ¦ ¦ continue - ¦ ¦ site = {} - ¦ ¦ site['name'] = each.get('media_name') - ¦ ¦ site['id'] = each.get('media_url') - ¦ ¦ site['gid'] = 1 #gid() - ¦ ¦ site['url'] = urlparse(each.get('url')).netloc - ¦ ¦ url = each.get('display_url') - ¦ ¦ html = requests.get(url).text - ¦ ¦ site['logo'] = get_logo_url(html) - ¦ ¦ res_site.append(site) - - ¦ ¦ post = {} - ¦ ¦ for k in post_to_get_field: - ¦ ¦ ¦ post[k] = each.get(k) - ¦ ¦ post['html'] = get_article(html) - ¦ ¦ post['source_gid'] = site['gid'] - ¦ ¦ res_post.append(post) - - ¦ return [res_site, res_post] + data = json.loads(json_str).get('data') + site_to_get_field = ['media_name', 'media_url', 'url', 'display_url'] + post_to_get_field = ['title', 'abstract', 'keywords', 'digg_count', 'bury_count', 'comment_count', + 'article-url'] + res_site = [] + res_post = [] + + for each in data: + media_name = each.get('media_name') + if not media_name: + continue + site = {} + site['name'] = each.get('media_name') + site['id'] = each.get('media_url') + site['gid'] = 1 # gid() + site['url'] = urlparse(each.get('url')).netloc + url = each.get('display_url') + html = requests.get(url).text + site['logo'] = get_logo_url(html) + res_site.append(site) + + post = {} + for k in post_to_get_field: + post[k] = each.get(k) + post['html'] = get_article(html) + post['source_gid'] = site['gid'] + res_post.append(post) + + return [res_site, res_post] diff --git a/curl/parse_curl.py b/curl/parse_curl.py new file mode 100644 index 0000000..3b98ad7 --- /dev/null +++ b/curl/parse_curl.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import uncurl + +# 将curl命令转换成python代码 +cmd = """curl 'https://www.jianshu.com/u/66ffe8731054' \ + -H 'Connection: keep-alive' \ + -H 'Cache-Control: max-age=0' \ + -H 'sec-ch-ua: "Google Chrome";v="89", "Chromium";v="89", ";Not A Brand";v="99"' \ + -H 'sec-ch-ua-mobile: ?0' \ + -H 'Upgrade-Insecure-Requests: 1' \ + -H 'User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36' \ + -H 'Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9' \ + -H 'Sec-Fetch-Site: none' \ + -H 'Sec-Fetch-Mode: navigate' \ + -H 'Sec-Fetch-User: ?1' \ + -H 'Sec-Fetch-Dest: document' \ + -H 'Accept-Language: zh-CN,zh;q=0.9' \ + -H 'Cookie: read_mode=day; default_font=font2; locale=zh-CN; Hm_lvt_0c0e9d9b1e7d617b3e6842e85b9fb068=1616237295; __yadk_uid=ynf9cBVSMNLLsCZzCeKyg7tsQHodqm8B; web_login_version=MTYxNjIzNzMyOA%3D%3D--d359cc29a88014cd936a9af99bd35db45a669991; _ga=GA1.2.1476924542.1616237344; remember_user_token=W1sxMjI0MTIyNl0sIiQyYSQxMSRZNk1ESFBXbHNqYlhVSjEuTjM2bWcuIiwiMTYxNjQyOTk2MC45NzI0NTgxIl0%3D--f2fad88d4e055ce210350d8082be86b075ddcf75; _m7e_session_core=d100c914638dc090d837d9b63f072033; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%221784f3ff75853c-0c274aca237e5-5771031-1327104-1784f3ff7599a3%22%2C%22first_id%22%3A%22%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%2C%22%24latest_referrer%22%3A%22%22%7D%2C%22%24device_id%22%3A%221784f3ff75853c-0c274aca237e5-5771031-1327104-1784f3ff7599a3%22%7D; Hm_lpvt_0c0e9d9b1e7d617b3e6842e85b9fb068=1616429971' \ + -H 'If-None-Match: W/"f44091782b9faf76ebeaca98cfd8b7b7"' \ + --compressed""" + +result = uncurl.parse(cmd) +print(result) +""" +result: +requests.get("https://www.jianshu.com/u/66ffe8731054", + headers={ + "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9", + "Accept-Language": "zh-CN,zh;q=0.9", + "Cache-Control": "max-age=0", + "Connection": "keep-alive", + "If-None-Match": "W/\"f44091782b9faf76ebeaca98cfd8b7b7\"", + "Sec-Fetch-Dest": "document", + "Sec-Fetch-Mode": "navigate", + "Sec-Fetch-Site": "none", + "Sec-Fetch-User": "?1", + "Upgrade-Insecure-Requests": "1", + "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36", + "sec-ch-ua": "\"Google Chrome\";v=\"89\", \"Chromium\";v=\"89\", \";Not A Brand\";v=\"99\"", + "sec-ch-ua-mobile": "?0" + }, + cookies={ + "Hm_lpvt_0c0e9d9b1e7d617b3e6842e85b9fb068": "1616429971", + "Hm_lvt_0c0e9d9b1e7d617b3e6842e85b9fb068": "1616237295", + "__yadk_uid": "ynf9cBVSMNLLsCZzCeKyg7tsQHodqm8B", + "_ga": "GA1.2.1476924542.1616237344", + "_m7e_session_core": "d100c914638dc090d837d9b63f072033", + "default_font": "font2", + "locale": "zh-CN", + "read_mode": "day", + "remember_user_token": "W1sxMjI0MTIyNl0sIiQyYSQxMSRZNk1ESFBXbHNqYlhVSjEuTjM2bWcuIiwiMTYxNjQyOTk2MC45NzI0NTgxIl0%3D--f2fad88d4e055ce210350d8082be86b075ddcf75", + "sensorsdata2015jssdkcross": "%7B%22distinct_id%22%3A%221784f3ff75853c-0c274aca237e5-5771031-1327104-1784f3ff7599a3%22%2C%22first_id%22%3A%22%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%2C%22%24latest_referrer%22%3A%22%22%7D%2C%22%24device_id%22%3A%221784f3ff75853c-0c274aca237e5-5771031-1327104-1784f3ff7599a3%22%7D", + "web_login_version": "MTYxNjIzNzMyOA%3D%3D--d359cc29a88014cd936a9af99bd35db45a669991" + }, +) +""" diff --git a/data_visualization/mapa.csv b/data_visualization/mapa.csv new file mode 100644 index 0000000..6ea4b41 --- /dev/null +++ b/data_visualization/mapa.csv @@ -0,0 +1,251 @@ +País,data science,machine learning,deep learning +Santa Elena,100.0,100.0,52.0 +India,99.0,77.0,25.0 +Ruanda,,, +Lesoto,,, +Singapur,91.0,79.0,52.0 +Zimbabue,,, +Botsuana,,, +Nepal,77.0,49.0, +Nigeria,72.0,25.0,8.0 +Etiopía,,, +Ghana,60.0,, +Kenia,60.0,27.0, +Pakistán,56.0,38.0,17.0 +Malaui,,, +Uganda,,, +Hong Kong,48.0,49.0,39.0 +Estados Unidos,48.0,30.0,15.0 +Camerún,,, +Sudáfrica,42.0,21.0,9.0 +Australia,38.0,22.0,12.0 +Bangladés,37.0,39.0,15.0 +Zambia,,, +Sri Lanka,36.0,36.0,12.0 +Emiratos Árabes Unidos,36.0,20.0,9.0 +Mauricio,,, +Irlanda,35.0,31.0,13.0 +Filipinas,34.0,14.0,6.0 +Canadá,32.0,28.0,15.0 +Namibia,,, +Somalia,,, +Reino Unido,30.0,22.0,10.0 +Malasia,30.0,20.0,12.0 +Myanmar (Birmania),,, +Líbano,,, +Jamaica,,, +Catar,,, +Suiza,28.0,32.0,20.0 +Tanzania,,, +Países Bajos,27.0,20.0,11.0 +Túnez,27.0,27.0,22.0 +Senegal,,, +Corea del Sur,25.0,58.0,100.0 +Nueva Zelanda,25.0,20.0,10.0 +Trinidad y Tobago,,, +Chipre,,, +Côte d’Ivoire,,, +Laos,,, +Luxemburgo,,, +Madagascar,,, +Afganistán,,, +Armenia,,, +Omán,,, +Macao,,, +Israel,20.0,35.0,31.0 +Baréin,,, +Islandia,,, +Mongolia,,, +Portugal,17.0,18.0,8.0 +Grecia,17.0,17.0,8.0 +Macedonia del Norte,,, +Serbia,16.0,12.0,5.0 +Brunéi,,, +Dinamarca,15.0,23.0,10.0 +Austria,15.0,16.0,11.0 +Alemania,15.0,18.0,13.0 +Noruega,15.0,24.0,13.0 +Marruecos,14.0,13.0,9.0 +Bélgica,14.0,15.0,10.0 +Egipto,14.0,16.0,9.0 +Kuwait,,, +Tailandia,13.0,10.0,5.0 +Puerto Rico,,, +Sudán,,, +Nueva Caledonia,,, +Kosovo,,, +Taiwán,13.0,26.0,34.0 +Azerbaiyán,,, +Jordania,,, +Estonia,,, +Albania,,, +Finlandia,12.0,20.0,10.0 +Suecia,12.0,23.0,13.0 +Irán,12.0,17.0,15.0 +Arabia Saudí,12.0,9.0,5.0 +Chile,11.0,10.0,4.0 +Kazajistán,,, +Francia,11.0,12.0,10.0 +Angola,,, +Costa Rica,,, +Indonesia,10.0,8.0,4.0 +Croacia,,13.0,6.0 +Palestina,,, +Camboya,,, +China,10.0,27.0,36.0 +España,10.0,11.0,6.0 +Bielorrusia,,, +Uruguay,,, +Italia,9.0,10.0,5.0 +Eslovenia,,, +Vietnam,9.0,13.0,7.0 +Brasil,9.0,7.0,3.0 +Rusia,9.0,8.0,5.0 +Ucrania,9.0,7.0,3.0 +Lituania,,, +Panamá,,, +Perú,8.0,10.0,3.0 +Chequia,8.0,13.0,7.0 +Kirguistán,,, +Polonia,8.0,8.0,5.0 +Uzbekistán,,, +Georgia,,, +Hungría,8.0,8.0,7.0 +Letonia,,, +Honduras,,, +Guatemala,,, +Bosnia y Herzegovina,,, +Bulgaria,,9.0, +Colombia,7.0,10.0,4.0 +Argelia,,9.0,9.0 +Bolivia,,, +México,6.0,6.0,2.0 +El Salvador,,, +Siria,,, +Argentina,6.0,6.0,2.0 +Rumanía,6.0,11.0,5.0 +República Dominicana,,, +Ecuador,,, +Moldavia,,, +Eslovaquia,,, +Paraguay,,, +Turquía,4.0,6.0,5.0 +Irak,,, +Japón,3.0,6.0,16.0 +Venezuela,,, +Corea del Norte,,, +Kiribati,,, +Samoa Americana,,, +Eritrea,,, +Montserrat,,, +Esuatini,,, +Palaos,,, +Gambia,,, +Micronesia,,, +Islas Marshall,,, +Bután,,, +Islas Salomón,,, +Islas Vírgenes de EE. UU.,,, +Antigua y Barbuda,,, +San Cristóbal y Nieves,,, +Islas Marianas del Norte,,, +Liechtenstein,,, +Santo Tomé y Príncipe,,, +Samoa,,, +Groenlandia,,, +Guyana,,, +Benín,,, +Papúa Nueva Guinea,,, +Liberia,,, +Belice,,, +Anguila,,, +Islas Vírgenes Británicas,,, +Tonga,,, +Gibraltar,,, +Burundi,,, +San Vicente y las Granadinas,,, +Guinea-Bisáu,,, +Granada,,, +Vanuatu,,, +Sudán del Sur,,, +Barbados,,, +Guam,,, +Sint Maarten,,, +Congo,,, +Sáhara Occidental,,, +Islas Caimán,,, +Malta,,, +Islas Turcas y Caicos,,, +Sierra Leona,,, +Togo,,, +Caribe neerlandés,,, +Bermudas,,, +San Martín,,, +Níger,,, +Isla de Man,,, +Fiyi,,, +Aruba,,, +Santa Lucía,,, +Maldivas,,, +San Marino,,, +Guernsey,,, +Cuba,,, +Yibuti,,, +Haití,,, +San Bartolomé,,, +Jersey,,, +Guinea Ecuatorial,,, +Gabón,,, +Chad,,, +Curazao,,, +Bahamas,,, +República Democrática del Congo,,, +Seychelles,,, +Mozambique,,, +Burkina Faso,,, +Dominica,,, +Surinam,,, +Islas Feroe,,, +Montenegro,,, +Cabo Verde,,, +Guinea,,, +Mali,,, +Timor-Leste,,, +Andorra,,, +Turkmenistán,,, +Libia,,, +Mónaco,,, +Tayikistán,,, +Nicaragua,,, +Polinesia Francesa,,, +Mauritania,,, +Guayana Francesa,,, +Yemen,,, +Martinica,,, +Guadalupe,,, +Reunión,,, +Islas Åland,,, +Antártida,,, +Territorios Australes Franceses,,, +Isla Bouvet,,, +República Centroafricana,,, +Islas Cocos,,, +Islas Cook,,, +Comoras,,, +Isla de Navidad,,, +Islas Malvinas (Islas Falkland),,, +Islas Heard y McDonald,,, +Territorio Británico del Océano Índico,,, +Mayotte,,, +Isla Norfolk,,, +Niue,,, +Nauru,,, +Islas Pitcairn,,, +Islas Georgia del Sur y Sandwich del Sur,,, +Svalbard y Jan Mayen,,, +San Pedro y Miquelón,,, +Tokelau,,, +Tuvalu,,, +Islas menores alejadas de EE. UU.,,, +Ciudad del Vaticano,,, +Wallis y Futuna,,, diff --git a/data_visualization/simple.ipynb b/data_visualization/simple.ipynb new file mode 100644 index 0000000..da3bae8 --- /dev/null +++ b/data_visualization/simple.ipynb @@ -0,0 +1,1032 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 数据集：https://github.com/albertsl/datasets\n", + "# 原文链接：https://mp.weixin.qq.com/s?__biz=MzUyOTAwMzI4NA==&mid=2247508542&idx=1&sn=1acd06e08f32ce51283afafced184877&chksm=fa654445cd12cd53ec4f0bbddb85cc3479ee60a7252007acd43302824acbb755d273aa383c8f&scene=132#wechat_redirect\n", + "# 这些数据集都是与人工智能相关的三个术语（数据科学，机器学习和深度学习）在互联网上搜索流行度的数据，从搜索引擎中提取而来。\n", + "# 该数据集包含了两个文件temporal.csv和mapa.csv。\n", + "# 在这个教程中，我们将更多使用的第一个包括随时间推移（从2004年到2020年）的三个术语的受欢迎程度数据。另外，我添加了一个分类变量（1和0）来演示带有分类变量的图表的功能。\n", + "# mapa.csv文件包含按国家/地区分隔的受欢迎程度数据。在最后的可视化地图时，我们会用到它。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Mes data science machine learning deep learning categorical\n", + "0 2004-01-01 12 18 4 1\n", + "1 2004-02-01 12 21 2 1\n", + "2 2004-03-01 9 21 2 1\n", + "3 2004-04-01 10 16 4 1\n", + "4 2004-05-01 7 14 3 1\n", + "5 2004-06-01 9 17 3 1\n", + "6 2004-07-01 9 16 3 1\n", + "7 2004-08-01 7 14 3 1\n", + "8 2004-09-01 10 17 4 1\n", + "9 2004-10-01 8 17 4 1" + ], + "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Mes</th>\n <th>data science</th>\n <th>machine learning</th>\n <th>deep learning</th>\n <th>categorical</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2004-01-01</td>\n <td>12</td>\n <td>18</td>\n <td>4</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2004-02-01</td>\n <td>12</td>\n <td>21</td>\n <td>2</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2004-03-01</td>\n <td>9</td>\n <td>21</td>\n <td>2</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2004-04-01</td>\n <td>10</td>\n <td>16</td>\n <td>4</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2004-05-01</td>\n <td>7</td>\n <td>14</td>\n <td>3</td>\n <td>1</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2004-06-01</td>\n <td>9</td>\n <td>17</td>\n <td>3</td>\n <td>1</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2004-07-01</td>\n <td>9</td>\n <td>16</td>\n <td>3</td>\n <td>1</td>\n </tr>\n <tr>\n <th>7</th>\n <td>2004-08-01</td>\n <td>7</td>\n <td>14</td>\n <td>3</td>\n <td>1</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2004-09-01</td>\n <td>10</td>\n <td>17</td>\n <td>4</td>\n <td>1</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2004-10-01</td>\n <td>8</td>\n <td>17</td>\n <td>4</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>" + }, + "metadata": {}, + "execution_count": 1 + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv('temporal.csv')\n", + "df.head(10) #View first 10 data rows" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " data science machine learning deep learning categorical\n", + "count 194.000000 194.000000 194.000000 194.000000\n", + "mean 20.953608 27.396907 24.231959 0.257732\n", + "std 23.951006 28.091490 34.476887 0.438517\n", + "min 4.000000 7.000000 1.000000 0.000000\n", + "25% 6.000000 9.000000 2.000000 0.000000\n", + "50% 8.000000 13.000000 3.000000 0.000000\n", + "75% 26.750000 31.500000 34.000000 1.000000\n", + "max 100.000000 100.000000 100.000000 1.000000" + ], + "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>data science</th>\n <th>machine learning</th>\n <th>deep learning</th>\n <th>categorical</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>194.000000</td>\n <td>194.000000</td>\n <td>194.000000</td>\n <td>194.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>20.953608</td>\n <td>27.396907</td>\n <td>24.231959</td>\n <td>0.257732</td>\n </tr>\n <tr>\n <th>std</th>\n <td>23.951006</td>\n <td>28.091490</td>\n <td>34.476887</td>\n <td>0.438517</td>\n </tr>\n <tr>\n <th>min</th>\n <td>4.000000</td>\n <td>7.000000</td>\n <td>1.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>6.000000</td>\n <td>9.000000</td>\n <td>2.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>8.000000</td>\n <td>13.000000</td>\n <td>3.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>26.750000</td>\n <td>31.500000</td>\n <td>34.000000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>100.000000</td>\n <td>100.000000</td>\n <td>100.000000</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>" + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "df.describe()\n", + "# 使用命令描述，我们将看到数据如何分布，最大值，最小值，均值……\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 194 entries, 0 to 193\nData columns (total 5 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Mes 194 non-null object\n 1 data science 194 non-null int64 \n 2 machine learning 194 non-null int64 \n 3 deep learning 194 non-null int64 \n 4 categorical 194 non-null int64 \ndtypes: int64(4), object(1)\nmemory usage: 7.7+ KB\n" + ] + } + ], + "source": [ + "df.info()\n", + "# 使用info命令，我们将看到每列包含的数据类型。我们可以发现一列的情况，当使用head命令查看时，该列似乎是数字的，但是如果我们查看后续数据，则字符串格式的值将被编码为字符串。" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_rows',500)\n", + "pd.set_option('display.max_columns',500)\n", + "pd.set_option('display.width',1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Mes data science machine learning deep learning categorical\n", + "0 2004-01-01 12 18 4 1\n", + "1 2004-02-01 12 21 2 1\n", + "2 2004-03-01 9 21 2 1\n", + "3 2004-04-01 10 16 4 1\n", + "4 2004-05-01 7 14 3 1\n", + "5 2004-06-01 9 17 3 1\n", + "6 2004-07-01 9 16 3 1\n", + "7 2004-08-01 7 14 3 1\n", + "8 2004-09-01 10 17 4 1\n", + "9 2004-10-01 8 17 4 1" + ], + "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Mes</th>\n <th>data science</th>\n <th>machine learning</th>\n <th>deep learning</th>\n <th>categorical</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2004-01-01</td>\n <td>12</td>\n <td>18</td>\n <td>4</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2004-02-01</td>\n <td>12</td>\n <td>21</td>\n <td>2</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2004-03-01</td>\n <td>9</td>\n <td>21</td>\n <td>2</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2004-04-01</td>\n <td>10</td>\n <td>16</td>\n <td>4</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2004-05-01</td>\n <td>7</td>\n <td>14</td>\n <td>3</td>\n <td>1</td>\n </tr>\n <tr>\n <th>5</th>\n <td>2004-06-01</td>\n <td>9</td>\n <td>17</td>\n <td>3</td>\n <td>1</td>\n </tr>\n <tr>\n <th>6</th>\n <td>2004-07-01</td>\n <td>9</td>\n <td>16</td>\n <td>3</td>\n <td>1</td>\n </tr>\n <tr>\n <th>7</th>\n <td>2004-08-01</td>\n <td>7</td>\n <td>14</td>\n <td>3</td>\n <td>1</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2004-09-01</td>\n <td>10</td>\n <td>17</td>\n <td>4</td>\n <td>1</td>\n </tr>\n <tr>\n <th>9</th>\n <td>2004-10-01</td>\n <td>8</td>\n <td>17</td>\n <td>4</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>" + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " data science machine learning deep learning categorical\n", + "count 194.000000 194.000000 194.000000 194.000000\n", + "mean 20.953608 27.396907 24.231959 0.257732\n", + "std 23.951006 28.091490 34.476887 0.438517\n", + "min 4.000000 7.000000 1.000000 0.000000\n", + "25% 6.000000 9.000000 2.000000 0.000000\n", + "50% 8.000000 13.000000 3.000000 0.000000\n", + "75% 26.750000 31.500000 34.000000 1.000000\n", + "max 100.000000 100.000000 100.000000 1.000000" + ], + "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>data science</th>\n <th>machine learning</th>\n <th>deep learning</th>\n <th>categorical</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>194.000000</td>\n <td>194.000000</td>\n <td>194.000000</td>\n <td>194.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>20.953608</td>\n <td>27.396907</td>\n <td>24.231959</td>\n <td>0.257732</td>\n </tr>\n <tr>\n <th>std</th>\n <td>23.951006</td>\n <td>28.091490</td>\n <td>34.476887</td>\n <td>0.438517</td>\n </tr>\n <tr>\n <th>min</th>\n <td>4.000000</td>\n <td>7.000000</td>\n <td>1.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>6.000000</td>\n <td>9.000000</td>\n <td>2.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>8.000000</td>\n <td>13.000000</td>\n <td>3.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>26.750000</td>\n <td>31.500000</td>\n <td>34.000000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>100.000000</td>\n <td>100.000000</td>\n <td>100.000000</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>" + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 194 entries, 0 to 193\nData columns (total 5 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Mes 194 non-null object\n 1 data science 194 non-null int64 \n 2 machine learning 194 non-null int64 \n 3 deep learning 194 non-null int64 \n 4 categorical 194 non-null int64 \ndtypes: int64(4), object(1)\nmemory usage: 7.7+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<pandas.io.formats.style.Styler at 0x235f6fc7cd0>" + ], + "text/html": "<style type=\"text/css\" >\n</style><table id=\"T_6d960_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Mes</th> <th class=\"col_heading level0 col1\" >data science</th> <th class=\"col_heading level0 col2\" >machine learning</th> <th class=\"col_heading level0 col3\" >deep learning</th> <th class=\"col_heading level0 col4\" >categorical</th> </tr></thead><tbody>\n <tr>\n <th id=\"T_6d960_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n <td id=\"T_6d960_row0_col0\" class=\"data row0 col0\" >01-2004</td>\n <td id=\"T_6d960_row0_col1\" class=\"data row0 col1\" >$12.00</td>\n <td id=\"T_6d960_row0_col2\" class=\"data row0 col2\" >1800.00%</td>\n <td id=\"T_6d960_row0_col3\" class=\"data row0 col3\" >4</td>\n <td id=\"T_6d960_row0_col4\" class=\"data row0 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6d960_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n <td id=\"T_6d960_row1_col0\" class=\"data row1 col0\" >02-2004</td>\n <td id=\"T_6d960_row1_col1\" class=\"data row1 col1\" >$12.00</td>\n <td id=\"T_6d960_row1_col2\" class=\"data row1 col2\" >2100.00%</td>\n <td id=\"T_6d960_row1_col3\" class=\"data row1 col3\" >2</td>\n <td id=\"T_6d960_row1_col4\" class=\"data row1 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6d960_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n <td id=\"T_6d960_row2_col0\" class=\"data row2 col0\" >03-2004</td>\n <td id=\"T_6d960_row2_col1\" class=\"data row2 col1\" >$9.00</td>\n <td id=\"T_6d960_row2_col2\" class=\"data row2 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>1</td>\n </tr>\n <tr>\n <th id=\"T_6d960_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n <td id=\"T_6d960_row5_col0\" class=\"data row5 col0\" >06-2004</td>\n <td id=\"T_6d960_row5_col1\" class=\"data row5 col1\" >$9.00</td>\n <td id=\"T_6d960_row5_col2\" class=\"data row5 col2\" >1700.00%</td>\n <td id=\"T_6d960_row5_col3\" class=\"data row5 col3\" >3</td>\n <td id=\"T_6d960_row5_col4\" class=\"data row5 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6d960_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n <td id=\"T_6d960_row6_col0\" class=\"data row6 col0\" >07-2004</td>\n <td id=\"T_6d960_row6_col1\" class=\"data row6 col1\" >$9.00</td>\n <td id=\"T_6d960_row6_col2\" class=\"data row6 col2\" >1600.00%</td>\n <td id=\"T_6d960_row6_col3\" class=\"data row6 col3\" >3</td>\n <td id=\"T_6d960_row6_col4\" class=\"data row6 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6d960_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n <td id=\"T_6d960_row7_col0\" class=\"data row7 col0\" >08-2004</td>\n <td id=\"T_6d960_row7_col1\" class=\"data row7 col1\" >$7.00</td>\n <td id=\"T_6d960_row7_col2\" class=\"data row7 col2\" >1400.00%</td>\n <td id=\"T_6d960_row7_col3\" class=\"data row7 col3\" >3</td>\n <td id=\"T_6d960_row7_col4\" class=\"data row7 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6d960_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n <td id=\"T_6d960_row8_col0\" class=\"data row8 col0\" >09-2004</td>\n <td id=\"T_6d960_row8_col1\" class=\"data row8 col1\" >$10.00</td>\n <td id=\"T_6d960_row8_col2\" class=\"data row8 col2\" >1700.00%</td>\n <td id=\"T_6d960_row8_col3\" class=\"data row8 col3\" >4</td>\n <td id=\"T_6d960_row8_col4\" class=\"data row8 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6d960_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n <td id=\"T_6d960_row9_col0\" class=\"data row9 col0\" >10-2004</td>\n <td id=\"T_6d960_row9_col1\" class=\"data row9 col1\" >$8.00</td>\n <td id=\"T_6d960_row9_col2\" class=\"data row9 col2\" >1700.00%</td>\n <td id=\"T_6d960_row9_col3\" class=\"data row9 col3\" >4</td>\n <td id=\"T_6d960_row9_col4\" class=\"data row9 col4\" >1</td>\n </tr>\n </tbody></table>" + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "format_dict = {'data science':'${0:,.2f}', 'Mes':'{:%m-%Y}', 'machine learning':'{:.2%}'}\n", + "#We make sure that the Month column has datetime format\n", + "df['Mes'] = pd.to_datetime(df['Mes'])\n", + "#We apply the style to the visualization\n", + "df.head(10).style.format(format_dict)\n", + "# 为了给出每种类型的示例，我添加了货币和百分比符号，即使它们对于此数据没有任何意义。" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<pandas.io.formats.style.Styler at 0x235f6fc7fd0>" + ], + "text/html": "<style type=\"text/css\" >\n#T_817a9_row0_col1,#T_817a9_row0_col3,#T_817a9_row1_col1,#T_817a9_row1_col2,#T_817a9_row2_col2,#T_817a9_row3_col3,#T_817a9_row8_col3,#T_817a9_row9_col3{\n background-color: darkgreen;\n }#T_817a9_row0_col4,#T_817a9_row1_col4,#T_817a9_row2_col4,#T_817a9_row3_col4,#T_817a9_row4_col4,#T_817a9_row5_col4,#T_817a9_row6_col4,#T_817a9_row7_col4,#T_817a9_row8_col4,#T_817a9_row9_col4{\n background-color: darkgreen;\n background-color: #ff0000;\n }#T_817a9_row1_col3,#T_817a9_row2_col3,#T_817a9_row4_col1,#T_817a9_row4_col2,#T_817a9_row7_col1,#T_817a9_row7_col2{\n background-color: #ff0000;\n }</style><table id=\"T_817a9_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Mes</th> <th class=\"col_heading level0 col1\" >data science</th> <th class=\"col_heading level0 col2\" >machine learning</th> <th class=\"col_heading level0 col3\" >deep learning</th> <th class=\"col_heading level0 col4\" >categorical</th> </tr></thead><tbody>\n <tr>\n <th id=\"T_817a9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n <td id=\"T_817a9_row0_col0\" class=\"data row0 col0\" >01-2004</td>\n <td id=\"T_817a9_row0_col1\" class=\"data row0 col1\" >12</td>\n <td id=\"T_817a9_row0_col2\" class=\"data row0 col2\" >18</td>\n <td id=\"T_817a9_row0_col3\" class=\"data row0 col3\" >4</td>\n <td id=\"T_817a9_row0_col4\" class=\"data row0 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_817a9_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n <td id=\"T_817a9_row1_col0\" class=\"data row1 col0\" >02-2004</td>\n <td id=\"T_817a9_row1_col1\" class=\"data row1 col1\" >12</td>\n <td id=\"T_817a9_row1_col2\" class=\"data row1 col2\" >21</td>\n <td id=\"T_817a9_row1_col3\" class=\"data row1 col3\" >2</td>\n <td id=\"T_817a9_row1_col4\" class=\"data row1 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_817a9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n <td id=\"T_817a9_row2_col0\" class=\"data row2 col0\" >03-2004</td>\n <td id=\"T_817a9_row2_col1\" class=\"data row2 col1\" >9</td>\n <td id=\"T_817a9_row2_col2\" class=\"data row2 col2\" >21</td>\n <td id=\"T_817a9_row2_col3\" class=\"data row2 col3\" >2</td>\n <td id=\"T_817a9_row2_col4\" class=\"data row2 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_817a9_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n <td id=\"T_817a9_row3_col0\" class=\"data row3 col0\" >04-2004</td>\n <td id=\"T_817a9_row3_col1\" class=\"data row3 col1\" >10</td>\n <td id=\"T_817a9_row3_col2\" class=\"data row3 col2\" >16</td>\n <td id=\"T_817a9_row3_col3\" class=\"data row3 col3\" >4</td>\n <td id=\"T_817a9_row3_col4\" class=\"data row3 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_817a9_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n <td id=\"T_817a9_row4_col0\" class=\"data row4 col0\" >05-2004</td>\n <td id=\"T_817a9_row4_col1\" class=\"data row4 col1\" >7</td>\n <td id=\"T_817a9_row4_col2\" class=\"data row4 col2\" >14</td>\n <td id=\"T_817a9_row4_col3\" class=\"data row4 col3\" >3</td>\n <td id=\"T_817a9_row4_col4\" class=\"data row4 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_817a9_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n <td id=\"T_817a9_row5_col0\" class=\"data row5 col0\" >06-2004</td>\n <td id=\"T_817a9_row5_col1\" class=\"data row5 col1\" >9</td>\n <td id=\"T_817a9_row5_col2\" class=\"data row5 col2\" >17</td>\n <td id=\"T_817a9_row5_col3\" class=\"data row5 col3\" >3</td>\n <td id=\"T_817a9_row5_col4\" class=\"data row5 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_817a9_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n <td id=\"T_817a9_row6_col0\" class=\"data row6 col0\" >07-2004</td>\n <td id=\"T_817a9_row6_col1\" class=\"data row6 col1\" >9</td>\n <td id=\"T_817a9_row6_col2\" class=\"data row6 col2\" >16</td>\n <td id=\"T_817a9_row6_col3\" class=\"data row6 col3\" >3</td>\n <td id=\"T_817a9_row6_col4\" class=\"data row6 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_817a9_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n <td id=\"T_817a9_row7_col0\" class=\"data row7 col0\" >08-2004</td>\n <td id=\"T_817a9_row7_col1\" class=\"data row7 col1\" >7</td>\n <td id=\"T_817a9_row7_col2\" class=\"data row7 col2\" >14</td>\n <td id=\"T_817a9_row7_col3\" class=\"data row7 col3\" >3</td>\n <td id=\"T_817a9_row7_col4\" class=\"data row7 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_817a9_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n <td id=\"T_817a9_row8_col0\" class=\"data row8 col0\" >09-2004</td>\n <td id=\"T_817a9_row8_col1\" class=\"data row8 col1\" >10</td>\n <td id=\"T_817a9_row8_col2\" class=\"data row8 col2\" >17</td>\n <td id=\"T_817a9_row8_col3\" class=\"data row8 col3\" >4</td>\n <td id=\"T_817a9_row8_col4\" class=\"data row8 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_817a9_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n <td id=\"T_817a9_row9_col0\" class=\"data row9 col0\" >10-2004</td>\n <td id=\"T_817a9_row9_col1\" class=\"data row9 col1\" >8</td>\n <td id=\"T_817a9_row9_col2\" class=\"data row9 col2\" >17</td>\n <td id=\"T_817a9_row9_col3\" class=\"data row9 col3\" >4</td>\n <td id=\"T_817a9_row9_col4\" class=\"data row9 col4\" >1</td>\n </tr>\n </tbody></table>" + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "format_dict = {'Mes':'{:%m-%Y}'} #Simplified format dictionary with values that do make sense for our data\n", + "df.head(10).style.format(format_dict).highlight_max(color='darkgreen').highlight_min(color='#ff0000')\n", + "# 我们可以用颜色突出显示最大值和最小值。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<pandas.io.formats.style.Styler at 0x235f6fc7790>" + ], + "text/html": "<style type=\"text/css\" >\n#T_6ad44_row0_col1,#T_6ad44_row1_col1,#T_6ad44_row1_col2,#T_6ad44_row2_col2{\n background-color: #00441b;\n color: #f1f1f1;\n }#T_6ad44_row0_col2{\n background-color: #51b689;\n color: #000000;\n }#T_6ad44_row2_col1,#T_6ad44_row5_col1,#T_6ad44_row6_col1{\n background-color: #8fd4c2;\n color: #000000;\n }#T_6ad44_row3_col1,#T_6ad44_row8_col1{\n background-color: #48b27f;\n color: #000000;\n }#T_6ad44_row3_col2,#T_6ad44_row6_col2{\n background-color: #bde6de;\n color: #000000;\n }#T_6ad44_row4_col1,#T_6ad44_row4_col2,#T_6ad44_row7_col1,#T_6ad44_row7_col2{\n background-color: #f7fcfd;\n color: #000000;\n }#T_6ad44_row5_col2,#T_6ad44_row8_col2,#T_6ad44_row9_col2{\n background-color: #84cfb9;\n color: #000000;\n }#T_6ad44_row9_col1{\n background-color: #d6f0ee;\n color: #000000;\n }</style><table id=\"T_6ad44_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Mes</th> <th class=\"col_heading level0 col1\" >data science</th> <th class=\"col_heading level0 col2\" >machine learning</th> <th class=\"col_heading level0 col3\" >deep learning</th> <th class=\"col_heading level0 col4\" >categorical</th> </tr></thead><tbody>\n <tr>\n <th id=\"T_6ad44_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n <td id=\"T_6ad44_row0_col0\" class=\"data row0 col0\" >01-2004</td>\n <td id=\"T_6ad44_row0_col1\" class=\"data row0 col1\" >12</td>\n <td id=\"T_6ad44_row0_col2\" class=\"data row0 col2\" >18</td>\n <td id=\"T_6ad44_row0_col3\" class=\"data row0 col3\" >4</td>\n <td id=\"T_6ad44_row0_col4\" class=\"data row0 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6ad44_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n <td id=\"T_6ad44_row1_col0\" class=\"data row1 col0\" >02-2004</td>\n <td id=\"T_6ad44_row1_col1\" class=\"data row1 col1\" >12</td>\n <td id=\"T_6ad44_row1_col2\" class=\"data row1 col2\" >21</td>\n <td id=\"T_6ad44_row1_col3\" class=\"data row1 col3\" >2</td>\n <td id=\"T_6ad44_row1_col4\" class=\"data row1 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6ad44_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n <td id=\"T_6ad44_row2_col0\" class=\"data row2 col0\" >03-2004</td>\n <td id=\"T_6ad44_row2_col1\" class=\"data row2 col1\" >9</td>\n <td id=\"T_6ad44_row2_col2\" class=\"data row2 col2\" >21</td>\n <td id=\"T_6ad44_row2_col3\" class=\"data row2 col3\" >2</td>\n <td id=\"T_6ad44_row2_col4\" class=\"data row2 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6ad44_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n <td id=\"T_6ad44_row3_col0\" class=\"data row3 col0\" >04-2004</td>\n <td id=\"T_6ad44_row3_col1\" class=\"data row3 col1\" >10</td>\n <td id=\"T_6ad44_row3_col2\" class=\"data row3 col2\" >16</td>\n <td id=\"T_6ad44_row3_col3\" class=\"data row3 col3\" >4</td>\n <td id=\"T_6ad44_row3_col4\" class=\"data row3 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6ad44_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n <td id=\"T_6ad44_row4_col0\" class=\"data row4 col0\" >05-2004</td>\n <td id=\"T_6ad44_row4_col1\" class=\"data row4 col1\" >7</td>\n <td id=\"T_6ad44_row4_col2\" class=\"data row4 col2\" >14</td>\n <td id=\"T_6ad44_row4_col3\" class=\"data row4 col3\" >3</td>\n <td id=\"T_6ad44_row4_col4\" class=\"data row4 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6ad44_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n <td id=\"T_6ad44_row5_col0\" class=\"data row5 col0\" >06-2004</td>\n <td id=\"T_6ad44_row5_col1\" class=\"data row5 col1\" >9</td>\n <td id=\"T_6ad44_row5_col2\" class=\"data row5 col2\" >17</td>\n <td id=\"T_6ad44_row5_col3\" class=\"data row5 col3\" >3</td>\n <td id=\"T_6ad44_row5_col4\" class=\"data row5 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6ad44_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n <td id=\"T_6ad44_row6_col0\" class=\"data row6 col0\" >07-2004</td>\n <td id=\"T_6ad44_row6_col1\" class=\"data row6 col1\" >9</td>\n <td id=\"T_6ad44_row6_col2\" class=\"data row6 col2\" >16</td>\n <td id=\"T_6ad44_row6_col3\" class=\"data row6 col3\" >3</td>\n <td id=\"T_6ad44_row6_col4\" class=\"data row6 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6ad44_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n <td id=\"T_6ad44_row7_col0\" class=\"data row7 col0\" >08-2004</td>\n <td id=\"T_6ad44_row7_col1\" class=\"data row7 col1\" >7</td>\n <td id=\"T_6ad44_row7_col2\" class=\"data row7 col2\" >14</td>\n <td id=\"T_6ad44_row7_col3\" class=\"data row7 col3\" >3</td>\n <td id=\"T_6ad44_row7_col4\" class=\"data row7 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6ad44_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n <td id=\"T_6ad44_row8_col0\" class=\"data row8 col0\" >09-2004</td>\n <td id=\"T_6ad44_row8_col1\" class=\"data row8 col1\" >10</td>\n <td id=\"T_6ad44_row8_col2\" class=\"data row8 col2\" >17</td>\n <td id=\"T_6ad44_row8_col3\" class=\"data row8 col3\" >4</td>\n <td id=\"T_6ad44_row8_col4\" class=\"data row8 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_6ad44_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n <td id=\"T_6ad44_row9_col0\" class=\"data row9 col0\" >10-2004</td>\n <td id=\"T_6ad44_row9_col1\" class=\"data row9 col1\" >8</td>\n <td id=\"T_6ad44_row9_col2\" class=\"data row9 col2\" >17</td>\n <td id=\"T_6ad44_row9_col3\" class=\"data row9 col3\" >4</td>\n <td id=\"T_6ad44_row9_col4\" class=\"data row9 col4\" >1</td>\n </tr>\n </tbody></table>" + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "df.head(10).style.format(format_dict).background_gradient(subset=['data science', 'machine learning'], cmap='BuGn')\n", + "# 我们使用颜色渐变来显示数据值。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<pandas.io.formats.style.Styler at 0x235f80739d0>" + ], + "text/html": "<style type=\"text/css\" >\n#T_b2968_row0_col1,#T_b2968_row0_col3,#T_b2968_row1_col1,#T_b2968_row3_col3,#T_b2968_row8_col3,#T_b2968_row9_col3{\n width: 10em;\n height: 80%;\n background: linear-gradient(90deg,red 100.0%, transparent 100.0%);\n }#T_b2968_row1_col3,#T_b2968_row2_col3,#T_b2968_row4_col1,#T_b2968_row7_col1{\n width: 10em;\n height: 80%;\n }#T_b2968_row2_col1,#T_b2968_row5_col1,#T_b2968_row6_col1{\n width: 10em;\n height: 80%;\n background: linear-gradient(90deg,red 40.0%, transparent 40.0%);\n }#T_b2968_row3_col1,#T_b2968_row8_col1{\n width: 10em;\n height: 80%;\n background: linear-gradient(90deg,red 60.0%, transparent 60.0%);\n }#T_b2968_row4_col3,#T_b2968_row5_col3,#T_b2968_row6_col3,#T_b2968_row7_col3{\n width: 10em;\n height: 80%;\n background: linear-gradient(90deg,red 50.0%, transparent 50.0%);\n }#T_b2968_row9_col1{\n width: 10em;\n height: 80%;\n background: linear-gradient(90deg,red 20.0%, transparent 20.0%);\n }</style><table id=\"T_b2968_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Mes</th> <th class=\"col_heading level0 col1\" >data science</th> <th class=\"col_heading level0 col2\" >machine learning</th> <th class=\"col_heading level0 col3\" >deep learning</th> <th class=\"col_heading level0 col4\" >categorical</th> </tr></thead><tbody>\n <tr>\n <th id=\"T_b2968_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n <td id=\"T_b2968_row0_col0\" class=\"data row0 col0\" >01-2004</td>\n <td id=\"T_b2968_row0_col1\" class=\"data row0 col1\" >12</td>\n <td id=\"T_b2968_row0_col2\" class=\"data row0 col2\" >18</td>\n <td id=\"T_b2968_row0_col3\" class=\"data row0 col3\" >4</td>\n <td id=\"T_b2968_row0_col4\" class=\"data row0 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_b2968_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n <td id=\"T_b2968_row1_col0\" class=\"data row1 col0\" >02-2004</td>\n <td id=\"T_b2968_row1_col1\" class=\"data row1 col1\" >12</td>\n <td id=\"T_b2968_row1_col2\" class=\"data row1 col2\" >21</td>\n <td id=\"T_b2968_row1_col3\" class=\"data row1 col3\" >2</td>\n <td id=\"T_b2968_row1_col4\" class=\"data row1 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_b2968_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n <td id=\"T_b2968_row2_col0\" class=\"data row2 col0\" >03-2004</td>\n <td id=\"T_b2968_row2_col1\" class=\"data row2 col1\" >9</td>\n <td id=\"T_b2968_row2_col2\" class=\"data row2 col2\" >21</td>\n <td id=\"T_b2968_row2_col3\" class=\"data row2 col3\" >2</td>\n <td id=\"T_b2968_row2_col4\" class=\"data row2 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_b2968_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n <td id=\"T_b2968_row3_col0\" class=\"data row3 col0\" >04-2004</td>\n <td id=\"T_b2968_row3_col1\" class=\"data row3 col1\" >10</td>\n <td id=\"T_b2968_row3_col2\" class=\"data row3 col2\" >16</td>\n <td id=\"T_b2968_row3_col3\" class=\"data row3 col3\" >4</td>\n <td id=\"T_b2968_row3_col4\" class=\"data row3 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_b2968_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n <td id=\"T_b2968_row4_col0\" class=\"data row4 col0\" >05-2004</td>\n <td id=\"T_b2968_row4_col1\" class=\"data row4 col1\" >7</td>\n <td id=\"T_b2968_row4_col2\" class=\"data row4 col2\" >14</td>\n <td id=\"T_b2968_row4_col3\" class=\"data row4 col3\" >3</td>\n <td id=\"T_b2968_row4_col4\" class=\"data row4 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_b2968_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n <td id=\"T_b2968_row5_col0\" class=\"data row5 col0\" >06-2004</td>\n <td id=\"T_b2968_row5_col1\" class=\"data row5 col1\" >9</td>\n <td id=\"T_b2968_row5_col2\" class=\"data row5 col2\" >17</td>\n <td id=\"T_b2968_row5_col3\" class=\"data row5 col3\" >3</td>\n <td id=\"T_b2968_row5_col4\" class=\"data row5 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_b2968_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n <td id=\"T_b2968_row6_col0\" class=\"data row6 col0\" >07-2004</td>\n <td id=\"T_b2968_row6_col1\" class=\"data row6 col1\" >9</td>\n <td id=\"T_b2968_row6_col2\" class=\"data row6 col2\" >16</td>\n <td id=\"T_b2968_row6_col3\" class=\"data row6 col3\" >3</td>\n <td id=\"T_b2968_row6_col4\" class=\"data row6 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_b2968_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n <td id=\"T_b2968_row7_col0\" class=\"data row7 col0\" >08-2004</td>\n <td id=\"T_b2968_row7_col1\" class=\"data row7 col1\" >7</td>\n <td id=\"T_b2968_row7_col2\" class=\"data row7 col2\" >14</td>\n <td id=\"T_b2968_row7_col3\" class=\"data row7 col3\" >3</td>\n <td id=\"T_b2968_row7_col4\" class=\"data row7 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_b2968_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n <td id=\"T_b2968_row8_col0\" class=\"data row8 col0\" >09-2004</td>\n <td id=\"T_b2968_row8_col1\" class=\"data row8 col1\" >10</td>\n <td id=\"T_b2968_row8_col2\" class=\"data row8 col2\" >17</td>\n <td id=\"T_b2968_row8_col3\" class=\"data row8 col3\" >4</td>\n <td id=\"T_b2968_row8_col4\" class=\"data row8 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_b2968_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n <td id=\"T_b2968_row9_col0\" class=\"data row9 col0\" >10-2004</td>\n <td id=\"T_b2968_row9_col1\" class=\"data row9 col1\" >8</td>\n <td id=\"T_b2968_row9_col2\" class=\"data row9 col2\" >17</td>\n <td id=\"T_b2968_row9_col3\" class=\"data row9 col3\" >4</td>\n <td id=\"T_b2968_row9_col4\" class=\"data row9 col4\" >1</td>\n </tr>\n </tbody></table>" + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "df.head(10).style.format(format_dict).bar(color='red', subset=['data science', 'deep learning'])\n", + "# 我们也可以用条形显示数据值。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<pandas.io.formats.style.Styler at 0x235f8073af0>" + ], + "text/html": "<style type=\"text/css\" >\n#T_0b465_row0_col1,#T_0b465_row1_col1,#T_0b465_row1_col2,#T_0b465_row2_col2{\n background-color: #00441b;\n color: #f1f1f1;\n background-color: yellow;\n }#T_0b465_row0_col2{\n background-color: #51b689;\n color: #000000;\n }#T_0b465_row0_col3,#T_0b465_row0_col4,#T_0b465_row1_col4,#T_0b465_row2_col4,#T_0b465_row3_col3,#T_0b465_row3_col4,#T_0b465_row4_col4,#T_0b465_row5_col4,#T_0b465_row6_col4,#T_0b465_row7_col4,#T_0b465_row8_col3,#T_0b465_row8_col4,#T_0b465_row9_col3,#T_0b465_row9_col4{\n background-color: yellow;\n }#T_0b465_row2_col1,#T_0b465_row5_col1,#T_0b465_row6_col1{\n background-color: #8fd4c2;\n color: #000000;\n }#T_0b465_row3_col1,#T_0b465_row8_col1{\n background-color: #48b27f;\n color: #000000;\n }#T_0b465_row3_col2,#T_0b465_row6_col2{\n background-color: #bde6de;\n color: #000000;\n }#T_0b465_row4_col1,#T_0b465_row4_col2,#T_0b465_row7_col1,#T_0b465_row7_col2{\n background-color: #f7fcfd;\n color: #000000;\n }#T_0b465_row5_col2,#T_0b465_row8_col2,#T_0b465_row9_col2{\n background-color: #84cfb9;\n color: #000000;\n }#T_0b465_row9_col1{\n background-color: #d6f0ee;\n color: #000000;\n }</style><table id=\"T_0b465_\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Mes</th> <th class=\"col_heading level0 col1\" >data science</th> <th class=\"col_heading level0 col2\" >machine learning</th> <th class=\"col_heading level0 col3\" >deep learning</th> <th class=\"col_heading level0 col4\" >categorical</th> </tr></thead><tbody>\n <tr>\n <th id=\"T_0b465_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n <td id=\"T_0b465_row0_col0\" class=\"data row0 col0\" >01-2004</td>\n <td id=\"T_0b465_row0_col1\" class=\"data row0 col1\" >12</td>\n <td id=\"T_0b465_row0_col2\" class=\"data row0 col2\" >18</td>\n <td id=\"T_0b465_row0_col3\" class=\"data row0 col3\" >4</td>\n <td id=\"T_0b465_row0_col4\" class=\"data row0 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_0b465_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n <td id=\"T_0b465_row1_col0\" class=\"data row1 col0\" >02-2004</td>\n <td id=\"T_0b465_row1_col1\" class=\"data row1 col1\" >12</td>\n <td id=\"T_0b465_row1_col2\" class=\"data row1 col2\" >21</td>\n <td id=\"T_0b465_row1_col3\" class=\"data row1 col3\" >2</td>\n <td id=\"T_0b465_row1_col4\" class=\"data row1 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_0b465_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n <td id=\"T_0b465_row2_col0\" class=\"data row2 col0\" >03-2004</td>\n <td id=\"T_0b465_row2_col1\" class=\"data row2 col1\" >9</td>\n <td id=\"T_0b465_row2_col2\" class=\"data row2 col2\" >21</td>\n <td id=\"T_0b465_row2_col3\" class=\"data row2 col3\" >2</td>\n <td id=\"T_0b465_row2_col4\" class=\"data row2 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_0b465_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n <td id=\"T_0b465_row3_col0\" class=\"data row3 col0\" >04-2004</td>\n <td id=\"T_0b465_row3_col1\" class=\"data row3 col1\" >10</td>\n <td id=\"T_0b465_row3_col2\" class=\"data row3 col2\" >16</td>\n <td id=\"T_0b465_row3_col3\" class=\"data row3 col3\" >4</td>\n <td id=\"T_0b465_row3_col4\" class=\"data row3 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_0b465_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n <td id=\"T_0b465_row4_col0\" class=\"data row4 col0\" >05-2004</td>\n <td id=\"T_0b465_row4_col1\" class=\"data row4 col1\" >7</td>\n <td id=\"T_0b465_row4_col2\" class=\"data row4 col2\" >14</td>\n <td id=\"T_0b465_row4_col3\" class=\"data row4 col3\" >3</td>\n <td id=\"T_0b465_row4_col4\" class=\"data row4 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_0b465_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n <td id=\"T_0b465_row5_col0\" class=\"data row5 col0\" >06-2004</td>\n <td id=\"T_0b465_row5_col1\" class=\"data row5 col1\" >9</td>\n <td id=\"T_0b465_row5_col2\" class=\"data row5 col2\" >17</td>\n <td id=\"T_0b465_row5_col3\" class=\"data row5 col3\" >3</td>\n <td id=\"T_0b465_row5_col4\" class=\"data row5 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_0b465_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n <td id=\"T_0b465_row6_col0\" class=\"data row6 col0\" >07-2004</td>\n <td id=\"T_0b465_row6_col1\" class=\"data row6 col1\" >9</td>\n <td id=\"T_0b465_row6_col2\" class=\"data row6 col2\" >16</td>\n <td id=\"T_0b465_row6_col3\" class=\"data row6 col3\" >3</td>\n <td id=\"T_0b465_row6_col4\" class=\"data row6 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_0b465_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n <td id=\"T_0b465_row7_col0\" class=\"data row7 col0\" >08-2004</td>\n <td id=\"T_0b465_row7_col1\" class=\"data row7 col1\" >7</td>\n <td id=\"T_0b465_row7_col2\" class=\"data row7 col2\" >14</td>\n <td id=\"T_0b465_row7_col3\" class=\"data row7 col3\" >3</td>\n <td id=\"T_0b465_row7_col4\" class=\"data row7 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_0b465_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n <td id=\"T_0b465_row8_col0\" class=\"data row8 col0\" >09-2004</td>\n <td id=\"T_0b465_row8_col1\" class=\"data row8 col1\" >10</td>\n <td id=\"T_0b465_row8_col2\" class=\"data row8 col2\" >17</td>\n <td id=\"T_0b465_row8_col3\" class=\"data row8 col3\" >4</td>\n <td id=\"T_0b465_row8_col4\" class=\"data row8 col4\" >1</td>\n </tr>\n <tr>\n <th id=\"T_0b465_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n <td id=\"T_0b465_row9_col0\" class=\"data row9 col0\" >10-2004</td>\n <td id=\"T_0b465_row9_col1\" class=\"data row9 col1\" >8</td>\n <td id=\"T_0b465_row9_col2\" class=\"data row9 col2\" >17</td>\n <td id=\"T_0b465_row9_col3\" class=\"data row9 col3\" >4</td>\n <td id=\"T_0b465_row9_col4\" class=\"data row9 col4\" >1</td>\n </tr>\n </tbody></table>" + }, + "metadata": {}, + "execution_count": 12 + } + ], + "source": [ + "df.head(10).style.format(format_dict).background_gradient(subset = ['data science','machine learning'],cmap ='BuGn').highlight_max(color ='yellow')\n", + "\n", + "# 此外，我们还可以结合以上功能并生成更复杂的可视化效果。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Summarize dataset: 100%|██████████| 18/18 [00:02<00:00, 6.09it/s, Completed]\n", + "Generate report structure: 100%|██████████| 1/1 [00:02<00:00, 2.53s/it]\n", + "Render HTML: 100%|██████████| 1/1 [00:00<00:00, 2.24it/s]\n", + "Export report to file: 100%|██████████| 1/1 [00:00<00:00, 143.19it/s]\n" + ] + } + ], + "source": [ + "from pandas_profiling import ProfileReport\n", + "prof = ProfileReport(df)\n", + "prof.to_file(output_file='report.html')\n", + "# Pandas分析是一个库，可使用我们的数据生成交互式报告，我们可以看到数据的分布，数据的类型以及可能出现的问题。它非常易于使用，只需三行，我们就可以生成一个报告，该报告可以发送给任何人，即使您不了解编程也可以使用。" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x23589b4b3d0>]" + ] + }, + "metadata": {}, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 432x288 with 1 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# Matplotlib是用于以图形方式可视化数据的最基本的库。它包含许多我们可以想到的图形。仅仅因为它是基本的并不意味着它并不强大，我们将要讨论的许多其他数据可视化库都基于它。\n", + "# Matplotlib的图表由两个主要部分组成，即轴（界定图表区域的线）和图形（我们在其中绘制轴，标题和来自轴区域的东西），现在让我们创建最简单的图：\n", + "import matplotlib.pyplot as plt\n", + "plt.plot(df['Mes'], df['data science'], label='data science') \n", + "# The parameter label is to indicate the legend. This doesn't mean that it will be shown, we'll have to use another command that I'll explain later." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x23589c3c9a0>]" + ] + }, + "metadata": {}, + "execution_count": 15 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 432x288 with 1 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 375.2875 248.518125\" width=\"375.2875pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" 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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.plot(df['Mes'],df['data science'],label ='data science')\n", + "plt.plot(df['Mes'],df['machine learning'],label ='machine learning ')\n", + "plt.plot(df['Mes'],df['deep learning'],label ='deep learning')\n", + "# 我们可以在同一张图中制作多个变量的图,然后进行比较。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<matplotlib.legend.Legend at 0x23589c6db50>" + ] + }, + "metadata": {}, + "execution_count": 16 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 432x288 with 1 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"277.314375pt\" version=\"1.1\" viewBox=\"0 0 388.965625 277.314375\" width=\"388.965625pt\" 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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.plot(df['Mes'], df['data science'], label='data science')\n", + "plt.plot(df['Mes'], df['machine learning'], label='machine learning')\n", + "plt.plot(df['Mes'], df['deep learning'], label='deep learning')\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Popularity')\n", + "plt.title('Popularity of AI terms by date')\n", + "plt.grid(True)\n", + "plt.legend()\n", + "# 每种颜色代表哪个变量还不是很清楚。我们将通过添加图例和标题来改进图表。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x23589d72fa0>]" + ] + }, + "metadata": {}, + "execution_count": 17 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 432x288 with 4 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n 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xlink:href=\"#mb172923088\" y=\"21.052066\"/>\r\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"359.003688\" xlink:href=\"#mb172923088\" y=\"23.859917\"/>\r\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"359.710141\" xlink:href=\"#mb172923088\" y=\"29.47562\"/>\r\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"360.440143\" xlink:href=\"#mb172923088\" y=\"17.308264\"/>\r\n <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"361.170145\" xlink:href=\"#mb172923088\" y=\"11.692562\"/>\r\n </g>\r\n </g>\r\n <g id=\"matplotlib.axis_3\">\r\n <g id=\"xtick_6\">\r\n <g id=\"line2d_9\">\r\n <g>\r\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"222.823037\" xlink:href=\"#m24c8f2b03c\" y=\"106.036364\"/>\r\n </g>\r\n </g>\r\n <g id=\"text_9\">\r\n <!-- 2004 -->\r\n <g transform=\"translate(210.098037 120.634801)scale(0.1 -0.1)\">\r\n <defs>\r\n <path d=\"M 2419 4116 \r\nL 825 1625 \r\nL 2419 1625 \r\nL 2419 4116 \r\nz\r\nM 2253 4666 \r\nL 3047 4666 \r\nL 3047 1625 \r\nL 3713 1625 \r\nL 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-->\r\n <g transform=\"translate(278.90659 120.634801)scale(0.1 -0.1)\">\r\n <use xlink:href=\"#DejaVuSans-32\"/>\r\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-30\"/>\r\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-31\"/>\r\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-32\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_11\">\r\n <g id=\"line2d_14\">\r\n <g>\r\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"308.845502\" xlink:href=\"#m24c8f2b03c\" y=\"106.036364\"/>\r\n </g>\r\n </g>\r\n <g id=\"text_14\">\r\n <!-- 2014 -->\r\n <g transform=\"translate(296.120502 120.634801)scale(0.1 -0.1)\">\r\n <use xlink:href=\"#DejaVuSans-32\"/>\r\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-30\"/>\r\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-31\"/>\r\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-34\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_12\">\r\n <g id=\"line2d_15\">\r\n <g>\r\n <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"326.035866\" 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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "fig, axes = plt.subplots(2,2)\n", + "axes[0, 0].hist(df['data science'])\n", + "axes[0, 1].scatter(df['Mes'], df['data science'])\n", + "axes[1, 0].plot(df['Mes'], df['machine learning'])\n", + "axes[1, 1].plot(df['Mes'], df['deep learning'])\n", + "# 我们可以在一个图形中制作多个图形。这对于比较图表或通过单个图像轻松共享几种图表类型的数据非常有用。" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x23589e8c760>]" + ] + }, + "metadata": {}, + "execution_count": 18 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 432x288 with 1 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"248.518125pt\" version=\"1.1\" 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uo2otJBUTYQnCPcwsNZPbScCq0cSUcFzPAtPNbEx0gU4HRt0tM4q4skl0ycak4spxnqrG5e6Xu/uE6Jo/B/i9u4+6NZpgXD3Ac2b2lmjTyYzyuk/w2noWmGpmO0fnPJkKXvNmdhDhBeU8d/9b7Phkrvmkbh4k+QG8k/DWZSWwPPr4ALA34UbRE8DvgL2i4w24mnCH+VGgPcM5L6C0apnEYiLcZFkZbf8Z0FLtuAh36OcRLu5VwPcr/Dfcj9C6+wewOXq8W7TvA4TKgyeB/6iFuLKdp9pxpZ3z3ZReLZPk33Ey4Ub9SsI71j1rIKavA38FHgN+Doyr4O/qOmBT7NjO2LlKvuY1/YCISAOqyW4ZEREpjZK7iEgDUnIXEWlASu4iIg1IyV1EpAEpuYuINCAldxGRBvT/AYcgiVtXaQ40AAAAAElFTkSuQmCC\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.plot(df ['Mes'],df ['data science'],'r-')\n", + "plt.plot(df ['Mes'],df ['data science'] * 2,'bs')\n", + "plt.plot(df ['Mes'],df ['data science'] * 3,'g ^')\n", + "# 我们可以为每个变量的点绘制具有不同样式的图形：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<matplotlib.collections.PathCollection at 0x2358a0e6730>" + ] + }, + "metadata": {}, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 432x288 with 1 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 375.2875 248.518125\" width=\"375.2875pt\" xmlns=\"http://www.w3.org/2000/svg\" 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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.scatter(df['data science'], df['machine learning'])\n", + "# 现在让我们看一些使用Matplotlib可以做的不同图形的例子。我们从散点图开始：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<BarContainer object of 194 artists>" + ] + }, + "metadata": {}, + "execution_count": 20 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 432x288 with 1 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 375.2875 248.518125\" width=\"375.2875pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF 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nN8gluHYHrYRWChqNRgPE5sSSVZxFdE50Zdkvsb/g4+DDCN+qteO8HbyBmmmpWcVZlJSXEOgUCECAYwCpBakArD25FkOZgTmWXpATX3vg6A1wagcDvaqUQp7Rl9OZhZSVm+jhPQiAuOxo8kQezuWW2FvZt96Fn4FWChqNpsuTX5pPWpEKHldkGeUac9mWuI2ZITOxtLCsrOttr5RCRlFGZVlKYQoAfo5+APg6+pJWmEa5qZztSdvxc/Bl+B8vwPpnaw58cit8PQe+mkWv5GPYmCe9SaMrRpPkdFYhTu498TEaic2JZr+dJZ7GtktHBa0UNBqNhtjc2MrPFbOWdxh2UC7LuSb0mhp1KyyF6hlIKYaaSsHPwQ+jNJJVnEVCfgIhDn4IgKOroSQfIpfBumfgu1vAIwRsnbH+fh59y0xYYQMmWwBi0gxg50pPo4n1hadJtLaiW+lA2hKdkqrRaLo8sTlKKYzyG8We1D2Ulpey3bCd0f6j6ebSrUZdH3sfoOYEtgpLwd9RzR/wdfRV5QUpJBgSuNTdfCM3FsH3t0H0OrCwBhd/uP5bKMiAVfcxte90TkUn4OvnzNGUfGLSC0AIemDDLgHu5eW4iLozmFoLrRQ0Gk2XJy43DmsLa64OvZpdKbt49M9HyS7PZk6fObXqutq6Ym1hXSOmkFKQgq2lLe627gD4OiilEJ0TTU5JDkEVt1o7V6UQel0MN66ACreUVyjcH8EtwJKdm+gT7ExWQWlVsNnaBchjRn4xGWaF01Zo95FGo+nyxObG0t2lO+MDx+Pn6MeOpB0EWAcwqdukWnWFELXmKiQXJOPr4IvaULLKjbQnVc1VCDaaAAGTn4HgUXDNkiqFUA2TSZKSW4y/mx29vJ0qlcJIe3+6l5UxItcJO5va7VoTbSloNJouT0xODAO9BuJq68q62esA2Lx5M9YWdW936e3gXRmYBmUp+Dv6Q2EWHP4Bt+G3YmtpW7neUVBxATj7wcj56lUPGQUllJabCHC1p6DEyC+RyUgp6ePak1+PreOH8vE4WIlWvPLaaEtBo9F0aYqNxSQaEglxbfxS1D1dexKVEVU5VyGlIEXFEQ4sh9WPIqLX4evgS6JBbSAZZMgCl8AG+03OUf0FuNnT18+F3KIyTmUWgpPZHWUKwKF52zI3Gq0UNBpNl+ZU3ikksklK4YpeV5Bfls+6U+swmoykF6UrSyFDbaVJxNLKYLO7rTtOeUngGtRgv0k5RQD4u9oxtpcnAFujM5SVAcTIQG0paDQaTVuyPWk7oJaZaCwjfEcQ7BzMDyd+IL0wHZM0qThChtoqkxN/4Get5hMEOQdCbmLjlEKushQC3ewJ8XIkwNWObdEZEDSSfOde7DWF4mCtlYJGo9G0CVJKfjjxA8N8htVKPT0bQghmhc5iT+qeyjWOKpVCjwtBSnxzzUtS2PuoVNRGKIXknCLsrC1wc7BGCMH4UC+2x2RS7tmHVeN/JB03HNo4EqyVgkaj6bLsS9vHybyTzAqd1eS2V/a6Ensre17Z/QoA/lZOYEiB3pMheBR+2QkABFk5qwaNiCkcScmjp5dTZRbTuN5e5BaVcSgxl/xitW2ndh9pNBpNG7HyxEocrR25tPulTW7r7eDNy+Nfpsio4gB+RSp9FK8+4N4D38JcAIIxR4YbsBSKy8r5+2Q248yxBFBKAVRcIa+oDCsLQRtnpGqloNFouiZGk5FNpzcxpdsUHKwdmtXHlO5TeHD4g4T7huNYsdidVx9wCWBATgq9XXsxXJgXr2tAKfx9MotSo4lxoV6VZV5OtvTydiQyPoe84jJc7K0rrYi2QisFjUbTJYnKjCK/LJ/xgeNb1M8dg+5g6WVLIfMEWFiBew9wDcTHWMaPF39I92IDWNqCg9dZ+9l6IgNrS8Gonh41yrt7OhKfXURekREXu7afWqaVgkaj6ZLsSNoBwCj/Ua3TYcZxcO8JltZV8YO8RMiKU1aCxdlvt3+dyGB4N3ccbGre+Lt5OBCfVUhukbIU2hqtFDQaTZdkZ/JO+nv0x93OvXU6zDihXEdQUylkHAfvvmdtmmkoISo5jwtDa1sTQe72GEqMxGcV4qwtBY1Go2l9CssK2Z++v949k5tMca5SCr7muQ4VSiH7FGTGVCmLejiQoILSF/T0rHUu2EPFO+IyC3Cxa3tLQa99pNG0Aj/99BOrV68mLy+P+fPnY2Nj09Eiac7CntQ9GE1GRvu3klI4uRVkOYRMVMcOHmBlB6e2gakMvPudtXnFwnehPk61zgW7K6UgJe2iFLSloDnnOXjwIN27d+fDDz9sUT+33347Pj4+hIU1fVP0q666isWLF/PRRx+xfPnyZsvw+++/07dvX3r37s2rr75ab7233nqLgQMHEhYWxvXXX09xcXG9dTW1OZp1FICh3kNbp8OYTWDtAEEj1bEQ4BKglAWA99kthZh0Ax6ONrg71n6YCPao2nrTxV67jzSaBhk0aBDLli3jiy++aFE/t956K7///nuz2paXlwPw4osvcu+99za7j3vvvZc1a9YQFRXFt99+S1RUVK16iYmJLFy4kD179nDo0CHKy8tZtmxZs8bsqqQVpuFq69r0VNTCLNi9GIylNctjN0P3cWBlW1XmEgil1eYunIWYtAJ6eTvWec7Zzhp38yp42n2k0TQSHx8fDh8+3KI+JkyYwMmTJxtdf86cOXh4eBAZGcmMGTMwGAxMmzaN4cOHs3nz5iaPv3v3bnr37k1IiFqY7brrruPnn39mwIDaa/IYjUaKioqwtramsLCQgICAJo/XlUkvTK/ca7nRlBvh+1sgbotyDQ2/WZXnJqh01BG31axfEVdwCQRb57N2HZNu4JIB9W+eE+zhQHZhrgo0lzVN7KailYLmvODJJ5+kpKSEU6dO0b179xrnLrzwQvLz82u1ef3115kyZUqzxzx48CDXXnstO3fuZOHChfz000/k5uYSHR1Nv341fciNkSExMZHg4ODKc0FBQezatatWm8DAQB577DG6deuGvb09l156KZde2vQZuV2Z9KImKIWsWNj9CWTFKIVg6wIRn1UphZiN6j3kjA15XM1KoQErIaewlMyCUnp5144nVBDs7sCBhFyVkqqVgkZzdtasWUNBQQEzZszg8OHDtZTCX3/91epjFhcXk5WVxX/+8x8AHnjgAR544IHK82daCq0pQ3Z2Nj///DNxcXG4ubkxZ84cvvrqK2666aZWG+N8J70onZ6uPRtX+c/X1D4Jts4w7kFw9offn4TkA+DoBRtfVDd+n/4127mYrbcG0lFj0gsA6OVTt/sIIMgcV3Cxs4a8xondXDpEKQghHgbuACRwELgN8AeWAZ5ABHCzlLK03k40GqC0tJQnnniCVatW8dlnn3Ho0CGmT59eo05bWAqHDx9m1KhRWFk17l+oMTIEBgYSHx9feS4hIYHAwNqLqK1fv56ePXvi7a2edGfNmsX27du1UmgkJmkiozADHwefhiuXFkDUKhh2E1zxrioryob1z8LqR6C0EEoMcPNPKrhcHZfGWQoVmUchXme3FABc7K0pbFjqFtHuSkEIEQg8AAyQUhYJIb4DrgOmA29JKZcJIT4C5gMtSyfRnPd8+eWXzJs3jx49ejBo0CBWrVpVq05Ln9InT57MF198UeMGffDgQQYPHtzoPhojw8iRIzlx4gRxcXEEBgaybNkyvvnmm1r1unXrxs6dOyksLMTe3p4NGzYwYsSIRsvS1ckuzsYojXjZn33ZCQCOroayAhh8XVWZvTuMuhsiloK1PVyzuGp+QnUChoPvoKo01XqISTdgY2lBkLt9vXXG9PJkYIALvX2cOHCqYbFbQkdlH1kB9kIIK8ABSAYuBlaYz38OXNUxomnOFY4dO8aePXt46KGHAJWFdOjQoWb3d/311zNmzBiOHTtGUFAQS5YswWQyER0djYdHzfVomqoUGoOVlRXvvfceU6dOpX///lx77bUMHDiw8vz06dNJSkpi1KhRzJ49m+HDhzNo0CBMJhMLFixoVVnOJ8rKy1hycAkGcyZQelE6QG1LIf5vOP5HzbLIZeAaDN3G1Cy/5Dl48hQ8ehT6zah7YGdfuGcrePY6q3wxaQX08HLAyrL+23EvbydWP3AhHnWkrLY2QkrZ5oPUGlSIB4GXgCLgD+BBYKeUsrf5fDCwRkpZK2FcCLEAWADg6+sb3txUPIPBgJNT/eZaR6HlahptLVdcXBy//fZbk9NMu+r31RzaWqbDRYf5KO0jLne7nEtdL608fsTvEXraVsUVhuz/F7YlmewepRwUBXk5TNs3n8TAGcT0vr3N5Hv8z0J6uFpw71C7RtVvje9r0qRJEVLKus1LKWW7vgB3YCPgDVgDPwE3AdHV6gQDhxrqKzw8XDaXTZs2NbttW6LlahparqbRGeVqa5k+2v+RDFsaJqeumCrLTeVy5fGVMmxpmEzMT6xZ8X+hUr7oX3n496pPpHzGRcqDK9tMtixDiez+xK/yw83RjW7TGt8XsEfWc1/tCPfRFCBOSpkupSwDfgDGAW5mdxJAEJDYAbJpNJrzjIrZy4mGRHYl7yKtMA2gZkyhKBsMqSp+UKISApzzo9W5gKFtJtuhJLXm0aBA1zYbo6l0hFI4DYwWQjgItVvEZCAK2ATMNte5Bfi5A2TTaDTnGUeyjjApeBKutq6sPLGS9MJ03GzdsLGs5p9PP171OT8VAOf8GLBzVcthtxEVC+GFBXRhpSCl3IUKKO9FpaNaAIuAJ4BHhBDRqLTUJe0tm0ajOb/ILckl0ZDIUJ+hXB5yORtOb+B49nG8Hc6YuJZxrOqzoUIpRIP/0Nqppq3IocRcuns64OrQ9stXNJYOyT6SUj4jpewnpQyTUt4spSyRUsZKKS+QUvaWUs6RUpZ0hGwajeb84UjWEQD6e/Rndp/ZGE1G9qfvx8f+jMyj9OpKIQWMJTgWnIKAYa0ih5SSL3eeIj5LzTL4YW8CUUl5HEjIJawTuY5AL4in0WjOY45kVimFXm69KldFrW0pHAcX8x7K+amQehgLaWy1eML7m6L590+HeGXNEeIyCnjku0huWrKLxJwiBncypaCXudBoNOcthzMP4+/oj5udGwCz+8xmf/r+2usepR+F4AvgaJqyFJL3q/JWsBT+PJ7OG+uO42xrxfojabjYWWNlISguUyvrdqYgM2hLQaPRnKfsSNrBulPrGBc4rrLs0h6XMtxnOCP9RlZVLC2EnHi1EY6Tr7IUkvZRZuUEbt3r6Llp/Lw/EXcHGz65ZQSlRhPL/o7nkgG+vHntUAYHuTIk2K3FY7Qm2lLQaDTnHSkFKTy+5XFCXEN4fMTjleX2VvZ8Pu3zmpUzTwBSbYTj5KsshcIs8p1749EKQWZDsREfZ1su6OlBiLcjsekFXH9BNyb08eayML8W99/aaEtBo9Gcd2w8vZHcklz+N+F/DW+kk2reyMi7Pzj7Qc5pSIsi37l3q8hiKDHiZGuFEIK7L+rFxL7ejO/diHWXOghtKWg0mvOO5IJkbCxsCHELaUTlSLWVpleoshSO/gpAvvPZ1yxqLIYSI+4Oak7EtSOCuXZEcAMtOhZtKWg0mvOOJEMS/k7+WIhG3OKSI8E3DCwslaVgptUshWIjTnbnzvO3Vgoajea8I7kgGX9H/4YrmkyQcgD8h6hjJ/OWmPYelNg2cbvOejCUGHG21UpBo9FoOowkQxIBTo3Ytzo7DkoNVUqhwlIIGNZqM5krYgrnClopaDSa84qS8hIyizMbZylUzEc401JopUlr5SZJYWk5jlopaDQaTceQbEgGaJylkBwJljZqjgKoYHPwaOh/eavIUlBqBMBZxxQ0Go2mY0gqSAJopKUQCT79wcq8YqqNI8xf22prHhmKlVLQ7iONRqPpIJpkKaQfB5869lduJQwlSilo95FGo9F0EEkFSVgIi9p7MJ9JuVHNXnZtvXkDaXnFvLvhBMZyE1ClFHRKqkaj0XQQyYZkfBx8sLZoYI+C/GSQJnANbLWxP9gcwxvrjvP3yWygyn2kU1I1Go2mg0gqSCLAsRGuo9wE9V6xZHYLKS4r54e9qs9t0RmAthQ0Go2mQ5FSEpcbR7BzI1xCeeZt4F1bRymsPZxCXrERZzsr/jpDKTjaaKWg0Wg07U5qYSpZxVkM9BrYcOXcePXeSu6j5X/HE+xhz61je3AwIYfcwrIq95G2FDQajab9OZx5GIABno3IKMpNBDtXsHVu8bhFpeXsisvi8sEBXBjqjUnCjtgMnX2k0Wg0HcnhjMNYCkv6uvdtuHJeYqvFE6KS8yg3SYYGuzGsmxuONpb8dSKDghIjdtYWWFueO7fac0dSjUajaYCorCh6ufXCzsqu4cq58a3mOjqUmAvAoCBXrC0tGBLsxqGkPPLPsXWPQCsFjUZzniClJCojioGejYgngHIftVKQ+WBiLl5ONvi5KGXU3dOBhKxCtWy2VgoajUbT/qQUpJBdkt24eEJpIRRlgUvrWAoHE3IZFOiKMK+sGuTuQGZBKal5xedUOipopaDRaM4TKoLM9VoKp3fC3i/U58p01JbPZi4qLedEWj6DAl0ry4I91BagR1Pyz6l0VNDbcWo0mvOE6JxoAHq717FjmpSw+jFIPwph17RqOmpUci4mCYOC3CrLupmVQm5R2TmVjgpaKWg0mvOEhPwEfOx9sLeyr30yaS+kHlSfT++APLVoXmu4jw4mmIPM1S0F9yoZzrWYwrklrUaj0dRDgiGBIOd6AscRS8HaAcrLIG4L5MSDnVurBJoPJObi7WyLr4ttZZmHow0ONpYUlpbrmIJGo9F0BPH58XUrheI8OLhSuY2CRsKRX9Rr6A1g2cCieY3gUGLNIDOAEIJgd+VCOpcmroFWChqN5jygpLyEtMK0upXCoRVQVgDht0HIRZAVC6YyCL+1xeMWlhqJTjPUcB1VUBFsPpdWSAWtFDQazXlAokFlEwU51aEUIpaC7yAIHA49J6iy7uPAuxGznhsgKilPBZnrVAoqrnCuxRS0UtBoNOc8Cflqyepaq6Mm7VNbbobfAkJA4AjoNRkmPN4q4x5IqJrJfCYV7iMnu5a7qNqTRikFIcQbQohGThNsVH9uQogVQoijQogjQogxQggPIcQ6IcQJ87t7a42n0WjOb+LzVYppLfdRxOdgZQ+Dr1XHVjZw8w/Qa1KrjHsoMRcfZ1t8XWovq1HhPnKytWyVsdqLxloKR4BFQohdQoi7hRC11WLTeAf4XUrZDxhi7v9JYIOUMhTYYD7WaDSaBknIT8Deyh5PO8+qwhIDHPwewmap1VDbgAOJuQyuw0oACO/uzpgQTwZXm79wLtAopSCl/ERKOQ6YB/QADgghvhFCNFndmhXKBGCJue9SKWUOcCXwubna58BVTe1bo9F0TSrSUatnAHFoJZQaWiWgXBcFJUZi0g2E1RFPAJWW+u2C0QS41TFvohMjpJSNqyiEJTATuA0IBr4DxgMFUsrrGj2gEEOBRUAUykqIAB4EEqWUbuY6AsiuOD6j/QJgAYCvr2/4smXLGjt0DQwGA05OTs1q25ZouZqGlqtpdEa5WkOml5NexsvKiwU+CyrLhkc8hoWplD0j3lHxhFaW61BGOa/vKeaRcFsGe7dfMLk1vq9JkyZFSClH1HlSStngC3gLOAF8DFxwxrljjemjWv0RgBEYZT5+B3gByDmjXnZDfYWHh8vmsmnTpma3bUu0XE1Dy9U0OqNcLZXJZDLJEV+OkP/d/d+qwqRIKZ9xkXLnR60qV3JOkXxr3TFZWGKUr645Ins9tVoaisuaPUZrydVUgD2ynvtqY9XbAeBfUsqCOs5d0Mg+KkgAEqSUu8zHK1Dxg1QhhL+UMlkI4Q+kNbFfjUbTBUkqSKK4vJgeLj2qCo+tAQQMmtNq45QYy7nryz1EJuTi6WjD9ugMtaHOOZZy2hCNDTTfdKZCEEJsAJBS5jZlQCllChAvhKhIEp6MciWtAm4xl90C/NyUfjUaTdfkSOYR4IwtOE9tA78wcPBoVp+Hk3L5OboUk0lyNCWPR7+L5MbFu4hMUPsmfLbtJAcTcxnTy6s1LqFTcVYVJ4SwAxwAL3OKaIVjzgVoyUpS9wNfCyFsgFhUnMIC+E4IMR84BVzbgv41Gk0XISozCkthSah7qCowlkL8bhg+r9l9/rA3kR+jy3BffYQ/olLIKSzDy8mGJ6f1w9nOiqd/PATA2F6eDfR07tGQ3XMX8BAQAOytVp4HvNfcQaWU+1GxhTOZ3Nw+NRpN1+RI1hFC3EKwtTQvSJccCcYi6D622X1mFZQC8Om2OCwtBN/dNYbw7mrqlKHEyMurj1AuJcO6ubVU/E7HWZWClPId4B0hxP1SynfbSSaNRqNpFFJKojKjGB84vqrw1Db13kKlEOxswdAQP0aHeFQqBFDLVjw4JZScwjJsrc6tiWmNoSH30cVSyo1AohBi1pnnpZQ/tJlkGo1G0wDpRelkFWedEU/YDp6h4OTT7H6zCkpxtxW8e/2wOs8vmNCr2X13dhpyH10EbAQur+OcBLRS0Gg0HcbRrKMA9PPoV1WYvF+tb9QCsgpK6eHQ9LkN5wMNuY+eEUJYAGuklN+1k0wajUbTKKIyo4BqSqEoBwypLV4BNauglDC3rqkUGkxJlVKagP9rB1k0Go2mSexN3Utvt944Wjuqgozj6r0FSqGotJyisnKcbbRSOBvrhRCPCSGCzauZegghmpcArNFoNK1ASXkJe9P2Mtp/dFVh+jH17tWn2f1mFarMI2frrqkUGjsVb675/d5qZRIIaV1xNBqNpnHsS9tHSXkJYwLGVBVmHANLW3Dv0ex+swxmpdBFLYVGKQUpZc+2FkSj0Wiawo6kHVgJK8J9w6sK04+DZ2+waFyq6Ne7TrE9JhNXe2sevaQPnk62ZBaUAFopNIgQIgwYAFTuJiGl/KIthNJoNJqG2Jm8k8Heg6viCQDpRyEwvP5GZ/D2+hOUGk0UlhqJSy/gi/kXkF3YtS2Fxu689gzwrvk1CXgNuKIN5dJoNJp6ySnO4UjmkZquo7IiyDnd6CBzYamR9PwSFkwI4dVZg9kRm8nra4+R2cXdR40NNM9GLUGRIqW8DbUPQttsZaTRaDQNcCz7GBLJUJ+hVYUZJwDZ6CBzfFYRoLbNvCY8iBmD/FkRkUBWQSmWFgL782vx00bTWKVQZE5NNQohXFDLWgc30Eaj0WjahERDIgBBTtX2ZG5iOurprEIAupv3Uh4V4kFmQSlRyXm4O9hg0YyNec4HGqsL9wgh3IDFqJ3SDMCOthJKo9FozkZCfgKWwhI/R7+qwvRjICxUoLkRnMpUuwF0MyuFgQHK+bEzNtNc1rhdKc83Gpt99A/zx4+EEL8DLlLKA20nlkaj0dRPoiERP0c/rCyq3cIyjoF7T7CybVQf8VmFONta4eZgDUB/f2eEgOIyEx6ONkBJG0je+WloQbzhZzsnpdxb33mNRqNpKxIMCTVdR6DSUZswk/l0ViHdPB0QZjeRg40VvbydiE4z4Oloi1YKdfPGWc5J4OJWlEWj0WgaRWJ+IhcFX1RVUG6EzGjoM7XRfZzKKqSvr3ONsrAAF6LTDLg7WreWqOccDS2IN6m9BNFoNJrGUGQsIrM4k0Cnaps/ZseBqazRloLJJEnIKuKS/r41ysMCXflpfxIejo1zQZ2PNCqmIISoc187PXlNo9G0N0mGJICaSqFyzaPGKYXU/GJKy00Em4PMFVQEmz0crKGs5bKeizQ2+2hktc92qDkLewGtFDQaTbtSkY5aQylkVCiF0Eb1cSrTnI7qWVMpDA12Y+pAX8b19iLxyKmWC3sO0tjso/urH5vTU5e1hUAajUZzNhLyEwAIcq4WaE4/Di6BYOfSqD4q5ih0O8NSsLex5OOb1fbxiUdaQdhzkMZOXjuTAkAvkqfRaNqdBEMCdpZ2eNp5VhVmHGvSctlxGQVYWQgC3OzbQMJzm8bGFH6haiaHJdAf0DuxaTSaNuVw5mG2JW5jweAFlWWJ+YkEOgWqVNLYzbB7MaRGwYjbGt1vdJqBHl6OWFs297n4/KWxMYXXq302AqeklAltII9Go9FU8u2Rb/k55mdu7H9j5Wqop/NPE+xiXmVn50cQ96fKOupf11bydROTZqCvn3PDFbsgjVKTUso/gWOoRfA8UIpBo9Fo2pSoLLUHc0UcwWgycjLvJCGu5v29kvdDv5lw91/QY3yj+iwxlnMqq5DePk5tIfI5T2OXzr4D2A3MQq2YulMIcXtbCqbRaLo2RcYiYnJigKqMo/j8eIwmo1IK+SmQnwwBw5rU78mMQspNUiuFemis++hxYJiUMhNACOEJbAc+bSvBNBpN1yKrOItXd79KkbGIUX6jCPMKwyRNQJVSiM2NBVBKIWm/ahgwtFZf8VmFfPJXLE9N74+ddc1d2KLTDAD08tZKoS4aG2XJBPKrHeebyzQajaZV2Je6jzVxa4hMi+SNiDfYlrQNAEthWakU4nLjAOjp2hOS9gEC/AbX6mvRllg+33GK3w4m1zoXnWZACK0U6qOxlkI0sEsI8TMqC+lK4IAQ4hEAKeWbbSSfRqPpImSXZAPw0viX+MeGf/D54c/xsPPAy96rMqYQmxOLr4MvTjZOKp7g1Qdsa97cS40mfjmgZj1/s+s0E/p488pvR8ktKmVQoBvR6QYC3eyxt2ncPs5djcYqhRjzq4Kfze86fK/RaFqFnJIcAEb4jWCw12AOZBxghO8IrCysiM+PByAmN6YqyJy0D0JqL8+2+VgaOYVljO/txdboDG76ZBexGQV083Bg/ZE0rC0F43p7tddlnXM0dkbzcwBCCCfzsaEthdJoNF2P3JJc7CztsLey5+rQqzmQcYABngMoKCtgZ/JOTNJEXG4cs0JnQV4yGFLrjCd8tyceLycb3pw7hPGvbuJoSj5vzBnCrOGB3P1VBGsPp9Jbu47qpbGT18KAL1HpqAghMoB5UsrDbSibRqPpQuSU5OBqqxakm95zOtuTtjO1x1R2p+ymyFjEkawjFBmLlKVwertqFDSyRh/f7DrN+iNpPHJJH3yc7Xh8al+MJsk14WpJjNfnDMHS4gDTBvmhqZvGuo8WAY9IKTcBCCEmorbmHNvcgYUQlsAeIFFKOVMI0RO1npInasvPm6WUpc3tX6PRnFvkFOfgZusGgIO1A29OVKHKiiDzhlMbAHPm0d9fgq0L+A+tbH8oMZdnVh3ioj7e3DtJbcl554SQGmM421nzwY3hbXwl5zaNzT5yrFAIAFLKzYBjC8d+EKi+5NR/gbeklL2BbGB+C/vXaDTnEDklObjZudUqr9hh7bNDn+Ft780AzwEQtwW6jwPLqufaP4+nU1YueXvuUCwtRHuJfd7RWKUQK4T4txCih/n1LyC2uYMKIYKAGcAn5mOB2sVthbnK58BVze1fo9Gce+SUVFkK1QlwCgBACMFbk97CoSATsmKh54Qa9VLzinG1t8bd0aY9xD1vEVLKhisJ4Q48B4xHpaT+BTwnpcxu1qBCrABeQWUvPQbcCuw0WwkIIYKBNVLKsDraLgAWAPj6+oYvW9a8FbwNBgNOTp0v2KTlahparqbRGeWqkOmp+KcY6jCUuZ5za9VZlrmMULtQwh3D8UveQL9jC/l7xDsUOPWorPPuvmJSCky8NN6hVvuWyNXZaA25Jk2aFCGlHFHnSSllvS/UhjoPAe8BdwHWZ6vfmBcwE/jA/Hki8CvgBURXqxMMHGqor/DwcNlcNm3a1Oy2bYmWq2louZpGZ5Rr06ZNstxULgd/Plgu3Luw4QYrF0j53xApy8trFF/53lZ50yc7W1WuzkhryAXskfXcVxsKNH+O2pTuL2Aaasnsh5qrncyMA64QQkw3Kx0X4B3ATQhhJaU0AkFAYgvH0Wg05wj5pfmYpAl3W/eGKyftg+ALwKKm9zs9v4QQ75aGOjUNxRQGSClvklJ+jFoIb0ID9RtESvmUlDJIStkDuA7YKKW8EdhkHgPgFqomyGk0mvOciolrFSmp9VJeBlkxaqnsaphMkrT8Ynxd7NpIwq5DQ0qhcutq8xN8W/IE8IgQIhqVlrqkjcfTaDSdhAqlUFeguQZZcWAygldNpZBdWEpZucTX2bZtBOxCNOQ+GiKEyDN/FoC9+VgAUkrZuA1R60Gq1NbN5s+xwAUt6U+j0Zyb5BTnAI1QChnH1Lt3za03U/NKALSl0AqcVSlIKfWKURqNps2ptBTqmKdQg/Sj6v2M/ZhT84sB8NFKocXoDUo1Gk2HU6/76PROWP9s1XH6cQrt/VhxKLdGtXSzpeCj3UctRisFjUbT4eSW5GIlrHCyPiP/ft0zsPUtyE9VxxnHOFLmz//WHq1IXwfUxDUAHxetFFqKVgoajabDyS7JxsXWBbW4gZm0IxC/U31OOQAmE2Sc4KjRn9S8EmLSCyqrpuYX4+5gja2V9ni3FK0UNBpNh5Nbklt7jkLEUrCwVp+T90NeApQVcrjMH4AdMRmVVVPzSnSQuZVo7CqpGo1G0+rE5cbxcdrHJJgSCHEKgm9vAGOROnl6Fwy4EpL2QnIk+A0B4IQpEIBt0Zn4uNgRGZ9Dal4x3jqe0CpopaDRaDqMTfGbOFR0iMFeg5lZbgPHf4JA89LW/kNg/EPw1xuQGAH2v1BuaU+U7E4PTwf+OpHOxqNplJabAJht3jNB0zK0+0ij0XQY8fnxOFk48fW0L5kTvRt6XQx3rFev29eA3yClHHJOw4HvOeV/GQXYc83wIApKy/F1tWVamNowx1cHmVsFbSloNJoOIyE/AU8rT4j7U8UMLn2+diV/5TbCWMROjysRAq4f1Y2o5DwemtKHYA977K0tmdLft32FP0/RSkGj0XQYCfkJ+Fr5QuS3YOcKfWfUrmSOJeA3mP3lPfF2ysDLyZYPb6raQe3NuUPbR+AugHYfaTSaDqHMVEZyQTJeVl4Q+yeETgXrOjKIHD1h1D0w5VmSckvwd7Nvf2G7EFopaDSaDiGlIIVyWY4vtmBIgcDh9Vee9ir0nkxSbhGBbjr1tC3RSkGj0XQICfkJAHQrNaeg+g89a30pJUk5Rfi7akuhLdExBY1G077EboaYjcR3U7vthhRmgrBQmUZnIaewjOIyEwHafdSmaKWg0Wjal+3vQfQ6EmzuwdrCmu658WrVU9uz7zucmKMsigBX7T5qS7T7SKPRtB/GUji1DYCEpD0EOgXgaoiFgGENNk3OVYveaUuhbdFKQaPRtB8Ju6GsEJwDSDAkEWjriW1pdoPxBIDI+BwsLQQ99T7MbYpWChqNpv2I2cR2ewfmh/Qh2tqC4IS9qrwRlsJf0RkMCXLFxc66jYXs2miloNFo2o/YzazwCeRQYQpDrd2Yau1NmvdYCBh61ma5hWUcTMhhfKh3+8jZhdGBZo1G0+asjEigNCeZ65P2EtWrL+MDx/HGxDc4kZrPbV9sxfnTvcwY5M/NY3qwbPdpftqfWNk2xNuJ0SGemCSM7+3VgVfRNdBKQaPRtDmL/zzOf3L+TbatDYnlBczxHADA2sMpHM40EWpZyr9/PszprEI+2RpHiJcjnk62mEySb3ad5pfIJBxtLBnWza1jL6QLoN1HGo2mTSksNXJp1teMtTjEipCbARhgVgon0gx42gl+uX88/f1dWPxXHAMDXFj9wIV8d9cYVtwzljvG9yS/2MjoEE+sLfUtq63RloJGo2lTohJzudZyM1tMQ1iaawv2VUohOs2Av5MFdtaWfHjjcBZuPMGjl/bFzrpqW80npvWjxGhi6kC/DrqCroVWuxqNpk05GX2IIJFBWa+pZJbF4m0XgKutKyaTJCbdQICj2pe5h5cjb147lMAz5iFYW1rwwlVhjA/V8YT2QCsFjUbTtsRsAmDUJbOwcUgiO9uHDEMJiTlFatkKJ30b6kzov4ZGo2k5h3+Eza+qzye3wWcz4LPpcPhHfDN28qmrP/dGvoK0yqK4IIAHl+0jOs0AgL+jvg11JnRMQaPRtJytb0HyARhyPWx+BVIPgoMncuWdDDRZ8Jh7AO6FaYwLHEdotyt5d20mno5q+8xAbSl0KrRS0Gg0LaMgUykEJGx4jr3Ju3k3ZDAmJ19mxxopL8mkUJTz/rgXGeE3gtyiMhZvXM+qyCQ8HW1wshEdfQWaamgVrdFoWkbcn4AElyA4tJLvXJw5XJZLZmku/3Kx5E03H7o7dSfcV22f6WpvzfQwfwB6+Zx9ZVRN+6OVgkajaRmxm8DWFS55DgnsdHJhYrdJfD39ayxNHmRblzO3/1yEqLII5o4MBqC3VgqdDq0UNBpN85ESYjZDzwuh/xUcHzyLTIyM8R+DFY4YTt9Mb/tLuKr3VTWaXdDTg/nje3LN8KAOEVtTP+2uFIQQwUKITUKIKCHEYSHEg+ZyDyHEOiHECfO7e5sLs/FFWDIVvrkOSgvbfDiN5rxi1yISl1zM09YG0oNHgpUNO/tOBGBMwBh2n8yitMiHR4Y9hbONc42mQgj+PXMA4d3b/t9c0zQ6wlIwAo9KKQcAo4F7hRADgCeBDVLKUGCD+bjNsDQWwda3ISsGjq+BpH1tOZxGc35x+EfK1jzO/1lkscrZiW8s1UPVjuQd9HDpgZ+jH1tPZGBjZcHIHh4dLKymKbS7UpBSJksp95o/5wNHgEDgSuBzc7XPgavaUg7X3CgwlcHkZ1RBxrG2HE6jObeJ3w0/3gPlRsiMgZ/+wbvd+nPA0kSgUyDLYn7nud92EZESQXeHoVz78Q6+2xPPyB7uNZas0HR+hJSy4wYXogewBQgDTksp3czlAsiuOD6jzQJgAYCvr2/4smXLmjV2tyMf0SNtPVvHf824bfNI9p9CdOidzeqrNTEYDDg5db7gm5araZxvcg2OfAaP7P0cDHsa9+xIDuZs5v98PBjnNI6BdmEsyvgYU6kHFtY5GE/fg4OpG36Ogqk9rBnsffbM9/Ptu2prWkOuSZMmRUgpR9R1rsPmKQghnICVwENSyrzqmQlSSimEqFNbSSkXAYsARowYISdOnNis8Q1/P4BFz3FMmDwVovsRZFtAUDP7ak02b95Mc6+pLdFyNY3zQa7VB5I5lJTLEyOtYfN+AAYV7eBk9j7+5ePJAI9BLJy+kA1H0jElL8fCJovilJm4WvTmlwfH4e/auL2Uz4fvqj1pa7k6RCkIIaxRCuFrKeUP5uJUIYS/lDJZCOEPpLWZAPmpOBWcgpDb1LF3Xzi5tc2G02jORRZtiSEyIZe7SrbiJixh0Gw4sJxvPdwpk4Kck9dRXm7Jst2J2OfN5uaLbEh1Hse1I7s1WiFoOh/trhTMrqElwBEp5ZvVTq0CbgFeNb//3GZCxG5W7yGT1LtXHziwHErywda53mYaTVchp7CUHsm/8YzNWhwiE6HPZTDh/yg6+B0/OztB4RCOJ1kwfeFfnMws4P5Jl/FgeF8I72jJNS2lI7KPxgE3AxcLIfabX9NRyuASIcQJYIr5uG2wtCbbbRD4DVbH3n3Ve8bxNhtSozmX2BGdxv9ZLSPQIpu/ZT/KLnwcvHrz27DZFFgIJgZcwX+vGUyQuz2X9PflpjHdO1pkTSvR7paClHIrUN9iJ5PbRYiwWURmeDDRwqwTvfup9/TjEBgOR36F6PUw/XWwNH9Fm1+FE+vAxgGuXgQu/u0iqkbTZhz+CWI2qt+5lU2NU2n7VhMoMtk7+h1u3OxNvxX59PbZy0mbfMpLvLm6/4VM6ufLtSOCO0Z2TZuhZzQDuPcEC2tIP6qOd34AEZ/BxhfUcVmxmtNQlA1xf8GeJR0mqkbTKsT/DT/cCXs/h7VP1Trd+/T35Fq6M/ji65kTHoS3sy2/HUzkpOEYsmAAo0I8O0BoTXuglQIoa8Czl1IKJQaVk23vDtvehhPrIX4XGItg6ssQegns/RIMabDsRlh8May4HUympo2Zfhy+m6fG02haEylh1f0QtarWKZ/ULeo3+/U14OwPI26Hvz+BfV+rdn/8m5IPJzLauIdTwVdjZWPL/+YM4cv5o7h7sjdClBPsHISDjV5g+XxFK4UKuo+F2D/h+O9qUtvVi8Ctm1IMsZvAwgp6jIPw28CQAp9MhhN/gLCEQyshdmPTxtv2DkT9rPrWaFqTrFjY+4WyBJIjq8pNJnrGfQn5KdDjQrhhOUz7H/ScAL8+DL8+BNsXklsK60wjcL7ovhrdTgpTk9CuHjS4HS9G095opVDBsJuVNbD2abCyU/8o4bfCyb/gwHcQNFJlJoVeCs4BkHMaLnsVbv0VHDxhz2c1+8tLUpZEQWbtsYrz4LA5E7ciE0qjaS3itqh3awdYfhMUZqnjmI3YF6fBpS/AdV+DT39lJc/+DBy9IWIp9L+CZz1f51n7p+jRI6RGt0mGJACm9x/YjhejaW+0UqggYBj4DlJWQLcxYG0HQ29SFkJeYlX6qqUVzHwTLnlemd5WtjD0Rji2BvKSq/o79AMc/VW9zuTwD1BWqCyRGG0paFqZuD+Va+jGFcoqWHE7mMoh4jNKrV2h3+U16zt6wQ3LYOQdlF/xHttjsxgf6lVjqWuABEMCAoG/o06yOJ/RSqECIWD4PPU5ZKJ6d/aFfjNqlgH0nQbjHlRtQFkUsrxmALriaa0u99C+r8C7P4y6Ry3Il3O6FS9E06UxmVQyRM+LICgcZryhfoMfjIFja0jxm1wr0wgAv0Ew4w0OZ0pyCssY39urVpXE/ER8HX2xsayjvea8QUeLqjPkOkg7rN4rmPgUOPmpVNX68OwFA2epfWr7TlPzH05tU+di/1T/qBXpryUGSIyACY9DL7P1Ebu5SiFpNC0h/QgUZij3J6jfVVGOekjxCiXBfSbdztJ8a3QGAOPqUgqGRAKdAltfZk2nQlsK1bFzgcvfAWe/qjKf/jD9tar5CvUx4w2lPFbMVzf5UgP0nQFFWZByoKpeykGQJggYruZHOPnBhufhsxnmfW41XRJTOax+TAWIz0b0evjkElg0SVmcZ1IRo6pQCgDjHiDr6m+4o+QhYkrdScsv5valf3PFe1t59LtISo0qc05KyfqoVPr5OePtbFur6wRDglYKXQCtFFoLBw+YvQRy4+H7W1XZxf9S79VdSMn71XvAUOV+uvhfKp6RcQyW3YB1aW47Cq3pNGx+Bf5eDL88qCaU1UXaUfjuFjCkgrFYpZ1Wj0lJCZHfKleQW81JZeujUll/JI139hbz0LL9bIvOwNXempV7E3hxdRQAi7bEsvd0DtdfUNuWKCkvIb0wnSAnvVPa+U6XdB+tjEjg3e1FOB/cyrwx3ZnTWrMyu42Gaf+F1Y+qf0zfAeAzQAWhxz2klEDSPhUErLBGht+sXkn74NPLCI94GOKqLQklhIo9DJnbOjJqWg+TCdb+E07vAGt7mLW41s24UcT+CVv+B4PnKmtxxe2w4E9wNy8dceQX5ZrMPqnGuW0N2LnCkktUdpFnb7U2UeilkHKQ7/0e5vKy8hr7GPwVnYGznRU5xUa2x2Ty0tVh3DiqOy+tjmLxX3HsOZnNsdR8pg/yY555yYoVx1ew4vgKLC0suTzkciSSQGdtKZzvdElLwcHGEldbQWpeMZ9tO9m6nY+YrzbumfB/6jj8NjX5bdvb6jhpP/gPrd0uYBhc+wUGpxBw8q16lRXBz/+AUztaV05Ny9n6Juz6EGyc1ITH5s503/aOSnO+/B2VKmoyqZt9WREk7lUuyeJclRV3w3JwDQRbJ7juG+gzFSxt4M9XYeXtFAs7njs5kOd+iars3mSSbIvOYEp/X+4dZssjl/ThBrM18MRl/Zg/vid+rnbMCQ/itdlDEELwV8JfPL/jecpMZWQWZfLSrpcAtKXQBeiSlsK0Qf7YZx7jQHkgb60/Tk5hKW4OrZRRIQRc+EjV8QV3qifJDc+DRy+16F7YNXW37TOVQ0m2NddKL8qBxZPg+1vgri014x0mE6y4DbLjqsrCb1WpsrsXw74vVUrt1Feg26jGyR+9QaXTznhDpeVq6iZ+N2x6CcJmwzWfwLIbYP83MOlpsLTGI3MPLH4eTMaa7QbNgbH3Vx1nn1TuooueUFaAZy+YtQi+nQsfjFZ/fycfuH2tSh2tjkdPmP2p+h18ex2cWMsv8mJsHN34dvdp9p3Oxs/VjtvG9SSroJTxvb3wzM9h4sTQyi6MspRC1y+5cdAUJndXS4+lFqTyxJYn6OvRly+mfUFOcQ5zf51Ldkm2jil0AbqkpVDB6BBPpITdcVltN4gQcOV7Kqi84jZAqnhCY7F3g7lfq2W9v7sFjKVV52I2QtRPapKSs796mtz4kpoct/FF1SbjBOx4t/HjrX8G9n8Fqx9RPmpN3Rz8Xk1yvPwd9TcedrPy9Z/4A1IOMfDwa2qtLGf/qpewgD/+BQdXVPUT8XnNdGiAvpfBle+r30zIRco6OFMhVMfCAq5ZTHL/23i75EpevCqMuyaEEOhmz87YTO7+MgKA8aE1+5BS8sLOF/g19lee+OsJjmQeAeD7499jKDPw+kWvY29lj7+TP+9Ofpd5A+bh4+DTal+hpnPSJS2FCoYEu2JrZcHO2CwOJebiaGvFXRf1qrPu2+uP4+Vky02jay4RvCMmk+/2xPPS1WH1rwdj4whzv1IZIyW5dbuPzobvAKVYVtwOfzwN0/+nyiM+AwcvmLdK5Z4fXwvfXKvcTcU5cO0XatmOvz+B3ARY9x/1pNp3Wt3jJO1X2VF+g2D/18pdMfzm2vWMpfDLA6qv3mdZ2DZyOZzcAjPeqjs3/lwmZhN0H6fcOKD8+U5+KlBsMmK0csDytjU1LTtjKXxxJfz0D+UyAsiMJjfoYv65OpU3r/XD1socBxh2k3o1FjtXvnW/h2QRzbjeXkwfpCaYrYxI4NHvIwn1ccLXxY5P83fwwS8fAFBmKiM6J5ob+9/I+lPreXjzw3w1/St+PPEj4wLH0d2l6rc+xHsIQ7yHNPvr0pw7dGmlYGtlyYge7iz7+zSFpeW4O1hz54UhWFjUnMlZXFbOh5tjsLexZM6IoKp/XOC/vx9lf3wOpeUm3rt+WK1ZoJV49lL+4piNalJcUwm7RvmXd7yn5kz0vEgFsMfeV3XD7TVZ+aaP/KJcVT0nqIDkzg9UGmN+Ehz7He7cCD79ao+x9wv19DtvFXw9G7YvVDemM6/p6C8qy+XoarhzE3j1rt3X6Z1KOZmMYO2o0nrPF3ITIPMEjLitqszSCi57GQ58D5bWHHSYyIjqCgHU3+naz2HdM8qKAHDrxtKyK1l9IJnpYf7MGNz82cJ/RWcwJNgNV3vryrJrwoPILiwlwM2ebYnb+DbrW0LdQwlwDADgoqCLuH/Y/czoOYNbfr+Fm367ibSiNJ7u83Sz5dCc23RppQAwuqcn26Iz8XKyJcNQwtGUfE6k5bNoSyxCwJOX9UcIKDGaKDGa+ONwKpcPUf9QUUl57I/PYXCQK6sPJDM40LVeSwNgS1k/fspy5nWTrKV4GsWU59QCZ6seUIpFlsPwW6rOW1rBsBtVJkv4repm7j9EuSHSj6oMqP3fwGfTVLDyTDJOwIArVXpt+G2w6j51c49eR/i+HyDGR633tOczpXyMxbD8RrhjQ9UTMyj31Xe3gGuwmgm++2MIHF41KTArTq0xdfHTYO8Bvz2mZAseWf+17/xQWS8W1iqN12yhOBri4NNX1bwQAFsXFQ/x6V+7j+RI+O3/oKygZrn/EJj5NiT8rawpY3HN8/2vhIserzo+c+e+CsKuqYwXGTZvrvs6nHzg6g8rD6WULH91I1DM8j3xDSqFTcfSeHvdcYwmybwx3Zk7UgWMU/OKiYzP4b5JtRX0HReGEJ8Xz9zVj+Nv7c+X077EwdqhRp1B3oN4etTTPLvjWXwcfJgQNKFWP5quQZdXCrPCg0jNL+bm0T2Y+vYWtkVn8OXOUxjLTRQbTSzceILh3dyxshB4O9uy/O/4SqXwze5T2FhZ8MXtF/DPHw/yv7XHGBXiydBgtzrH+mLHKdYfSeXqYYFcGOrddGErFi9bb37SHDZPWSDVuWABFGZW+aiFUGmySfvV0hwDrlB7Q5wZAAVw7wHjH1afB14Nvz+pVtrMjafcdQBkn1IBTUMqXPxvZbF8NUspj9mfVVkUB5arNaRuXwuBIyAzWrlVfPqr9MllN6qZ45kn1E08cQ8k7DEH0uuwoo7+pmTxH6omA35/GyzYBHauDDr4ElgJCBqh6sbvUpk7d25SkxErMKTBt9er664+O91YoiaBGUvVxDAbR+U+q6AgAza9qDZVqnDnxGxSmWF1KZ4mEptRQFJuMd08HPjrRDoJ2YUEuTvUWfd4aj73fr0XH2db7G2seOqHg/i72jMqxIN7vorA1sqSq4bVVvaFZYU8sOkBLIQFd3rdWUshVHBNn2soLi8myCkIK4suf2vosnT5v3ygmz0vXqVuAiFejiz6K5b0/BLeuW4oSTnF/Pf3o5zKLGB4N3fGh3rx5rrjTH/nLwCi0w3MHOSPm4MNr8waTGT8X9zy6W4C3WpuWn5hqBePTe3Lzli1Yuo3u043TykAOHnDVR+c5bwPzHyrZlnIxKq1mwLDYe6XDY9j6wRhs5RLqe8M9vvdwcQ+rrBkqspoGnazuoFP/g+sf1b1O/Z+FZyOWKpurMGjlKKY/Rksugg+v0IpgbwElW3z52uAVE/+W96Ajyeo6zuTzBilEG5fCwVpqt6SS8HSGpvSHJj3h7JEAE5ug88vhw/HqiB9BQUZKpNn/lplGVTn93/CzvfB1hXm/VxT0ZYbleL79RE158Srj5qM2HtKbbdaM9hmXlbi1WsGceMnu5j78U4C3Ox4/4bhONhacfeXEWQVqOSC5NwiHG2tWLZgDC72Vsz6YDt3fxWBl5Mtp7MK+eDG4aSUHmDJ1tX8Z8x/OJxxmDci3iCjMIOUwhQ+mvIRJcdLzirPjf1vbPE1ac5turxSqM6YXp58ves07g7WXBbmR36xkTfXHSM1r4QbR3XnxlHdOJFmoKi0HIBuHg78w2yuu9pbs2heOO9viqbUWJW1k5RTxKK/YgkLdMVQYqSXtyProlJJyy/Gx7mTp3xOeFzdxC96AnbuVTf+OUvVjbniiX7cQyrWse4ZdbO1doDUQzDjzaqbppO3yqDZ8jqUlypFMniOWiVWmpRV4z8M9nwK1JHx5DNAWSbWdqrN9ctg+7sgJYdshjO4QiGA2vPimsU1M3wAXLupGMCZCgHUirdWNtD7ktqWV4V1tugiWH4z+A9WVlp1t10L+OtEBsEe9ozt5cVjl/Zl3+lsNhxN45vdp/F2tmVrdAYT+nhjY2lBDy8H7rmoN36u6nezeN4I3vjjGIaScu6+qBfTwvyY/ct9HM8+TpGxiD0pe7C1sqWfRz8eHP4gYwLGsPn45laRW3P+opVCNcb19uLrXaeZHa6CybZOllw60I/VB5IZH+qFp5Mt714/rN72AwNc+eDGmgvnnc4sZML/NvHcL4cRAv43ZwizPtjOrA+242ynAoLTw/y4f3IoP+9P5PVtRThF/lWrby8nG964dkgNRVJQYuSJlQeYOTiASwf48p9VhxgY4Fq5TEF2QSlP/XCQeWO6M7aOBc4axK0bTH2pZln/mTWPhVCWy+KL4dsblPvF2lFlJlXHb5AKslanenZN6BT1OoO0/GKeXXWYB4pd8S8s48kfDnDdBb0YO+dLnv7xIDuOJhG0aCevzR5MsIfZLVLNt1+dkxkFvPplBE/P6F9VFzAJS57MuZpJ+T7UmZfl6KmsqyVT4dhvcOmL7LMYwH/e3YrRpJSYs60VL14dhpeTLY99H8mJRPV3tBBw10W9uMLscqxOblEZO2MymWk+d6/5AePmJbv4fk8CrvbW9PNz5vPbRtZIYDCajLy862VG+Y/i7eumVpZHpkdyPPs4/Tz6se7UOpysnfj8ks/p6dqzrqvSaOpEK4VqTOzrzbwx3bnzwqrNRR6e0ocAVzuGBLk1q89ung6M662C2YODXBnezZ1HLunDwUS1xtGpzALe2xTNTaO78/ofxygsk/Rzt6/Vz9YTGdz79V6+vmM0NlYWSCl5fEUkvx1MYf2RVKYP8ueHvYn09nHi+gu6YSw3cf+3+9gancHuk1msefBCfF3ayDKxdVZP75teVrNw+0yt6c9vAV/tOMVvB1M4lJhHNw8HtkZnsPVEBpP6+bAqMolBXpYcSszlgWX7+O6uMVhb1j/15uMtsfx+OIWEnEJW3D22chmIrdEZfLcngZ/2J+HvZl93TMg845zUQzDmPj74MoKTmQWMNu9VHHEqm7u/jMDXxY6IU9kM9BR4udtzMqOAR7/bT6CbHeHdPSq7KzdJHlq2j6KycuaOrLk0xtyRwdz3zT4Sc4p44aqwWhltb0a8yffHv2db4jamdJuCpYW6ju+PfY+DlQNLpi5h6aGljA0YqxWCpslopVANBxsrnr8yrEZZbx8nnp4xoEX9zh3ZjW3RmZVr1D8wuWpG6aHEXGa+u5X/W3mA+Kwi7h5iy5PXj6jVx6rIJB74dh8vrY7iuSvD+PDPGH47mMI9E3vxw94EftibiLezLdFpBtLyivlq12m2Rmdw76RefLr1JA8t28+X8y9gy4l03lp3grJyE/PG9OCGUcqqyDSU8Oj3kaTkqswbIQTzx/fkmuGBvLb2GL9GFOEVtY1nLx9ID09Hnlh5gLkXBDOpr3kyk2cvtSAgKhPmsSW7SM9X/msLIbh7Yi8uH+zPC78eYXtMRo1rs7O25N8z+9e4aQIYy00s3xNPH18nYtMLOJ1VyIOTQ/l8x0lWRSYxf3xPLnRKo8CjL/d+s5cpb/6JvflGPzrEk//MHFCZ5WUoMbJqfyL9/V04lJjHlDf/xMvJln/PHMA3u07j4WiDg40lNy/ZVRkT8nC04X9zhlTFiPpeBn0vIyW3mI1H07jzwhCenKZSe3fFZnLDJ7uIzSjgtdmD8THEMHHiCHIKS7ny/W3c+unfBFZT9iVGE3EZBbxwVVgNJZSQn8AvKS/i5jyF0hInrhpa08L4LfY3voz6kgGeA4jKjGJ70nYOZhxkw+kNxObGclXvq3CxceGB4Q/U+3vUaM6GVgrtwNSBvtw+rmedq0+GBboyMMCFdVGpeDjaEO5rWUcPcMWQAA7E5/DJ1jiMJsk3u09zxZAA/m9qX2YM8mdVZBJTB/pyzYc72HQsjaXb4rhsoB+PT+1HTy8nHvs+kke/j2R9VCrezraYJLy1/jhzRqi1bO77Zh8Rp7O5qI83AjidVciTKw/wd1wWy/fE09fdgqScIu75KoIQbyf1xB6dwar7xhHiXZWOWmIs5+6vIjianM+F5hm0cRkFPPZdJFuOp7MiIoExIZ4421X99A4m5nL3V3tZff94fKpZMxuPppGaV8ILV4ZRbpIk5hRxx4UhTOjjxYYjaTxySR+2/pXGjMH+pOYNqAzkG0qMLN1+Eg9Hm0oF/PP+RApKy3np6jCiUw2sP5LKgYRc7voygpzCUuaP78ns8CAWboympEzFjLbHZHLPVxF8d9eYGovLLf87nnKT5PoLqp7wR4V48vbcoWQYSrh2RDCbN8cA4OZgw5JbRrJwwwmKzf1WcP0Fwdw0quZv4qsjX7E9eRsXjwhhvOf1lS5GgGNZx3hm+zMM9xnOh1M+ZNoP03hux3OkFqYyzGcYFwdfzO0Db6/z96PRNBatFNoBWytL/nN5/dbG3JHB/Ofnw8wJD8LaIrXeek9O68fhpDy+3nWaAf4u/PeawQghCAt0JSzQlXKTxNXemv+tPU5esZFbxvYAYHZ4EIcSc1m6/SSejjZ8u2A0hxPzuOOLPWw6msaO2Ex2xGby5rVDmDVcKYm84jKufG8by/fEM3WgL3OD8vEOHc41H24nKTeDh6aE8vn2k1zz4fYabilDiZGE7CI+uHF45aza7IJSZr67lRURCVw5NIC35w6t4RI5lpLPVe9vY/rCrfg423LvpN5MC/Pj021x+LnYcXE/H6yquYXCu3vUsipuH9+T28crV4mUkke+i+TNdcdZfSAZISAxp4h+fs4MC3ZjeDd3rh0ZzNGUPK56fxtGk+T6C7rRw8uxRsxo7eEU7voyotKqePaKgfT2cWLZ36e5MNSL7p6ONWS4vI64AShrc+FZYlEVFBuLWRWzCoAj+ev55/hbuH/j/cwbMI9+Hv14aNNDuNi48MbEN3CwduDq3lez5NASxviP4cMpH1a6kTSaltBllYLsROv6zBoeREyagfnjexK1t36lYGVpwXs3DOP9TTHcPr4H9jY1bwKWFoLRIR6sPZxKiJcjo0OqbpxPz+iPg40llwzwxd/VHm8nW3ycbXl21WGScou5fVzPSoUA4GJnzeJ5I1i2+zQPXdKHPTu2MijIlYXXDyMxp0i5bkK9+XRrHEaTqYYc903qXakQANwdbVhy6whWRiTwyCV9a/nI+/o58/HN4Xyz6zTR6QYe/m4/G476szM2ixeuCquhEBqDEIJXZg3C1d6a5NwiAHp4OnLT6O41xu7n58LieSM4kWqgh5djrX6mDvTj1VmD2HQsjcj4XO76cg8DA1xJzSvm7blDmyRTY1h7ci35pfnM7TuX5ceWc9Oam0gpSGFf2j4GeQ0iuSCZpZctxcteWWDzBs7DJE3cFnabVgia1kNKec6+wsPDZXP44fgP8uKvLpalxtLKsqWHlsr39r0nTSZTrfrlpnL58s6X5VU/XSVv+PUGmWxIbta4Z2IymeR7+96Tnx78tLJs06ZNLepz6bY42f2JX+WiP2MarPvfNUdk9yd+ldcv2iHLjOVnrdtSuRpLRn6xHP3yetn9iV/lI8v31/n36Ai5DiXmyD5P/ya7P/GrXLyl4e/2TLnKTeXylV2vyMUHFtdZv6y8TF77y7Vy5g8zZbGxWI7/drwMWxomPz34qRz7zVgZtjRMLopc1OLraK/vqyl0RpmkPL/lAvbIeu6rXdJS8LT3JM2Yxsb4jUztMZXcklwW7l1IqakUb3tvru17bY36Hx/4mG+OfsNo/9HsTtnNsqPLeCj8oRbL8d2x7/go8iNsLGyYFToLV1vXFvd5xZAA4jIKmHtBcIN1bx/fk3KT5K6LejX5abyt8HSyZcktI/l5fyIPX9Kn/rWk2pmBAa58dHM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+ }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "plt.plot(df['Mes'], df['data science'], label='data science')\n", + "plt.plot(df['Mes'], df['machine learning'], label='machine learning')\n", + "plt.plot(df['Mes'], df['deep learning'], label='deep learning')\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Popularity')\n", + "plt.title('Popularity of AI terms by date')\n", + "plt.grid(True)\n", + "plt.text(x=14600, y=80, s=r'$\\lambda=1, r^2=0.8$') #Coordinates use the same units as the graph\n", + "# plt.annotate('Notice something?', xy=('2014-01-01', 30), xytext=('2006-01-01', 50), arrowprops={'facecolor':'red', 'shrink':0.05})" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "D:\\Program Files\\Python38\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n warnings.warn(\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<AxesSubplot:xlabel='Mes', ylabel='data science'>" + ] + }, + "metadata": {}, + "execution_count": 23 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 432x288 with 1 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"266.374219pt\" version=\"1.1\" viewBox=\"0 0 392.025469 266.374219\" width=\"392.025469pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" 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</g>\r\n </g>\r\n <g id=\"xtick_4\">\r\n <g id=\"line2d_4\">\r\n <path clip-path=\"url(#paa2876ee8b)\" d=\"M 178.803666 224.64 \r\nL 178.803666 7.2 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_4\">\r\n <!-- 2010 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(166.569604 242.013594)scale(0.11 -0.11)\">\r\n <defs>\r\n <path d=\"M 2384 0 \r\nL 1822 0 \r\nL 1822 3584 \r\nQ 1619 3391 1289 3197 \r\nQ 959 3003 697 2906 \r\nL 697 3450 \r\nQ 1169 3672 1522 3987 \r\nQ 1875 4303 2022 4600 \r\nL 2384 4600 \r\nL 2384 0 \r\nz\r\n\" id=\"ArialMT-31\" transform=\"scale(0.015625)\"/>\r\n </defs>\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_5\">\r\n <g id=\"line2d_5\">\r\n <path clip-path=\"url(#paa2876ee8b)\" d=\"M 216.622467 224.64 \r\nL 216.622467 7.2 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_5\">\r\n <!-- 2012 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(204.388404 242.013594)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-32\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_6\">\r\n <g id=\"line2d_6\">\r\n <path clip-path=\"url(#paa2876ee8b)\" d=\"M 254.493074 224.64 \r\nL 254.493074 7.2 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_6\">\r\n <!-- 2014 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(242.259012 242.013594)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-34\"/>\r\n 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+ }, + "metadata": {} + } + ], + "source": [ + "import seaborn as sns\n", + "sns.set()\n", + "sns.scatterplot(df['Mes'], df['data science'])\n", + "# Seaborn 是基于 Matplotlib 的库。基本上，它提供给我们的是更好的图形和功能，只需一行代码即可制作复杂类型的图形。\n", + "# 我们导入库并使用 sns.set（）初始化图形样式，如果没有此命令，图形将仍然具有与 Matplotlib 相同的样式。我们显示了最简单的图形之一，散点图：" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<seaborn.axisgrid.FacetGrid at 0x235887eefa0>" + ] + }, + "metadata": {}, + "execution_count": 24 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 828.725x360 with 2 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"364.092344pt\" version=\"1.1\" viewBox=\"0 0 833.589725 364.092344\" width=\"833.589725pt\" 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transform=\"scale(0.015625)\"/>\r\n </defs>\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-34\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_2\">\r\n <g id=\"line2d_2\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 101.842071 322.358125 \r\nL 101.842071 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_2\">\r\n <!-- 2006 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(89.608008 339.731719)scale(0.11 -0.11)\">\r\n <defs>\r\n <path d=\"M 3184 3459 \r\nL 2625 3416 \r\nQ 2550 3747 2413 3897 \r\nQ 2184 4138 1850 4138 \r\nQ 1581 4138 1378 3988 \r\nQ 1113 3794 959 3422 \r\nQ 806 3050 800 2363 \r\nQ 1003 2672 1297 2822 \r\nQ 1591 2972 1913 2972 \r\nQ 2475 2972 2870 2558 \r\nQ 3266 2144 3266 1488 \r\nQ 3266 1056 3080 686 \r\nQ 2894 316 2569 119 \r\nQ 2244 -78 1831 -78 \r\nQ 1128 -78 684 439 \r\nQ 241 956 241 2144 \r\nQ 241 3472 731 4075 \r\nQ 1159 4600 1884 4600 \r\nQ 2425 4600 2770 4297 \r\nQ 3116 3994 3184 3459 \r\nz\r\nM 888 1484 \r\nQ 888 1194 1011 928 \r\nQ 1134 663 1356 523 \r\nQ 1578 384 1822 384 \r\nQ 2178 384 2434 671 \r\nQ 2691 959 2691 1453 \r\nQ 2691 1928 2437 2201 \r\nQ 2184 2475 1800 2475 \r\nQ 1419 2475 1153 2201 \r\nQ 888 1928 888 1484 \r\nz\r\n\" id=\"ArialMT-36\" transform=\"scale(0.015625)\"/>\r\n </defs>\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-36\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_3\">\r\n <g id=\"line2d_3\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 138.654428 322.358125 \r\nL 138.654428 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_3\">\r\n <!-- 2008 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(126.420366 339.731719)scale(0.11 -0.11)\">\r\n <defs>\r\n <path d=\"M 1131 2484 \r\nQ 781 2613 612 2850 \r\nQ 444 3088 444 3419 \r\nQ 444 3919 803 4259 \r\nQ 1163 4600 1759 4600 \r\nQ 2359 4600 2725 4251 \r\nQ 3091 3903 3091 3403 \r\nQ 3091 3084 2923 2848 \r\nQ 2756 2613 2416 2484 \r\nQ 2838 2347 3058 2040 \r\nQ 3278 1734 3278 1309 \r\nQ 3278 722 2862 322 \r\nQ 2447 -78 1769 -78 \r\nQ 1091 -78 675 323 \r\nQ 259 725 259 1325 \r\nQ 259 1772 486 2073 \r\nQ 713 2375 1131 2484 \r\nz\r\nM 1019 3438 \r\nQ 1019 3113 1228 2906 \r\nQ 1438 2700 1772 2700 \r\nQ 2097 2700 2305 2904 \r\nQ 2513 3109 2513 3406 \r\nQ 2513 3716 2298 3927 \r\nQ 2084 4138 1766 4138 \r\nQ 1444 4138 1231 3931 \r\nQ 1019 3725 1019 3438 \r\nz\r\nM 838 1322 \r\nQ 838 1081 952 856 \r\nQ 1066 631 1291 507 \r\nQ 1516 384 1775 384 \r\nQ 2178 384 2440 643 \r\nQ 2703 903 2703 1303 \r\nQ 2703 1709 2433 1975 \r\nQ 2163 2241 1756 2241 \r\nQ 1359 2241 1098 1978 \r\nQ 838 1716 838 1322 \r\nz\r\n\" id=\"ArialMT-38\" transform=\"scale(0.015625)\"/>\r\n </defs>\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-38\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_4\">\r\n <g id=\"line2d_4\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 175.517213 322.358125 \r\nL 175.517213 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_4\">\r\n <!-- 2010 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(163.283151 339.731719)scale(0.11 -0.11)\">\r\n <defs>\r\n <path d=\"M 2384 0 \r\nL 1822 0 \r\nL 1822 3584 \r\nQ 1619 3391 1289 3197 \r\nQ 959 3003 697 2906 \r\nL 697 3450 \r\nQ 1169 3672 1522 3987 \r\nQ 1875 4303 2022 4600 \r\nL 2384 4600 \r\nL 2384 0 \r\nz\r\n\" id=\"ArialMT-31\" transform=\"scale(0.015625)\"/>\r\n </defs>\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_5\">\r\n <g id=\"line2d_5\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 212.329571 322.358125 \r\nL 212.329571 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_5\">\r\n <!-- 2012 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(200.095508 339.731719)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-32\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_6\">\r\n <g id=\"line2d_6\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 249.192356 322.358125 \r\nL 249.192356 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_6\">\r\n <!-- 2014 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(236.958294 339.731719)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-34\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_7\">\r\n <g id=\"line2d_7\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 286.004714 322.358125 \r\nL 286.004714 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_7\">\r\n <!-- 2016 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(273.770651 339.731719)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-36\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_8\">\r\n <g id=\"line2d_8\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 322.867499 322.358125 \r\nL 322.867499 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_8\">\r\n <!-- 2018 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(310.633436 339.731719)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-38\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_9\">\r\n <g id=\"line2d_9\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 359.679856 322.358125 \r\nL 359.679856 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_9\">\r\n <!-- 2020 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(347.445794 339.731719)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"text_10\">\r\n <!-- Mes -->\r\n <g style=\"fill:#262626;\" transform=\"translate(201.776828 354.507344)scale(0.12 -0.12)\">\r\n <defs>\r\n <path d=\"M 475 0 \r\nL 475 4581 \r\nL 1388 4581 \r\nL 2472 1338 \r\nQ 2622 884 2691 659 \r\nQ 2769 909 2934 1394 \r\nL 4031 4581 \r\nL 4847 4581 \r\nL 4847 0 \r\nL 4263 0 \r\nL 4263 3834 \r\nL 2931 0 \r\nL 2384 0 \r\nL 1059 3900 \r\nL 1059 0 \r\nL 475 0 \r\nz\r\n\" id=\"ArialMT-4d\" transform=\"scale(0.015625)\"/>\r\n <path d=\"M 2694 1069 \r\nL 3275 997 \r\nQ 3138 488 2766 206 \r\nQ 2394 -75 1816 -75 \r\nQ 1088 -75 661 373 \r\nQ 234 822 234 1631 \r\nQ 234 2469 665 2931 \r\nQ 1097 3394 1784 3394 \r\nQ 2450 3394 2872 2941 \r\nQ 3294 2488 3294 1666 \r\nQ 3294 1616 3291 1516 \r\nL 816 1516 \r\nQ 847 969 1125 678 \r\nQ 1403 388 1819 388 \r\nQ 2128 388 2347 550 \r\nQ 2566 713 2694 1069 \r\nz\r\nM 847 1978 \r\nL 2700 1978 \r\nQ 2663 2397 2488 2606 \r\nQ 2219 2931 1791 2931 \r\nQ 1403 2931 1139 2672 \r\nQ 875 2413 847 1978 \r\nz\r\n\" id=\"ArialMT-65\" transform=\"scale(0.015625)\"/>\r\n <path d=\"M 197 991 \r\nL 753 1078 \r\nQ 800 744 1014 566 \r\nQ 1228 388 1613 388 \r\nQ 2000 388 2187 545 \r\nQ 2375 703 2375 916 \r\nQ 2375 1106 2209 1216 \r\nQ 2094 1291 1634 1406 \r\nQ 1016 1563 777 1677 \r\nQ 538 1791 414 1992 \r\nQ 291 2194 291 2438 \r\nQ 291 2659 392 2848 \r\nQ 494 3038 669 3163 \r\nQ 800 3259 1026 3326 \r\nQ 1253 3394 1513 3394 \r\nQ 1903 3394 2198 3281 \r\nQ 2494 3169 2634 2976 \r\nQ 2775 2784 2828 2463 \r\nL 2278 2388 \r\nQ 2241 2644 2061 2787 \r\nQ 1881 2931 1553 2931 \r\nQ 1166 2931 1000 2803 \r\nQ 834 2675 834 2503 \r\nQ 834 2394 903 2306 \r\nQ 972 2216 1119 2156 \r\nQ 1203 2125 1616 2013 \r\nQ 2213 1853 2448 1751 \r\nQ 2684 1650 2818 1456 \r\nQ 2953 1263 2953 975 \r\nQ 2953 694 2789 445 \r\nQ 2625 197 2315 61 \r\nQ 2006 -75 1616 -75 \r\nQ 969 -75 630 194 \r\nQ 291 463 197 991 \r\nz\r\n\" id=\"ArialMT-73\" transform=\"scale(0.015625)\"/>\r\n </defs>\r\n <use xlink:href=\"#ArialMT-4d\"/>\r\n <use x=\"83.300781\" xlink:href=\"#ArialMT-65\"/>\r\n <use x=\"138.916016\" xlink:href=\"#ArialMT-73\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"matplotlib.axis_2\">\r\n <g id=\"ytick_1\">\r\n <g id=\"line2d_10\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 50.166094 311.457245 \r\nL 376.056312 311.457245 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_11\">\r\n <!-- 0 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(34.549063 315.394042)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"ytick_2\">\r\n <g id=\"line2d_11\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 50.166094 256.262915 \r\nL 376.056312 256.262915 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_12\">\r\n <!-- 20 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(28.432031 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xlink:href=\"#ArialMT-36\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"ytick_5\">\r\n <g id=\"line2d_14\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 50.166094 90.679926 \r\nL 376.056312 90.679926 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_15\">\r\n <!-- 80 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(28.432031 94.616723)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-38\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"ytick_6\">\r\n <g id=\"line2d_15\">\r\n <path clip-path=\"url(#paa92130074)\" d=\"M 50.166094 35.485597 \r\nL 376.056312 35.485597 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_16\">\r\n <!-- 100 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(22.315 39.422393)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"55.615234\" 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style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_19\">\r\n <!-- 2004 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(391.566386 339.731719)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-34\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_11\">\r\n <g id=\"line2d_17\">\r\n <path clip-path=\"url(#p986cbf4961)\" d=\"M 440.663233 322.358125 \r\nL 440.663233 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_20\">\r\n <!-- 2006 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(428.429171 339.731719)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-36\"/>\r\n 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xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_14\">\r\n <g id=\"line2d_20\">\r\n <path clip-path=\"url(#p986cbf4961)\" d=\"M 551.150734 322.358125 \r\nL 551.150734 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_23\">\r\n <!-- 2012 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(538.916671 339.731719)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-32\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_15\">\r\n <g id=\"line2d_21\">\r\n <path clip-path=\"url(#p986cbf4961)\" d=\"M 588.013519 322.358125 \r\nL 588.013519 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_24\">\r\n <!-- 2014 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(575.779456 339.731719)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-34\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_16\">\r\n <g id=\"line2d_22\">\r\n <path clip-path=\"url(#p986cbf4961)\" d=\"M 624.825876 322.358125 \r\nL 624.825876 21.825 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_25\">\r\n <!-- 2016 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(612.591814 339.731719)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-36\"/>\r\n 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+ }, + "metadata": {} + } + ], + "source": [ + "sns.relplot(x='Mes', y='deep learning', hue='data science', size='machine learning', col='categorical', data=df)\n", + "# 我们可以在同一张图中添加两个以上变量的信息。为此，我们使用颜色和大小。我们还根据类别列的值制作了一个不同的图：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<AxesSubplot:>" + ] + }, + "metadata": {}, + "execution_count": 25 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 432x288 with 2 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"329.043516pt\" version=\"1.1\" viewBox=\"0 0 427.908719 329.043516\" width=\"427.908719pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF 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\n" + }, + "metadata": {} + } + ], + "source": [ + "sns.heatmap(df.corr(),annot = True,fmt ='.2f')\n", + "# Seaborn 提供的最受欢迎的图形之一是热图。通常使用它来显示数据集中变量之间的所有相关性：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<seaborn.axisgrid.PairGrid at 0x23587100310>" + ] + }, + "metadata": {}, + "execution_count": 26 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 720x720 with 20 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"709.127969pt\" version=\"1.1\" viewBox=\"0 0 708.605114 709.127969\" width=\"708.605114pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" 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+ }, + "metadata": {} + } + ], + "source": [ + "sns.pairplot(df)\n", + "# 另一个最受欢迎的是配对图，它向我们显示了所有变量之间的关系。如果您有一个大数据集，请谨慎使用此功能，因为它必须显示所有数据点的次数与有列的次数相同，这意味着通过增加数据的维数，处理时间将成倍增加。" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<seaborn.axisgrid.PairGrid at 0x23588a18b50>" + ] + }, + "metadata": {}, + "execution_count": 27 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 606.675x540 with 12 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"532.774844pt\" version=\"1.1\" viewBox=\"0 0 604.599344 532.774844\" width=\"604.599344pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF 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+ }, + "metadata": {} + } + ], + "source": [ + "sns.pairplot(df,hue ='categorical')\n", + "# 现在让我们做一个成对图，显示根据分类变量的值细分的图表。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<seaborn.axisgrid.JointGrid at 0x2358ca9d640>" + ] + }, + "metadata": {}, + "execution_count": 28 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 432x432 with 3 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"421.514219pt\" version=\"1.1\" viewBox=\"0 0 424.446094 421.514219\" width=\"424.446094pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" 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+ }, + "metadata": {} + } + ], + "source": [ + "sns.jointplot(x='data science', y='machine learning', data=df)\n", + "# 联合图是一个非常有用的图，它使我们可以查看散点图以及两个变量的直方图，并查看它们的分布方式：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "<seaborn.axisgrid.FacetGrid at 0x235eef56370>" + ] + }, + "metadata": {}, + "execution_count": 29 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 360x360 with 1 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"363.654844pt\" version=\"1.1\" viewBox=\"0 0 363.488125 363.654844\" width=\"363.488125pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" 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+ }, + "metadata": {} + } + ], + "source": [ + "sns.catplot(x='categorical', y='data science', kind='violin', data=df)\n", + "# 另一个有趣的图形是 ViolinPlot：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Machine Learning')" + ] + }, + "metadata": {}, + "execution_count": 30 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "<Figure size 576x288 with 2 Axes>", + "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<svg height=\"280.963594pt\" version=\"1.1\" viewBox=\"0 0 505.80368 280.963594\" width=\"505.80368pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" 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1484 \r\nQ 888 1194 1011 928 \r\nQ 1134 663 1356 523 \r\nQ 1578 384 1822 384 \r\nQ 2178 384 2434 671 \r\nQ 2691 959 2691 1453 \r\nQ 2691 1928 2437 2201 \r\nQ 2184 2475 1800 2475 \r\nQ 1419 2475 1153 2201 \r\nQ 888 1928 888 1484 \r\nz\r\n\" id=\"ArialMT-36\" transform=\"scale(0.015625)\"/>\r\n </defs>\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-36\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_3\">\r\n <g id=\"line2d_3\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 105.261603 239.229375 \r\nL 105.261603 21.789375 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_3\">\r\n <!-- 2008 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(93.02754 256.602969)scale(0.11 -0.11)\">\r\n <defs>\r\n <path d=\"M 1131 2484 \r\nQ 781 2613 612 2850 \r\nQ 444 3088 444 3419 \r\nQ 444 3919 803 4259 \r\nQ 1163 4600 1759 4600 \r\nQ 2359 4600 2725 4251 \r\nQ 3091 3903 3091 3403 \r\nQ 3091 3084 2923 2848 \r\nQ 2756 2613 2416 2484 \r\nQ 2838 2347 3058 2040 \r\nQ 3278 1734 3278 1309 \r\nQ 3278 722 2862 322 \r\nQ 2447 -78 1769 -78 \r\nQ 1091 -78 675 323 \r\nQ 259 725 259 1325 \r\nQ 259 1772 486 2073 \r\nQ 713 2375 1131 2484 \r\nz\r\nM 1019 3438 \r\nQ 1019 3113 1228 2906 \r\nQ 1438 2700 1772 2700 \r\nQ 2097 2700 2305 2904 \r\nQ 2513 3109 2513 3406 \r\nQ 2513 3716 2298 3927 \r\nQ 2084 4138 1766 4138 \r\nQ 1444 4138 1231 3931 \r\nQ 1019 3725 1019 3438 \r\nz\r\nM 838 1322 \r\nQ 838 1081 952 856 \r\nQ 1066 631 1291 507 \r\nQ 1516 384 1775 384 \r\nQ 2178 384 2440 643 \r\nQ 2703 903 2703 1303 \r\nQ 2703 1709 2433 1975 \r\nQ 2163 2241 1756 2241 \r\nQ 1359 2241 1098 1978 \r\nQ 838 1716 838 1322 \r\nz\r\n\" id=\"ArialMT-38\" transform=\"scale(0.015625)\"/>\r\n </defs>\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-38\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_4\">\r\n <g id=\"line2d_4\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 128.213486 239.229375 \r\nL 128.213486 21.789375 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_4\">\r\n <!-- 2010 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(115.979424 256.602969)scale(0.11 -0.11)\">\r\n <defs>\r\n <path d=\"M 2384 0 \r\nL 1822 0 \r\nL 1822 3584 \r\nQ 1619 3391 1289 3197 \r\nQ 959 3003 697 2906 \r\nL 697 3450 \r\nQ 1169 3672 1522 3987 \r\nQ 1875 4303 2022 4600 \r\nL 2384 4600 \r\nL 2384 0 \r\nz\r\n\" id=\"ArialMT-31\" transform=\"scale(0.015625)\"/>\r\n </defs>\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_5\">\r\n <g id=\"line2d_5\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 151.133971 239.229375 \r\nL 151.133971 21.789375 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_5\">\r\n <!-- 2012 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(138.899909 256.602969)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-32\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_6\">\r\n <g id=\"line2d_6\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 174.085855 239.229375 \r\nL 174.085855 21.789375 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_6\">\r\n <!-- 2014 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(161.851792 256.602969)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-34\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_7\">\r\n <g id=\"line2d_7\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 197.00634 239.229375 \r\nL 197.00634 21.789375 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_7\">\r\n <!-- 2016 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(184.772277 256.602969)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-36\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_8\">\r\n <g id=\"line2d_8\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 219.958223 239.229375 \r\nL 219.958223 21.789375 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_8\">\r\n <!-- 2018 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(207.724161 256.602969)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-38\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"xtick_9\">\r\n <g id=\"line2d_9\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 242.878708 239.229375 \r\nL 242.878708 21.789375 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_9\">\r\n <!-- 2020 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(230.644646 256.602969)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"166.845703\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"text_10\">\r\n <!-- Mes -->\r\n <g style=\"fill:#262626;\" transform=\"translate(140.286264 271.378594)scale(0.12 -0.12)\">\r\n <defs>\r\n <path d=\"M 475 0 \r\nL 475 4581 \r\nL 1388 4581 \r\nL 2472 1338 \r\nQ 2622 884 2691 659 \r\nQ 2769 909 2934 1394 \r\nL 4031 4581 \r\nL 4847 4581 \r\nL 4847 0 \r\nL 4263 0 \r\nL 4263 3834 \r\nL 2931 0 \r\nL 2384 0 \r\nL 1059 3900 \r\nL 1059 0 \r\nL 475 0 \r\nz\r\n\" id=\"ArialMT-4d\" transform=\"scale(0.015625)\"/>\r\n <path d=\"M 2694 1069 \r\nL 3275 997 \r\nQ 3138 488 2766 206 \r\nQ 2394 -75 1816 -75 \r\nQ 1088 -75 661 373 \r\nQ 234 822 234 1631 \r\nQ 234 2469 665 2931 \r\nQ 1097 3394 1784 3394 \r\nQ 2450 3394 2872 2941 \r\nQ 3294 2488 3294 1666 \r\nQ 3294 1616 3291 1516 \r\nL 816 1516 \r\nQ 847 969 1125 678 \r\nQ 1403 388 1819 388 \r\nQ 2128 388 2347 550 \r\nQ 2566 713 2694 1069 \r\nz\r\nM 847 1978 \r\nL 2700 1978 \r\nQ 2663 2397 2488 2606 \r\nQ 2219 2931 1791 2931 \r\nQ 1403 2931 1139 2672 \r\nQ 875 2413 847 1978 \r\nz\r\n\" id=\"ArialMT-65\" transform=\"scale(0.015625)\"/>\r\n <path d=\"M 197 991 \r\nL 753 1078 \r\nQ 800 744 1014 566 \r\nQ 1228 388 1613 388 \r\nQ 2000 388 2187 545 \r\nQ 2375 703 2375 916 \r\nQ 2375 1106 2209 1216 \r\nQ 2094 1291 1634 1406 \r\nQ 1016 1563 777 1677 \r\nQ 538 1791 414 1992 \r\nQ 291 2194 291 2438 \r\nQ 291 2659 392 2848 \r\nQ 494 3038 669 3163 \r\nQ 800 3259 1026 3326 \r\nQ 1253 3394 1513 3394 \r\nQ 1903 3394 2198 3281 \r\nQ 2494 3169 2634 2976 \r\nQ 2775 2784 2828 2463 \r\nL 2278 2388 \r\nQ 2241 2644 2061 2787 \r\nQ 1881 2931 1553 2931 \r\nQ 1166 2931 1000 2803 \r\nQ 834 2675 834 2503 \r\nQ 834 2394 903 2306 \r\nQ 972 2216 1119 2156 \r\nQ 1203 2125 1616 2013 \r\nQ 2213 1853 2448 1751 \r\nQ 2684 1650 2818 1456 \r\nQ 2953 1263 2953 975 \r\nQ 2953 694 2789 445 \r\nQ 2625 197 2315 61 \r\nQ 2006 -75 1616 -75 \r\nQ 969 -75 630 194 \r\nQ 291 463 197 991 \r\nz\r\n\" id=\"ArialMT-73\" transform=\"scale(0.015625)\"/>\r\n </defs>\r\n <use xlink:href=\"#ArialMT-4d\"/>\r\n <use x=\"83.300781\" xlink:href=\"#ArialMT-65\"/>\r\n <use x=\"138.916016\" xlink:href=\"#ArialMT-73\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"matplotlib.axis_2\">\r\n <g id=\"ytick_1\">\r\n <g id=\"line2d_10\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 50.166094 231.342433 \r\nL 253.075185 231.342433 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_11\">\r\n <!-- 0 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(34.549063 235.27923)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"ytick_2\">\r\n <g id=\"line2d_11\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 50.166094 191.408549 \r\nL 253.075185 191.408549 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_12\">\r\n <!-- 20 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(28.432031 195.345345)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-32\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"ytick_3\">\r\n <g id=\"line2d_12\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 50.166094 151.474664 \r\nL 253.075185 151.474664 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_13\">\r\n <!-- 40 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(28.432031 155.411461)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-34\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"ytick_4\">\r\n <g id=\"line2d_13\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 50.166094 111.54078 \r\nL 253.075185 111.54078 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_14\">\r\n <!-- 60 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(28.432031 115.477577)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-36\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"ytick_5\">\r\n <g id=\"line2d_14\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 50.166094 71.606896 \r\nL 253.075185 71.606896 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_15\">\r\n <!-- 80 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(28.432031 75.543693)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-38\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"ytick_6\">\r\n <g id=\"line2d_15\">\r\n <path clip-path=\"url(#p4e82e044da)\" d=\"M 50.166094 31.673011 \r\nL 253.075185 31.673011 \r\n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\r\n </g>\r\n <g id=\"text_16\">\r\n <!-- 100 -->\r\n <g style=\"fill:#262626;\" transform=\"translate(22.315 35.609808)scale(0.11 -0.11)\">\r\n <use xlink:href=\"#ArialMT-31\"/>\r\n <use x=\"55.615234\" xlink:href=\"#ArialMT-30\"/>\r\n <use x=\"111.230469\" xlink:href=\"#ArialMT-30\"/>\r\n </g>\r\n </g>\r\n </g>\r\n <g id=\"text_17\">\r\n <!-- deep learning -->\r\n <g style=\"fill:#262626;\" transform=\"translate(15.789375 166.869375)rotate(-90)scale(0.12 -0.12)\">\r\n <defs>\r\n <path d=\"M 2575 0 \r\nL 2575 419 \r\nQ 2259 -75 1647 -75 \r\nQ 1250 -75 917 144 \r\nQ 584 363 401 755 \r\nQ 219 1147 219 1656 \r\nQ 219 2153 384 2558 \r\nQ 550 2963 881 3178 \r\nQ 1213 3394 1622 3394 \r\nQ 1922 3394 2156 3267 \r\nQ 2391 3141 2538 2938 \r\nL 2538 4581 \r\nL 3097 4581 \r\nL 3097 0 \r\nL 2575 0 \r\nz\r\nM 797 1656 \r\nQ 797 1019 1065 703 \r\nQ 1334 388 1700 388 \r\nQ 2069 388 2326 689 \r\nQ 2584 991 2584 1609 \r\nQ 2584 2291 2321 2609 \r\nQ 2059 2928 1675 2928 \r\nQ 1300 2928 1048 2622 \r\nQ 797 2316 797 1656 \r\nz\r\n\" id=\"ArialMT-64\" transform=\"scale(0.015625)\"/>\r\n <path d=\"M 422 -1272 \r\nL 422 3319 \r\nL 934 3319 \r\nL 934 2888 \r\nQ 1116 3141 1344 3267 \r\nQ 1572 3394 1897 3394 \r\nQ 2322 3394 2647 3175 \r\nQ 2972 2956 3137 2557 \r\nQ 3303 2159 3303 1684 \r\nQ 3303 1175 3120 767 \r\nQ 2938 359 2589 142 \r\nQ 2241 -75 1856 -75 \r\nQ 1575 -75 1351 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, sharey=True, figsize=(8, 4))\n", + "sns.scatterplot(x=\"Mes\", y=\"deep learning\", hue=\"categorical\", data=df, ax=axes[0])\n", + "axes[0].set_title('Deep Learning')\n", + "sns.scatterplot(x=\"Mes\", y=\"machine learning\", hue=\"categorical\", data=df, ax=axes[1])\n", + "axes[1].set_title('Machine Learning')\n", + "# 我们可以像使用 Matplotlib 一样在一个图像中创建多个图形：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "from bokeh.plotting import figure, output_file, save,gridplot\n", + "output_file('data_science_popularity.html')\n", + "# Bokeh 是一个库，可用于生成交互式图形。我们可以将它们导出到 HTML 文档中，并与具有 Web 浏览器的任何人共享。\n", + "# 当我们有兴趣在图形中查找事物并且希望能够放大并在图形中移动时，它是一个非常有用的库。或者，当我们想共享它们并给其他人探索数据的可能性时。\n", + "# 我们首先导入库并定义将要保存图形的文件：" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'h:\\\\Code\\\\collection_python\\\\data_visualization\\\\data_science_popularity.html'" + ] + }, + "metadata": {}, + "execution_count": 32 + } + ], + "source": [ + "p = figure(title='data science', x_axis_label='Mes', y_axis_label='data science')\n", + "p.line(df['Mes'], df['data science'], legend='popularity', line_width=2)\n", + "save(p)\n", + "# 我们绘制所需内容并将其保存在文件中：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'h:\\\\Code\\\\collection_python\\\\data_visualization\\\\multiple_graphs.html'" + ] + }, + "metadata": {}, + "execution_count": 33 + } + ], + "source": [ + "output_file('multiple_graphs.html')\n", + "s1 = figure(width=250, plot_height=250, title='data science')\n", + "s1.circle(df['Mes'], df['data science'], size=10, color='navy', alpha=0.5)\n", + "s2 = figure(width=250, height=250, x_range=s1.x_range, y_range=s1.y_range, title='machine learning') #share both axis range\n", + "s2.triangle(df['Mes'], df['machine learning'], size=10, color='red', alpha=0.5)\n", + "s3 = figure(width=250, height=250, x_range=s1.x_range, title='deep learning') #share only one axis range\n", + "s3.square(df['Mes'], df['deep learning'], size=5, color='green', alpha=0.5)\n", + "p = gridplot([[s1, s2, s3]])\n", + "save(p)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "import folium\n", + "m1 = folium.Map(location=[41.38, 2.17], tiles='openstreetmap', zoom_start=18)\n", + "m1.save('map1.html')\n", + "# Folium是一项研究，可以让我们绘制地图，标记，也可以在上面绘制数据。Folium让我们选择地图的提供者，这决定了地图的样式和质量。在本文中，为简单起见，我们仅将OpenStreetMap视为地图提供者。\n", + "# 使用地图非常复杂，值得一读。在这里，我们只是看一下基础知识，并用我们拥有的数据绘制几张地图。\n", + "# 让我们从基础开始，我们将绘制一个简单的地图，上面没有任何内容。" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "m2 = folium.Map(location=[41.38, 2.17], tiles='openstreetmap', zoom_start=16)\n", + "folium.Marker([41.38, 2.176], popup='<i>You can use whatever HTML code you want</i>', tooltip='click here').add_to(m2)\n", + "folium.Marker([41.38, 2.174], popup='<b>You can use whatever HTML code you want</b>', tooltip='dont click here').add_to(m2)\n", + "m2.save('map2.html')\n", + "# 我们为地图生成一个交互式文件，您可以在其中随意移动和缩放。\n", + "# 我们可以在地图上添加标记：" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "name": "python389jvsc74a57bd03b1827d4b6462cc460901af0bc0d075c933010817877a813d51f78a107cbf6e5", + "display_name": "Python 3.8.9 64-bit" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/data_visualization/temporal.csv b/data_visualization/temporal.csv new file mode 100644 index 0000000..744fd0a --- /dev/null +++ b/data_visualization/temporal.csv @@ -0,0 +1,195 @@ +Mes,data science,machine learning,deep learning,categorical +2004-01-01,12,18,4,1 +2004-02-01,12,21,2,1 +2004-03-01,9,21,2,1 +2004-04-01,10,16,4,1 +2004-05-01,7,14,3,1 +2004-06-01,9,17,3,1 +2004-07-01,9,16,3,1 +2004-08-01,7,14,3,1 +2004-09-01,10,17,4,1 +2004-10-01,8,17,4,1 +2004-11-01,8,15,2,1 +2004-12-01,8,16,2,1 +2005-01-01,10,16,2,0 +2005-02-01,9,14,3,0 +2005-03-01,8,13,2,0 +2005-04-01,8,12,3,0 +2005-05-01,8,15,3,0 +2005-06-01,8,12,3,0 +2005-07-01,6,11,2,0 +2005-08-01,9,10,2,0 +2005-09-01,7,14,2,0 +2005-10-01,7,16,3,0 +2005-11-01,7,13,4,0 +2005-12-01,5,13,2,0 +2006-01-01,7,12,3,0 +2006-02-01,7,12,3,0 +2006-03-01,7,12,2,0 +2006-04-01,7,12,2,0 +2006-05-01,7,11,2,0 +2006-06-01,6,10,2,0 +2006-07-01,5,9,1,0 +2006-08-01,6,9,2,0 +2006-09-01,7,11,1,0 +2006-10-01,6,11,2,0 +2006-11-01,7,11,2,0 +2006-12-01,4,9,3,0 +2007-01-01,6,11,2,0 +2007-02-01,7,11,3,0 +2007-03-01,6,9,3,0 +2007-04-01,5,10,3,0 +2007-05-01,6,9,1,0 +2007-06-01,6,9,2,0 +2007-07-01,4,9,2,0 +2007-08-01,5,9,3,0 +2007-09-01,6,9,2,0 +2007-10-01,6,11,2,0 +2007-11-01,6,10,2,0 +2007-12-01,5,8,3,0 +2008-01-01,6,9,2,1 +2008-02-01,6,9,2,1 +2008-03-01,5,8,3,1 +2008-04-01,6,10,2,1 +2008-05-01,5,9,2,1 +2008-06-01,5,8,2,1 +2008-07-01,5,7,3,1 +2008-08-01,5,7,1,1 +2008-09-01,6,9,2,1 +2008-10-01,6,10,2,1 +2008-11-01,6,9,2,1 +2008-12-01,5,8,2,1 +2009-01-01,6,8,3,0 +2009-02-01,6,9,3,0 +2009-03-01,6,8,2,0 +2009-04-01,6,9,3,0 +2009-05-01,5,8,2,0 +2009-06-01,5,8,2,0 +2009-07-01,4,8,2,0 +2009-08-01,5,8,2,0 +2009-09-01,7,8,3,0 +2009-10-01,6,9,2,0 +2009-11-01,7,9,2,0 +2009-12-01,6,8,2,0 +2010-01-01,7,8,2,0 +2010-02-01,7,9,2,0 +2010-03-01,6,9,2,0 +2010-04-01,6,9,2,0 +2010-05-01,6,8,2,0 +2010-06-01,5,8,2,0 +2010-07-01,5,8,3,0 +2010-08-01,5,8,2,0 +2010-09-01,7,9,2,0 +2010-10-01,6,9,3,0 +2010-11-01,6,9,3,0 +2010-12-01,5,9,2,0 +2011-01-01,7,8,3,0 +2011-02-01,6,9,2,0 +2011-03-01,6,9,3,0 +2011-04-01,6,9,2,0 +2011-05-01,5,9,3,0 +2011-06-01,5,8,2,0 +2011-07-01,4,7,2,0 +2011-08-01,5,8,2,0 +2011-09-01,7,11,3,0 +2011-10-01,7,12,3,0 +2011-11-01,7,12,3,0 +2011-12-01,6,10,2,0 +2012-01-01,7,10,3,1 +2012-02-01,7,11,3,1 +2012-03-01,7,11,2,1 +2012-04-01,6,11,3,1 +2012-05-01,6,11,4,1 +2012-06-01,6,10,3,1 +2012-07-01,6,10,3,1 +2012-08-01,7,10,3,1 +2012-09-01,10,12,3,1 +2012-10-01,8,13,4,1 +2012-11-01,8,12,6,1 +2012-12-01,6,11,4,1 +2013-01-01,8,11,5,0 +2013-02-01,8,12,4,0 +2013-03-01,8,12,5,0 +2013-04-01,9,13,6,0 +2013-05-01,9,13,7,0 +2013-06-01,8,12,5,0 +2013-07-01,8,12,6,0 +2013-08-01,9,11,6,0 +2013-09-01,12,13,8,0 +2013-10-01,11,16,7,0 +2013-11-01,11,15,8,0 +2013-12-01,10,14,8,0 +2014-01-01,13,14,9,0 +2014-02-01,12,16,10,0 +2014-03-01,13,16,10,0 +2014-04-01,13,17,10,0 +2014-05-01,12,16,11,0 +2014-06-01,12,15,10,0 +2014-07-01,13,16,9,0 +2014-08-01,16,16,11,0 +2014-09-01,19,18,12,0 +2014-10-01,17,18,12,0 +2014-11-01,17,20,11,0 +2014-12-01,16,18,17,0 +2015-01-01,19,20,19,0 +2015-02-01,18,21,20,0 +2015-03-01,18,21,21,0 +2015-04-01,20,24,19,0 +2015-05-01,19,22,20,0 +2015-06-01,20,24,23,0 +2015-07-01,20,24,23,0 +2015-08-01,23,23,22,0 +2015-09-01,27,26,24,0 +2015-10-01,25,27,24,0 +2015-11-01,24,30,28,0 +2015-12-01,22,28,28,0 +2016-01-01,27,28,34,1 +2016-02-01,27,32,34,1 +2016-03-01,26,34,44,1 +2016-04-01,27,34,44,1 +2016-05-01,27,34,42,1 +2016-06-01,27,36,46,1 +2016-07-01,26,32,40,1 +2016-08-01,29,34,41,1 +2016-09-01,36,39,46,1 +2016-10-01,33,42,52,1 +2016-11-01,34,46,55,1 +2016-12-01,33,46,59,1 +2017-01-01,41,51,65,0 +2017-02-01,42,56,69,0 +2017-03-01,44,59,75,0 +2017-04-01,42,61,76,0 +2017-05-01,43,63,80,0 +2017-06-01,43,65,78,0 +2017-07-01,42,62,76,0 +2017-08-01,47,64,84,0 +2017-09-01,53,71,87,0 +2017-10-01,53,75,89,0 +2017-11-01,52,80,94,0 +2017-12-01,48,72,91,0 +2018-01-01,58,76,91,0 +2018-02-01,57,80,93,0 +2018-03-01,58,82,95,0 +2018-04-01,58,82,95,0 +2018-05-01,59,83,98,0 +2018-06-01,57,81,90,0 +2018-07-01,60,81,88,0 +2018-08-01,64,82,87,0 +2018-09-01,68,85,94,0 +2018-10-01,70,90,97,0 +2018-11-01,67,88,94,0 +2018-12-01,61,81,87,0 +2019-01-01,76,86,93,0 +2019-02-01,77,93,95,0 +2019-03-01,75,91,98,0 +2019-04-01,77,92,98,0 +2019-05-01,76,92,97,0 +2019-06-01,80,93,96,0 +2019-07-01,85,92,96,0 +2019-08-01,88,93,92,0 +2019-09-01,95,100,100,0 +2019-10-01,90,98,98,0 +2019-11-01,87,97,96,0 +2019-12-01,81,89,91,0 +2020-01-01,94,94,93,1 +2020-02-01,100,99,99,1 diff --git a/date_time/date_range.py b/date_time/date_range.py new file mode 100644 index 0000000..f760e18 --- /dev/null +++ b/date_time/date_range.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from datetime import datetime, date, timedelta +import calendar + + +def get_month_range(start_date=None): + if start_date is None: + start_date = date.today().replace(day=1) + _, days_in_month = calendar.monthrange(start_date.year, start_date.month) + end_date = start_date + timedelta(days=days_in_month) + return start_date, end_date + + +def date_range(start, stop, step): + while start < stop: + yield start + start += step + + +if __name__ == '__main__': + a_day = timedelta(days=1) + now = datetime(2012, 4, 5).replace(day=1) + first_day, last_day = get_month_range(now) + for d in date_range(first_day, last_day, a_day): + print(d) diff --git a/date_time/get_date_from_timestamp.py b/date_time/get_date_from_timestamp.py index 30d49f1..00638b1 100755 --- a/date_time/get_date_from_timestamp.py +++ b/date_time/get_date_from_timestamp.py @@ -57,7 +57,7 @@ def days_from_now(timestamp): s = 1456415452 print(datestr_from_stamp(s)) date = datetime_from_timestamp(s) - print(now-date).days + print((now-date).days) def main(): @@ -66,6 +66,7 @@ def main(): except: timestamp = time.time() print(datetime_from_timestamp(timestamp)) + days_from_now(timestamp) if __name__ == '__main__': diff --git a/date_time/get_day_of_month.py b/date_time/get_day_of_month.py index f9384cc..b22f4d1 100644 --- a/date_time/get_day_of_month.py +++ b/date_time/get_day_of_month.py @@ -19,5 +19,5 @@ date_first = datetime.datetime(year , month, 1) date_last = datetime.datetime(year, month+1, 1) - datetime.timedelta(1) -print date_first -print date_last +print(date_first) +print(date_last) diff --git a/date_time/last_friday.py b/date_time/last_friday.py new file mode 100644 index 0000000..bef7128 --- /dev/null +++ b/date_time/last_friday.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +Topic: 最后的周五 +Desc : +""" +from datetime import datetime, timedelta +from dateutil.relativedelta import relativedelta +from dateutil.rrule import * + +weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', + 'Friday', 'Saturday', 'Sunday'] + + +def get_previous_byday(dayname, start_date=None): + """ + Given a day name, return the date of the previous day with that name + + :param dayname: The day of the week that you want to get the previous date for + :param start_date: The date to start looking back from. If not provided, the current date is used + """ + if start_date is None: + start_date = datetime.now() + day_num = start_date.weekday() + day_num_target = weekdays.index(dayname) + days_ago = (7 + day_num - day_num_target) % 7 + if days_ago == 0: + days_ago = 7 + return start_date - timedelta(days=days_ago) + + +def last_friday(): + """ + It output the date of the last Friday of the month + """ + print(datetime.now()) + print(get_previous_byday('Monday')) + print(get_previous_byday('Tuesday')) + print(get_previous_byday('Friday')) + print(get_previous_byday('Saturday')) + # 显式的传递开始日期 + print(get_previous_byday('Sunday', datetime(2012, 12, 21))) + + # 使用dateutil模块 + d = datetime.now() + # 下一个周五 + print(d + relativedelta(weekday=FR)) + # 上一个周五 + print(d + relativedelta(weekday=FR(-1))) + # 下一个周六， 为什么如果今天是周六，下一个/上一个都返回今天的日期？？ + print(d + relativedelta(weekday=SA)) + # 上一个周六 + print(d + relativedelta(weekday=SA(-1))) + + +if __name__ == '__main__': + last_friday() diff --git a/date_time/month.py b/date_time/month.py index 2fc9285..9789ff4 100644 --- a/date_time/month.py +++ b/date_time/month.py @@ -49,5 +49,5 @@ def test(): if __name__ == '__main__': test() - import doctest + # import doctest # doctest.testmod() diff --git a/date_time/to_timestamp.py b/date_time/to_timestamp.py index 91a8726..a11354a 100755 --- a/date_time/to_timestamp.py +++ b/date_time/to_timestamp.py @@ -23,7 +23,7 @@ def parse(date_str): def test(): s = "2015-10-18 00:00:00" - print to_timestamp(s) + print(to_timestamp(s)) diff --git a/decorator/class_decorator.py b/decorator/class_decorator.py new file mode 100644 index 0000000..329d98e --- /dev/null +++ b/decorator/class_decorator.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# 类装饰器 +import time + + +class deco_cls(object): + def __init__(self, func): + self._func = func + + def __call__(self, *args, **kwargs): + print("class decorator before function") + f = self._func(*args, **kwargs) + print("class decorator after function") + return f + + +@deco_cls +def do_something(a, b, c): + print(a) + time.sleep(1) + print(b) + time.sleep(1) + print(c) + return a + + +if __name__ == '__main__': + # 使用@ + f = do_something("1", "2", "3") + print(f) diff --git a/decorator/class_decorator_with_parameters.py b/decorator/class_decorator_with_parameters.py new file mode 100644 index 0000000..12f4619 --- /dev/null +++ b/decorator/class_decorator_with_parameters.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +class Decorator: + def __init__(self, arg1, arg2): + print('执行类Decorator的__init__()方法') + self.arg1 = arg1 + self.arg2 = arg2 + + def __call__(self, f): + print('执行类Decorator的__call__()方法') + + def wrap(*args): + print('执行wrap()') + print('装饰器参数：', self.arg1, self.arg2) + print('执行' + f.__name__ + '()') + f(*args) + print(f.__name__ + '()执行完毕') + + return wrap + + +@Decorator('Hello', 'World') +def example(a1, a2, a3): + print('传入example()的参数：', a1, a2, a3) + +if __name__ == '__main__': + print('装饰完毕') + print('准备调用example()') + example('Wish', 'Happy', 'EveryDay') + print('测试代码执行完毕') \ No newline at end of file diff --git a/decorator/debug.py b/decorator/debug.py new file mode 100644 index 0000000..3581b4c --- /dev/null +++ b/decorator/debug.py @@ -0,0 +1,18 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +def debug(func): + def wrapper(*args, **kwargs): + print(f"Debugging {func.__name__} - args: {args}, kwargs: {kwargs}") + return func(*args, **kwargs) + + return wrapper + + +@debug +def complex_data_processing(data, threshold=0.5): + # Your complex data processing code here + return data + + +if __name__ == "__main__": + print(complex_data_processing(1)) diff --git a/decorator/decorator_with_parameters.py b/decorator/decorator_with_parameters.py new file mode 100644 index 0000000..4789ee2 --- /dev/null +++ b/decorator/decorator_with_parameters.py @@ -0,0 +1,33 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# 带参数的装饰器 +import time + + +def logging(level): + def wrapper(func): + def inner_wrapper(*args, **kwargs): + print("[{level}]: enter function {func}()".format(level=level, func=func.__name__)) + f = func(*args, **kwargs) + print("after function: [{level}]: enter function {func}()".format(level=level, func=func.__name__)) + return f + + return inner_wrapper + + return wrapper + + +@logging(level="debug") +def do_something(a, b, c): + print(a) + time.sleep(1) + print(b) + time.sleep(1) + print(c) + return a + + +if __name__ == '__main__': + # 使用@ + f = do_something("1", "2", "3") + print(f) diff --git a/decorator/decorator_without_parameters.py b/decorator/decorator_without_parameters.py new file mode 100644 index 0000000..47c5865 --- /dev/null +++ b/decorator/decorator_without_parameters.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# 不带参数的装饰器 +import time + + +def deco_test(func): + def wrapper(*args, **kwargs): + print("before function") + f = func(*args, **kwargs) + print("after function") + return f + return wrapper + +def do_something(a,b,c): + print(a) + time.sleep(1) + print(b) + time.sleep(1) + print(c) + return a + +if __name__ == '__main__': + # 不用@ + f = deco_test(do_something)("1","2","3") + print(f) \ No newline at end of file diff --git a/decorator/decorator_without_parameters@.py b/decorator/decorator_without_parameters@.py new file mode 100644 index 0000000..fe5bd6f --- /dev/null +++ b/decorator/decorator_without_parameters@.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# 不带参数的装饰器 +import time + + +def deco_test(func): + def wrapper(*args, **kwargs): + print("before function") + f = func(*args, **kwargs) + print("after function") + return f + return wrapper + +@deco_test +def do_something(a,b,c): + print(a) + time.sleep(1) + print(b) + time.sleep(1) + print(c) + return a + +if __name__ == '__main__': + # 使用@ + f = do_something("1","2","3") + print(f) \ No newline at end of file diff --git a/decorator/deprecated.py b/decorator/deprecated.py new file mode 100644 index 0000000..9064d4b --- /dev/null +++ b/decorator/deprecated.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import warnings + + +def deprecated(func): + def wrapper(*args, **kwargs): + warnings.warn( + f"{func.__name__} is deprecated and will be removed in future versions.", + DeprecationWarning, + ) + return func(*args, **kwargs) + + return wrapper + + +@deprecated +def old_data_processing(data): + # Your old data processing code here + return data + + +if __name__ == "__main__": + print(old_data_processing("foo")) diff --git a/decorator/log_results.py b/decorator/log_results.py new file mode 100644 index 0000000..b1355d0 --- /dev/null +++ b/decorator/log_results.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +def log_results(func): + def wrapper(*args, **kwargs): + result = func(*args, **kwargs) + with open("results.log", "a") as log_file: + log_file.write(f"{func.__name__} - Result: {result}\n") + return result + + return wrapper + + +@log_results +def calculate_metrics(data): + # Your metric calculation code here + return data + + +if __name__ == "__main__": + calculate_metrics(123) diff --git a/decorator/memoize.py b/decorator/memoize.py new file mode 100644 index 0000000..0baa4ff --- /dev/null +++ b/decorator/memoize.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +def memoize(func): + cache = {} + + def wrapper(*args): + if args in cache: + return cache[args] + result = func(*args) + cache[args] = result + return result + + return wrapper + + +@memoize +def fibonacci(n): + if n <= 1: + return n + return fibonacci(n - 1) + fibonacci(n - 2) + + +if __name__ == "__main__": + print(fibonacci(2)) diff --git a/decorator/retry.py b/decorator/retry.py new file mode 100644 index 0000000..dac1107 --- /dev/null +++ b/decorator/retry.py @@ -0,0 +1,35 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import time +import requests + + +def retry(max_attempts, delay): + def decorator(func): + def wrapper(*args, **kwargs): + attempts = 0 + while attempts < max_attempts: + try: + return func(*args, **kwargs) + except Exception as e: + print( + f"Attempt {attempts + 1} failed. Retrying in {delay} seconds." + ) + attempts += 1 + time.sleep(delay) + raise Exception("Max retry attempts exceeded.") + + return wrapper + + return decorator + + +@retry(max_attempts=3, delay=2) +def fetch_data_from_api(api_url): + # Your API data fetching code here + response = requests.get(api_url) + return response.json() + + +if __name__ == "__main__": + print(fetch_data_from_api("https://www.google.com/")) diff --git a/decorator/suppress_errors.py b/decorator/suppress_errors.py new file mode 100644 index 0000000..ed30329 --- /dev/null +++ b/decorator/suppress_errors.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +def suppress_errors(func): + def wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + except Exception as e: + print(f"Error in {func.__name__}: {e}") + return None + + return wrapper + + +@suppress_errors +def preprocess_data(data): + # Your data preprocessing code here + return data / 0 + + +if __name__ == "__main__": + preprocess_data(123) diff --git a/decorator/timer.py b/decorator/timer.py new file mode 100644 index 0000000..54b96a8 --- /dev/null +++ b/decorator/timer.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import time + +def timer(func): + def wrapper(*args, **kwargs): + start_time=time.time() + result=func(*args, **kwargs) + end_time=time.time() + print(f'{func.__name__} took {end_time-start_time:.2f} seconds to execute.') + return result + return wrapper + +@timer +def my_func(): + time.sleep(3) + print('hello world') + return "ok" + + +if __name__ == '__main__': + print(my_func()) diff --git a/decorator/validate_input.py b/decorator/validate_input.py new file mode 100644 index 0000000..6bbb934 --- /dev/null +++ b/decorator/validate_input.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +''' +@File : validate_input.py +@Time : 2023/09/23 20:39:11 +@Author : jumploop +@Version : 1.0 +''' + + +def validate_input(func): + def wrapper(*args, **kwargs): + # your data validation logic here + if valid_date: + return func(*args, **kwargs) + else: + raise ValueError('Invalid data. Please check your inputs.') + + return wrapper + + +@validate_input +def my_func(data): + pass diff --git a/decorator/validate_output.py b/decorator/validate_output.py new file mode 100644 index 0000000..cc4ba94 --- /dev/null +++ b/decorator/validate_output.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + + +def valid_output(data): + return isinstance(data, int) + + +def validate_output(func): + def wrapper(*args, **kwargs): + result = func(*args, **kwargs) + if valid_output(result): + return result + else: + raise ValueError("Invalid output. Please check your function logic.") + + return wrapper + + +@validate_output +def clean_data(data): + # Your data cleaning code here + return data + + +if __name__ == "__main__": + clean_data("12") diff --git a/decorator/value_dispatch.py b/decorator/value_dispatch.py new file mode 100644 index 0000000..686af76 --- /dev/null +++ b/decorator/value_dispatch.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +# +# This source file is part of the EdgeDB open source project. +# +# Copyright 2021-present MagicStack Inc. and the EdgeDB authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +import functools + + +def value_dispatch(func): + """Like singledispatch() but dispatches by value of the first arg. + Example: + @value_dispatch + def eat(fruit): + return f"I don't want a {fruit}..." + @eat.register('apple') + def _eat_apple(fruit): + return "I love apples!" + @eat.register('eggplant') + @eat.register('squash') + def _eat_what(fruit): + return f"I didn't know {fruit} is a fruit!" + An alternative to applying multuple `register` decorators is to + use the `register_for_all` helper: + @eat.register_for_all({'eggplant', 'squash'}) + def _eat_what(fruit): + return f"I didn't know {fruit} is a fruit!" + """ + + registry = {} + + @functools.wraps(func) + def wrapper(arg0, *args, **kwargs): + try: + delegate = registry[arg0] + except KeyError: + pass + else: + return delegate(arg0, *args, **kwargs) + + return func(arg0, *args, **kwargs) + + def register(value): + def wrap(func): + if value in registry: + raise ValueError( + f'@value_dispatch: there is already a handler ' + f'registered for {value!r}' + ) + registry[value] = func + return func + return wrap + + def register_for_all(values): + def wrap(func): + for value in values: + if value in registry: + raise ValueError( + f'@value_dispatch: there is already a handler ' + f'registered for {value!r}' + ) + registry[value] = func + return func + return wrap + + wrapper.register = register + wrapper.register_for_all = register_for_all + return wrapper diff --git a/decorator/visualize_results.py b/decorator/visualize_results.py new file mode 100644 index 0000000..ff36c19 --- /dev/null +++ b/decorator/visualize_results.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + + +def visualize_results(func): + def wrapper(*args, **kwargs): + result = func(*args, **kwargs) + plt.figure() + # Your visualization code here + plt.plot(result) + plt.show() + return result + + return wrapper + + +@visualize_results +def analyze_and_visualize(data): + # Your combined analysis and visualization code here + data_DataFrame = pd.DataFrame( + {"x1": np.random.rand(10), "x2": np.random.rand(10), "x3": np.random.rand(10)} + ) + return data_DataFrame + + +if __name__ == "__main__": + print(analyze_and_visualize("data")) diff --git a/design_pattern/singlegon.py b/design_pattern/singlegon.py index a8f0cdf..f8652cc 100644 --- a/design_pattern/singlegon.py +++ b/design_pattern/singlegon.py @@ -4,6 +4,7 @@ class Singleton1(object): """实现方式1：使用__new__""" + def __new__(cls, *args, **kwargs): if not hasattr(cls, '_instance'): orig = super(Singleton1, cls) @@ -31,6 +32,7 @@ def getinstance(): if cls not in instances: instances[cls] = cls(*args, **kwargs) return instances[cls] + return getinstance @@ -61,3 +63,7 @@ def test_singleton(): s2 = Singleton() assert id(s1) == id(s2) assert s1 is s2 + + +if __name__ == '__main__': + test_singleton() diff --git a/dictionary/index_default.py b/dictionary/index_default.py new file mode 100644 index 0000000..8d2764c --- /dev/null +++ b/dictionary/index_default.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +"""创建一个从单词到其出现情况的映射""" + +import sys +import re +import collections +WORD_RE = re.compile(r'\w+') + +index = collections.defaultdict(list) +with open(sys.argv[1], encoding='utf-8') as fp: + for line_no, line in enumerate(fp, 1): + for match in WORD_RE.finditer(line): + word = match.group() + column_no = match.start()+1 + location = (line_no, column_no) + index[word].append(location) +# 以字母顺序打印出结果 +for word in sorted(index, key=str.upper): + print(word, index[word]) diff --git a/downloader/downloader_with_progress_bar.py b/downloader/downloader_with_progress_bar.py new file mode 100644 index 0000000..3c6d510 --- /dev/null +++ b/downloader/downloader_with_progress_bar.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from __future__ import annotations +# 用于显示进度条 +from tqdm import tqdm +# 用于发起网络请求 +import requests +# 用于多线程操作 +import multitasking +import signal +# 导入 retry 库以方便进行下载出错重试 +from retry import retry +signal.signal(signal.SIGINT, multitasking.killall) + +# 请求头 +headers = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36 QIHU 360SE' +} +# 定义 1 MB 多少为 B(字节) +MB = 1024**2 +# 1KB=1024B +# 1M=1024KB +# 1G=1024MB +# 1T=1024GB + +def split_file(start: int, end: int, step: int) -> list: + # 分多块 + parts = [(start, min(start+step, end)) + for start in range(0, end, step)] + # 看最后那一部分的最后一个数字是否等于总大小，不等于就新加一块 + if parts[-1][-1] != end: + start = step*len(parts) + parts.append((start, end)) + return parts + + +def get_file_size(url: str, raise_error: bool = False) -> int: + ''' + 获取文件大小 + + Parameters + ---------- + url : 文件直链 + raise_error : 如果无法获取文件大小，是否引发错误 + + Return + ------ + 文件大小（B为单位） + 如果不支持则会报错 + + ''' + response = requests.head(url) + file_size = response.headers.get('Content-Length') + if file_size is None: + if raise_error is True: + raise ValueError('该文件不支持多线程分段下载！') + return file_size + return int(file_size) + + +def download(url: str, file_name: str, retry_times: int = 3, each_size=16*MB) -> None: + ''' + 根据文件直链和文件名下载文件 + + Parameters + ---------- + url : 文件直链 + file_name : 文件名 + retry_times: 可选的，每次连接失败重试次数 + Return + ------ + None + + ''' + f = open(file_name, 'wb') + file_size = get_file_size(url) + + @retry(tries=retry_times) + @multitasking.task + def start_download(start: int, end: int) -> None: + ''' + 根据文件起止位置下载文件 + + Parameters + ---------- + start : 开始位置 + end : 结束位置 + ''' + _headers = headers.copy() + # 分段下载的核心 + _headers['Range'] = f'bytes={start}-{end}' + # 发起请求并获取响应（流式） + response = session.get(url, headers=_headers, stream=True) + # 每次读取的流式响应大小 + chunk_size = 128 + # 暂存已获取的响应，后续循环写入 + chunks = [] + for chunk in response.iter_content(chunk_size=chunk_size): + # 暂存获取的响应 + chunks.append(chunk) + # 更新进度条 + bar.update(chunk_size) + f.seek(start) + for chunk in chunks: + f.write(chunk) + # 释放已写入的资源 + del chunks + + session = requests.Session() + # 分块文件如果比文件大，就取文件大小为分块大小 + each_size = min(each_size, file_size) + + # 分块 + parts = split_file(0, file_size, each_size) + print(f'分块数：{len(parts)}') + # 创建进度条 + bar = tqdm(total=file_size, desc=f'下载文件：{file_name}') + for part in parts: + start, end = part + start_download(start, end) + # 等待全部线程结束 + multitasking.wait_for_tasks() + f.close() + bar.close() + + +if "__main__" == __name__: + # url = 'https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/0d/ea/f936c14b6e886221e53354e1992d0c4e0eb9566fcc70201047bb664ce777/tensorflow-2.3.1-cp37-cp37m-macosx_10_9_x86_64.whl#sha256=1f72edee9d2e8861edbb9e082608fd21de7113580b3fdaa4e194b472c2e196d0' + url = 'https://issuecdn.baidupcs.com/issue/netdisk/yunguanjia/BaiduNetdisk_7.2.8.9.exe' + file_name = 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+51.02402164776596,75.56704412296436,-54.335985003268995 +28.242979630698915,70.7331743911799,-70.3595025944615 +6.259489674932251,64.37502311035088,-5.383535176971577 +64.40228052037071,96.08530832379498,-15.453577307826393 +49.020311442176755,20.593416441008138,-10.529659126575952 +59.54992904023288,44.54563349100389,-72.72380892088538 +85.48659523851269,5.359131046817023,-31.46213639191111 +93.68816598583786,40.63787570837282,-46.460656961356406 diff --git a/excel/data/meteo_china_tmin_2018.xlsx b/excel/data/meteo_china_tmin_2018.xlsx new file mode 100644 index 0000000..a8948f0 Binary files /dev/null and b/excel/data/meteo_china_tmin_2018.xlsx differ diff --git a/excel/excel_demo.py b/excel/excel_demo.py new file mode 100644 index 0000000..ea3d6d4 --- /dev/null +++ b/excel/excel_demo.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# 1.用xlwings打开工作簿 +import xlwings as xw + +app = xw.App(visible=True, add_book=False) +workbook = app.books.open(r'data.xlsx') +sheet = workbook.sheets[0] # 选中第一个表格 + + +def exceL(sheet): + # 2.循环每行的数据 + info = sheet.used_range + # nrows = info.last_cell.row + # ncols = info.last_cell.column + list_cell = ['B1', 'D1', 'F1', 'H1', 'B2', 'D2', 'F2', 'H2'] + for i in info.raw_value[1:]: + print(i) + app = xw.App(visible=True, add_book=False) + workbook = app.books.open(r'template.xlsx') + sheet = workbook.sheets[0] + sheet['B1'].value = i[0] + sheet['D1'].value = i[1] + sheet['F1'].value = i[8] + sheet['H1'].value = i[2] + sheet['B2'].value = i[9] + sheet['D2'].value = i[5] + sheet['F2'].value = i[6] + sheet['H2'].value = i[7] + # 4.设置单元格格式 + for j in list_cell: + sheet[j].api.Font.Name = '楷体' # 设置字体 + sheet[j].api.Font.Size = 14 # 设置字号 + # 设置文本水平对齐方式为居中 + sheet[j].expand('table').api.HorizontalAlignment = xw.constants.HAlign.xlHAlignCenter + # 设置文本水平对齐方式为居中 + sheet[j].expand('table').api.VerticalAlignment = xw.constants.VAlign.xlVAlignCenter + workbook.save(r'data\{}.xlsx'.format(i[0])) # 以名字命名 + workbook.close() + app.quit() + +if __name__ == '__main__': + exceL(sheet) + workbook.close() + app.quit() \ No newline at end of file diff --git a/excel/excel_operating.py b/excel/excel_operating.py new file mode 100644 index 0000000..a61de16 --- /dev/null +++ b/excel/excel_operating.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import openpyxl + + +class ExcelOperate(object): + def __init__(self, file): + self.file = file + self.wb = openpyxl.load_workbook(self.file) + sheets = self.wb.sheetnames + self.sheet = sheets[0] + self.ws = self.wb[self.sheet] + + # 获取表格的总行数和总列数 + def get_row_clo_num(self): + rows = self.ws.max_row + columns = self.ws.max_column + return rows, columns + + # 获取某个单元格的值 + def get_cell_value(self, row, column): + cell_value = self.ws.cell(row=row, column=column).value + return cell_value + + # 获取某列的所有值 + def get_col_value(self, column): + rows = self.ws.max_row + column_data = [] + for i in range(1, rows + 1): + cell_value = self.ws.cell(row=i, column=column).value + column_data.append(cell_value) + return column_data + + # 获取某行所有值 + def get_row_value(self, row): + columns = self.ws.max_column + row_data = [] + for i in range(1, columns + 1): + cell_value = self.ws.cell(row=row, column=i).value + row_data.append(cell_value) + return row_data + + # 设置某个单元格的值 + def set_cell_value(self, row, colunm, cellvalue): + try: + self.ws.cell(row=row, column=colunm).value = cellvalue + self.wb.save(self.file) + except: + self.ws.cell(row=row, column=colunm).value = "writefail" + self.wb.save(self.file) + + +if __name__ == '__main__': + ep = ExcelOperate('F:\Code\day11\data.xlsx') + data = ep.get_col_value(1) + print(data) diff --git "a/excel/excel\346\212\245\350\241\250/README.md" "b/excel/excel\346\212\245\350\241\250/README.md" new file mode 100644 index 0000000..c96babd --- /dev/null +++ "b/excel/excel\346\212\245\350\241\250/README.md" @@ -0,0 +1,13 @@ +# 太强了，用 Python 自动生成 Excel 数据报表！ + +之前明哥分享过不少关于Python自动化操作Excel的文章，大家都挺喜欢的。 + +所以今天就带大家来实战一波，使用Python自动化生成数据报表！ + +从一条条的数据中，创建出一张数据报表，得出你想要的东西，提高效率。 + +主要使用到pandas、xlwings以及matplotlib这几个库。 + +[Python编程时光](https://mp.weixin.qq.com/s?__biz=MzIzMzMzOTI3Nw==&mid=2247499921&idx=2&sn=e136b01842348585805dd6239f5ab9e2&chksm=e885a073dff229659297845b077792d7c9b18a3657ac7dbeaa75f1afbf989c991a1e0932d999&scene=21#wechat_redirect +) + diff --git "a/excel/excel\346\212\245\350\241\250/fruit_and_veg_sales.csv" "b/excel/excel\346\212\245\350\241\250/fruit_and_veg_sales.csv" new file mode 100644 index 0000000..aebb499 --- /dev/null +++ "b/excel/excel\346\212\245\350\241\250/fruit_and_veg_sales.csv" @@ -0,0 +1,1001 @@ +交易编号,类别,销售日期,销售数量,平均价格(美元),平均成本(美元),总收入(美元),总成本(美元),总利润(美元) +498,橙子,26/06/2020,1217,1.09,0.61,1326.53,742.37,584.16 +884,洋葱,16/11/2020,1215,4.19,1.01,5090.85,1227.15,3863.7 +285,葡萄,18/04/2020,1212,2.54,1.85,3078.48,2242.2,836.28 +380,葡萄,18/05/2020,1211,1.52,1.1,1840.72,1332.1,508.62 +661,苹果,31/08/2020,1211,1.24,0.5,1501.64,605.5,896.14 +657,土豆,30/08/2020,1210,1.24,0.4,1500.4,484,1016.4 +42,苹果,20/01/2020,1209,1.09,0.79,1317.81,955.11,362.7 +188,土豆,10/03/2020,1208,4.34,2.46,5242.72,2971.68,2271.04 +256,西葫芦,07/04/2020,1208,2.54,1.02,3068.32,1232.16,1836.16 +501,西葫芦,29/06/2020,1208,4.34,1.05,5242.72,1268.4,3974.32 +169,胡萝卜,04/03/2020,1207,6.47,1.57,7809.29,1894.99,5914.3 +660,苹果,31/08/2020,1207,0.9,0.51,1086.3,615.57,470.73 +904,葡萄,24/11/2020,1207,0.9,0.21,1086.3,253.47,832.83 +845,橙子,03/11/2020,1206,0.31,0.12,373.86,144.72,229.14 +13,土豆,04/01/2020,1205,6.64,3.76,8001.2,4530.8,3470.4 +79,土豆,01/02/2020,1203,1.09,0.79,1311.27,950.37,360.9 +88,土豆,03/02/2020,1203,1.03,0.33,1239.09,396.99,842.1 +777,土豆,11/10/2020,1202,1.52,0.49,1827.04,588.98,1238.06 +189,橙子,10/03/2020,1201,6.47,3.14,7770.47,3771.14,3999.33 +214,胡萝卜,20/03/2020,1200,4.34,2.81,5208,3372,1836 +263,胡萝卜,09/04/2020,1198,6.64,0.53,7954.72,634.94,7319.78 +595,葡萄,05/08/2020,1198,0.52,0.16,622.96,191.68,431.28 +760,橙子,05/10/2020,1196,1.53,0.61,1829.88,729.56,1100.32 +105,葡萄,09/02/2020,1194,6.64,3.76,7928.16,4489.44,3438.72 +254,葡萄,06/04/2020,1194,1.09,0.61,1301.46,728.34,573.12 +623,洋葱,16/08/2020,1193,0.31,0.05,369.83,59.65,310.18 +928,洋葱,01/12/2020,1193,6.64,2.69,7921.52,3209.17,4712.35 +754,西葫芦,04/10/2020,1192,0.69,0.22,822.48,262.24,560.24 +287,洋葱,18/04/2020,1188,4.34,1.4,5155.92,1663.2,3492.72 +466,西葫芦,17/06/2020,1188,0.52,0.33,617.76,392.04,225.72 +543,葡萄,16/07/2020,1188,0.9,0.29,1069.2,344.52,724.68 +389,苹果,20/05/2020,1183,0.9,0.29,1064.7,343.07,721.63 +571,土豆,26/07/2020,1183,1.09,0.52,1289.47,615.16,674.31 +964,西葫芦,18/12/2020,1182,0.69,0.27,815.58,319.14,496.44 +995,西葫芦,30/12/2020,1182,2.05,1.16,2423.1,1371.12,1051.98 +167,胡萝卜,03/03/2020,1179,6.47,3.14,7628.13,3702.06,3926.07 +334,橙子,03/05/2020,1178,2.54,0.41,2992.12,482.98,2509.14 +342,石榴,05/05/2020,1178,4.34,2.11,5112.52,2485.58,2626.94 +782,西葫芦,13/10/2020,1178,1.45,0.47,1708.1,553.66,1154.44 +429,洋葱,04/06/2020,1177,0.52,0.04,612.04,47.08,564.96 +819,橙子,25/10/2020,1177,0.31,0.12,364.87,141.24,223.63 +726,洋葱,23/09/2020,1175,1.53,0.61,1797.75,716.75,1081 +985,石榴,26/12/2020,1172,4.19,0.67,4910.68,785.24,4125.44 +390,西葫芦,21/05/2020,1170,4.34,3.16,5077.8,3697.2,1380.6 +759,苹果,05/10/2020,1170,0.69,0.11,807.3,128.7,678.6 +776,西葫芦,11/10/2020,1167,4.19,1.69,4889.73,1972.23,2917.5 +926,苹果,01/12/2020,1166,0.73,0.47,851.18,548.02,303.16 +449,土豆,12/06/2020,1165,4.19,1.01,4881.35,1176.65,3704.7 +178,胡萝卜,07/03/2020,1163,1.53,0.12,1779.39,139.56,1639.83 +778,橙子,12/10/2020,1161,6.47,4.19,7511.67,4864.59,2647.08 +628,苹果,18/08/2020,1160,2.05,0.49,2378,568.4,1809.6 +667,胡萝卜,02/09/2020,1160,6.47,2.62,7505.2,3039.2,4466 +539,洋葱,14/07/2020,1157,6.64,0.53,7682.48,613.21,7069.27 +560,石榴,20/07/2020,1156,1.11,0.8,1283.16,924.8,358.36 +114,葡萄,13/02/2020,1151,0.69,0.16,794.19,184.16,610.03 +830,橙子,28/10/2020,1147,0.69,0.39,791.43,447.33,344.1 +520,苹果,07/07/2020,1146,1.53,1.11,1753.38,1272.06,481.32 +201,葡萄,16/03/2020,1144,1.45,0.23,1658.8,263.12,1395.68 +927,葡萄,01/12/2020,1144,0.69,0.44,789.36,503.36,286 +337,西葫芦,04/05/2020,1142,4.34,2.81,4956.28,3209.02,1747.26 +442,土豆,09/06/2020,1142,4.19,1.69,4784.98,1929.98,2855 +952,洋葱,11/12/2020,1141,6.64,3.22,7576.24,3674.02,3902.22 +670,胡萝卜,03/09/2020,1140,1.11,0.8,1265.4,912,353.4 +723,洋葱,22/09/2020,1139,4.19,2.03,4772.41,2312.17,2460.24 +607,土豆,08/08/2020,1137,4.19,0.67,4764.03,761.79,4002.24 +652,西葫芦,28/08/2020,1137,4.19,3.05,4764.03,3467.85,1296.18 +574,胡萝卜,27/07/2020,1133,6.47,3.67,7330.51,4158.11,3172.4 +853,西葫芦,08/11/2020,1133,1.52,1.1,1722.16,1246.3,475.86 +842,洋葱,01/11/2020,1132,6.64,4.3,7516.48,4867.6,2648.88 +562,胡萝卜,20/07/2020,1131,2.05,1.32,2318.55,1492.92,825.63 +849,土豆,07/11/2020,1129,2.54,1.02,2867.66,1151.58,1716.08 +416,洋葱,29/05/2020,1127,1.24,0.2,1397.48,225.4,1172.08 +576,橙子,27/07/2020,1126,1.09,0.35,1227.34,394.1,833.24 +77,橙子,31/01/2020,1125,2.54,1.44,2857.5,1620,1237.5 +451,胡萝卜,13/06/2020,1125,6.47,1.04,7278.75,1170,6108.75 +718,苹果,19/09/2020,1122,6.64,0.53,7450.08,594.66,6855.42 +139,葡萄,20/02/2020,1120,0.31,0.22,347.2,246.4,100.8 +507,苹果,02/07/2020,1119,4.19,1.69,4688.61,1891.11,2797.5 +533,葡萄,10/07/2020,1119,0.9,0.07,1007.1,78.33,928.77 +735,苹果,27/09/2020,1117,1.03,0.66,1150.51,737.22,413.29 +385,苹果,19/05/2020,1112,4.34,1.4,4826.08,1556.8,3269.28 +953,苹果,12/12/2020,1112,6.64,1.07,7383.68,1189.84,6193.84 +550,西葫芦,18/07/2020,1111,6.64,1.07,7377.04,1188.77,6188.27 +96,西葫芦,05/02/2020,1110,1.53,0.99,1698.3,1098.9,599.4 +806,西葫芦,20/10/2020,1109,2.05,1.16,2273.45,1286.44,987.01 +97,土豆,05/02/2020,1107,1.09,0.79,1206.63,874.53,332.1 +554,胡萝卜,19/07/2020,1107,1.09,0.17,1206.63,188.19,1018.44 +837,苹果,30/10/2020,1103,6.47,0.52,7136.41,573.56,6562.85 +156,橙子,27/02/2020,1102,2.05,1.16,2259.1,1278.32,980.78 +672,胡萝卜,03/09/2020,1102,6.64,1.07,7317.28,1179.14,6138.14 +123,石榴,16/02/2020,1101,4.19,2.03,4613.19,2235.03,2378.16 +608,土豆,08/08/2020,1100,4.34,3.16,4774,3476,1298 +92,橙子,04/02/2020,1098,4.34,0.35,4765.32,384.3,4381.02 +311,洋葱,25/04/2020,1095,1.24,0.9,1357.8,985.5,372.3 +987,胡萝卜,27/12/2020,1094,2.05,0.49,2242.7,536.06,1706.64 +998,洋葱,01/01/2021,1094,6.64,4.84,7264.16,5294.96,1969.2 +52,苹果,23/01/2020,1093,2.05,0.99,2240.65,1082.07,1158.58 +640,石榴,23/08/2020,1093,4.34,0.7,4743.62,765.1,3978.52 +208,葡萄,18/03/2020,1091,1.24,0.6,1352.84,654.6,698.24 +873,西葫芦,13/11/2020,1090,0.9,0.36,981,392.4,588.6 +832,西葫芦,29/10/2020,1089,6.64,2.69,7230.96,2929.41,4301.55 +26,橙子,13/01/2020,1087,2.05,0.33,2228.35,358.71,1869.64 +980,胡萝卜,24/12/2020,1087,1.52,0.86,1652.24,934.82,717.42 +710,石榴,16/09/2020,1085,1.53,0.12,1660.05,130.2,1529.85 +473,洋葱,21/06/2020,1084,0.9,0.65,975.6,704.6,271 +902,橙子,23/11/2020,1084,2.54,1.02,2753.36,1105.68,1647.68 +234,洋葱,27/03/2020,1083,6.47,1.04,7007.01,1126.32,5880.69 +827,葡萄,28/10/2020,1083,4.19,1.35,4537.77,1462.05,3075.72 +744,葡萄,29/09/2020,1081,6.47,3.67,6994.07,3967.27,3026.8 +481,苹果,23/06/2020,1079,0.73,0.41,787.67,442.39,345.28 +687,西葫芦,09/09/2020,1078,2.54,0.82,2738.12,883.96,1854.16 +434,土豆,06/06/2020,1074,6.47,0.52,6948.78,558.48,6390.3 +678,苹果,05/09/2020,1074,4.34,1.05,4661.16,1127.7,3533.46 +685,苹果,08/09/2020,1071,2.54,0.41,2720.34,439.11,2281.23 +898,洋葱,22/11/2020,1070,6.47,3.67,6922.9,3926.9,2996 +455,胡萝卜,14/06/2020,1069,4.19,1.35,4479.11,1443.15,3035.96 +857,橙子,09/11/2020,1069,1.52,0.49,1624.88,523.81,1101.07 +329,西葫芦,01/05/2020,1068,1.24,0.7,1324.32,747.6,576.72 +796,洋葱,17/10/2020,1068,0.69,0.33,736.92,352.44,384.48 +81,葡萄,01/02/2020,1067,2.54,0.61,2710.18,650.87,2059.31 +528,葡萄,09/07/2020,1067,1.11,0.08,1184.37,85.36,1099.01 +791,石榴,16/10/2020,1067,6.64,4.3,7084.88,4588.1,2496.78 +41,西葫芦,18/01/2020,1066,1.53,0.99,1630.98,1055.34,575.64 +577,橙子,27/07/2020,1062,4.34,2.11,4609.08,2240.82,2368.26 +551,土豆,18/07/2020,1061,1.45,0.94,1538.45,997.34,541.11 +63,土豆,27/01/2020,1059,0.9,0.21,953.1,222.39,730.71 +914,石榴,27/11/2020,1058,1.11,0.44,1174.38,465.52,708.86 +200,洋葱,15/03/2020,1054,1.09,0.7,1148.86,737.8,411.06 +216,橙子,21/03/2020,1051,6.64,1.61,6978.64,1692.11,5286.53 +925,西葫芦,01/12/2020,1049,1.52,0.36,1594.48,377.64,1216.84 +22,石榴,10/01/2020,1048,0.52,0.21,544.96,220.08,324.88 +277,洋葱,16/04/2020,1048,1.09,0.17,1142.32,178.16,964.16 +486,西葫芦,24/06/2020,1047,0.9,0.65,942.3,680.55,261.75 +951,土豆,11/12/2020,1047,1.45,0.82,1518.15,858.54,659.61 +973,石榴,21/12/2020,1047,2.54,1.64,2659.38,1717.08,942.3 +135,西葫芦,19/02/2020,1046,2.05,0.49,2144.3,512.54,1631.76 +297,葡萄,20/04/2020,1044,1.11,0.62,1158.84,647.28,511.56 +905,石榴,24/11/2020,1043,2.54,1.23,2649.22,1282.89,1366.33 +755,苹果,04/10/2020,1042,6.64,4.3,6918.88,4480.6,2438.28 +523,胡萝卜,08/07/2020,1040,6.47,1.57,6728.8,1632.8,5096 +530,苹果,10/07/2020,1039,4.34,1.75,4509.26,1818.25,2691.01 +67,土豆,29/01/2020,1038,1.45,0.94,1505.1,975.72,529.38 +826,苹果,27/10/2020,1037,1.52,1.1,1576.24,1140.7,435.54 +668,石榴,02/09/2020,1036,2.54,0.41,2631.44,424.76,2206.68 +39,葡萄,18/01/2020,1034,0.69,0.05,713.46,51.7,661.76 +271,橙子,13/04/2020,1034,6.47,4.19,6689.98,4332.46,2357.52 +57,西葫芦,25/01/2020,1032,1.53,0.99,1578.96,1021.68,557.28 +461,洋葱,15/06/2020,1031,0.31,0.12,319.61,123.72,195.89 +93,苹果,05/02/2020,1028,4.34,1.05,4461.52,1079.4,3382.12 +107,洋葱,10/02/2020,1028,1.11,0.71,1141.08,729.88,411.2 +580,苹果,28/07/2020,1026,6.47,4.19,6638.22,4298.94,2339.28 +164,石榴,02/03/2020,1023,6.47,0.52,6618.81,531.96,6086.85 +720,土豆,20/09/2020,1022,1.09,0.7,1113.98,715.4,398.58 +362,土豆,11/05/2020,1021,4.34,1.75,4431.14,1786.75,2644.39 +260,苹果,09/04/2020,1020,2.05,0.16,2091,163.2,1927.8 +281,土豆,16/04/2020,1020,0.9,0.29,918,295.8,622.2 +480,石榴,23/06/2020,1019,4.34,2.46,4422.46,2506.74,1915.72 +493,橙子,26/06/2020,1019,1.52,0.36,1548.88,366.84,1182.04 +617,西葫芦,12/08/2020,1018,1.53,0.49,1557.54,498.82,1058.72 +54,苹果,24/01/2020,1016,2.05,0.83,2082.8,843.28,1239.52 +230,洋葱,25/03/2020,1015,2.05,0.16,2080.75,162.4,1918.35 +544,苹果,16/07/2020,1014,0.69,0.11,699.66,111.54,588.12 +585,橙子,30/07/2020,1012,4.19,0.67,4240.28,678.04,3562.24 +641,葡萄,24/08/2020,1012,1.53,0.49,1548.36,495.88,1052.48 +3,西葫芦,02/01/2020,1011,1.53,0.12,1546.83,121.32,1425.51 +730,土豆,24/09/2020,1011,2.05,0.49,2072.55,495.39,1577.16 +833,石榴,29/10/2020,1011,4.34,1.05,4387.74,1061.55,3326.19 +102,西葫芦,07/02/2020,1010,1.03,0.33,1040.3,333.3,707 +172,石榴,04/03/2020,1009,6.47,4.71,6528.23,4752.39,1775.84 +702,土豆,13/09/2020,1008,1.52,1.1,1532.16,1108.8,423.36 +986,洋葱,26/12/2020,1008,1.11,0.8,1118.88,806.4,312.48 +309,橙子,23/04/2020,1007,4.34,1.75,4370.38,1762.25,2608.13 +843,胡萝卜,02/11/2020,1007,1.45,0.82,1460.15,825.74,634.41 +316,洋葱,26/04/2020,1005,1.03,0.5,1035.15,502.5,532.65 +411,西葫芦,28/05/2020,1005,0.73,0.17,733.65,170.85,562.8 +885,胡萝卜,16/11/2020,1005,1.24,0.1,1246.2,100.5,1145.7 +515,西葫芦,05/07/2020,1004,0.52,0.21,522.08,210.84,311.24 +681,石榴,05/09/2020,1002,1.09,0.35,1092.18,350.7,741.48 +622,苹果,15/08/2020,1001,1.52,0.86,1521.52,860.86,660.66 +408,西葫芦,26/05/2020,998,4.34,0.7,4331.32,698.6,3632.72 +447,橙子,12/06/2020,998,2.54,1.23,2534.92,1227.54,1307.38 +218,石榴,24/03/2020,997,4.19,0.67,4177.43,667.99,3509.44 +856,西葫芦,09/11/2020,996,6.47,3.14,6444.12,3127.44,3316.68 +871,土豆,12/11/2020,995,1.45,0.94,1442.75,935.3,507.45 +353,橙子,09/05/2020,993,0.69,0.22,685.17,218.46,466.71 +594,葡萄,04/08/2020,992,4.19,1.69,4156.48,1676.48,2480 +869,土豆,12/11/2020,992,2.05,1.16,2033.6,1150.72,882.88 +435,西葫芦,07/06/2020,990,4.19,1.69,4148.1,1673.1,2475 +420,西葫芦,31/05/2020,989,6.47,1.04,6398.83,1028.56,5370.27 +798,土豆,18/10/2020,989,4.34,2.11,4292.26,2086.79,2205.47 +210,土豆,19/03/2020,986,2.54,0.41,2504.44,404.26,2100.18 +739,石榴,28/09/2020,984,4.19,1.01,4122.96,993.84,3129.12 +433,胡萝卜,05/06/2020,982,1.53,0.99,1502.46,972.18,530.28 +134,土豆,18/02/2020,980,4.34,1.4,4253.2,1372,2881.2 +604,胡萝卜,07/08/2020,980,4.19,0.67,4106.2,656.6,3449.6 +662,土豆,01/09/2020,980,2.54,1.02,2489.2,999.6,1489.6 +810,胡萝卜,20/10/2020,980,1.52,0.98,1489.6,960.4,529.2 +835,胡萝卜,29/10/2020,979,1.52,0.36,1488.08,352.44,1135.64 +514,葡萄,05/07/2020,976,1.45,0.82,1415.2,800.32,614.88 +697,土豆,10/09/2020,976,6.64,1.61,6480.64,1571.36,4909.28 +194,苹果,14/03/2020,975,1.53,0.61,1491.75,594.75,897 +237,土豆,30/03/2020,975,1.24,0.4,1209,390,819 +900,葡萄,23/11/2020,975,6.64,2.69,6474,2622.75,3851.25 +982,葡萄,24/12/2020,975,2.54,1.44,2476.5,1404,1072.5 +824,土豆,27/10/2020,974,1.53,0.24,1490.22,233.76,1256.46 +684,葡萄,06/09/2020,972,6.47,4.71,6288.84,4578.12,1710.72 +889,石榴,18/11/2020,971,4.34,1.4,4214.14,1359.4,2854.74 +322,橙子,27/04/2020,970,0.31,0.1,300.7,97,203.7 +646,洋葱,26/08/2020,969,2.54,1.44,2461.26,1395.36,1065.9 +275,苹果,15/04/2020,968,1.53,0.86,1481.04,832.48,648.56 +280,西葫芦,16/04/2020,967,0.9,0.43,870.3,415.81,454.49 +192,西葫芦,13/03/2020,966,0.31,0.17,299.46,164.22,135.24 +305,苹果,21/04/2020,966,1.52,0.86,1468.32,830.76,637.56 +394,苹果,22/05/2020,966,2.54,0.61,2453.64,589.26,1864.38 +148,西葫芦,24/02/2020,964,1.45,0.58,1397.8,559.12,838.68 +977,橙子,23/12/2020,964,6.64,4.3,6400.96,4145.2,2255.76 +413,橙子,29/05/2020,963,1.52,0.49,1463.76,471.87,991.89 +414,西葫芦,29/05/2020,963,1.45,0.23,1396.35,221.49,1174.86 +185,石榴,10/03/2020,962,2.05,0.49,1972.1,471.38,1500.72 +861,橙子,10/11/2020,961,1.53,0.24,1470.33,230.64,1239.69 +788,苹果,14/10/2020,960,1.09,0.52,1046.4,499.2,547.2 +245,西葫芦,03/04/2020,959,1.52,0.12,1457.68,115.08,1342.6 +419,胡萝卜,31/05/2020,959,4.34,1.05,4162.06,1006.95,3155.11 +971,洋葱,20/12/2020,957,4.19,2.37,4009.83,2268.09,1741.74 +694,橙子,10/09/2020,954,0.52,0.08,496.08,76.32,419.76 +823,石榴,27/10/2020,954,6.47,2.09,6172.38,1993.86,4178.52 +922,洋葱,29/11/2020,954,4.34,1.75,4140.36,1669.5,2470.86 +150,胡萝卜,25/02/2020,952,1.24,0.1,1180.48,95.2,1085.28 +831,洋葱,28/10/2020,952,1.52,0.61,1447.04,580.72,866.32 +398,胡萝卜,23/05/2020,946,0.52,0.12,491.92,113.52,378.4 +153,橙子,26/02/2020,944,4.19,2.03,3955.36,1916.32,2039.04 +918,石榴,28/11/2020,941,1.53,0.49,1439.73,461.09,978.64 +487,苹果,24/06/2020,940,4.19,1.35,3938.6,1269,2669.6 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+691,胡萝卜,10/09/2020,898,1.52,0.24,1364.96,215.52,1149.44 +712,葡萄,16/09/2020,897,1.52,0.73,1363.44,654.81,708.63 +816,橙子,24/10/2020,896,2.54,1.44,2275.84,1290.24,985.6 +206,西葫芦,17/03/2020,895,6.47,1.57,5790.65,1405.15,4385.5 +227,西葫芦,25/03/2020,894,4.34,0.7,3879.96,625.8,3254.16 +790,洋葱,15/10/2020,894,1.09,0.7,974.46,625.8,348.66 +644,石榴,25/08/2020,893,0.52,0.08,464.36,71.44,392.92 +38,西葫芦,18/01/2020,892,0.31,0.2,276.52,178.4,98.12 +736,葡萄,28/09/2020,888,1.09,0.7,967.92,621.6,346.32 +241,橙子,02/04/2020,886,1.53,0.12,1355.58,106.32,1249.26 +202,洋葱,16/03/2020,884,0.73,0.05,645.32,44.2,601.12 +255,西葫芦,07/04/2020,881,1.09,0.44,960.29,387.64,572.65 +376,葡萄,17/05/2020,880,1.03,0.08,906.4,70.4,836 +474,西葫芦,21/06/2020,880,1.52,0.24,1337.6,211.2,1126.4 +818,苹果,24/10/2020,879,1.53,0.99,1344.87,870.21,474.66 +35,洋葱,17/01/2020,877,6.64,4.84,5823.28,4244.68,1578.6 +373,葡萄,15/05/2020,877,1.11,0.62,973.47,543.74,429.73 +443,葡萄,10/06/2020,876,2.54,1.23,2225.04,1077.48,1147.56 +485,葡萄,24/06/2020,876,1.03,0.66,902.28,578.16,324.12 +15,石榴,05/01/2020,872,0.9,0.43,784.8,374.96,409.84 +448,土豆,12/06/2020,871,4.19,1.35,3649.49,1175.85,2473.64 +162,洋葱,02/03/2020,870,2.05,0.49,1783.5,426.3,1357.2 +947,橙子,09/12/2020,870,6.47,1.57,5628.9,1365.9,4263 +296,土豆,20/04/2020,867,0.73,0.47,632.91,407.49,225.42 +548,土豆,17/07/2020,866,0.69,0.11,597.54,95.26,502.28 +586,胡萝卜,30/07/2020,866,4.19,3.05,3628.54,2641.3,987.24 +804,胡萝卜,19/10/2020,866,1.03,0.5,891.98,433,458.98 +49,洋葱,22/01/2020,865,2.05,1.49,1773.25,1288.85,484.4 +468,西葫芦,18/06/2020,863,2.54,1.02,2192.02,880.26,1311.76 +637,石榴,20/08/2020,863,1.45,0.23,1251.35,198.49,1052.86 +840,葡萄,31/10/2020,863,4.34,2.11,3745.42,1820.93,1924.49 +934,苹果,05/12/2020,862,2.05,0.33,1767.1,284.46,1482.64 +940,橙子,05/12/2020,862,4.19,3.05,3611.78,2629.1,982.68 +145,橙子,22/02/2020,861,1.53,0.86,1317.33,740.46,576.87 +734,葡萄,27/09/2020,861,2.05,0.99,1765.05,852.39,912.66 +786,土豆,13/10/2020,860,6.64,2.15,5710.4,1849,3861.4 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+965,西葫芦,18/12/2020,370,4.19,0.33,1550.3,122.1,1428.2 +418,葡萄,30/05/2020,369,1.53,0.12,564.57,44.28,520.29 +352,苹果,09/05/2020,368,1.53,0.74,563.04,272.32,290.72 +179,胡萝卜,08/03/2020,367,1.09,0.44,400.03,161.48,238.55 +117,葡萄,14/02/2020,364,1.53,0.49,556.92,178.36,378.56 +519,橙子,07/07/2020,363,6.47,2.09,2348.61,758.67,1589.94 +683,洋葱,06/09/2020,362,1.53,0.24,553.86,86.88,466.98 +693,石榴,10/09/2020,362,1.09,0.44,394.58,159.28,235.3 +916,洋葱,28/11/2020,362,4.19,1.69,1516.78,611.78,905 +877,西葫芦,15/11/2020,361,1.03,0.66,371.83,238.26,133.57 +708,石榴,16/09/2020,359,0.31,0.15,111.29,53.85,57.44 +733,胡萝卜,26/09/2020,359,2.05,0.49,735.95,175.91,560.04 +556,葡萄,19/07/2020,358,6.64,2.15,2377.12,769.7,1607.42 +34,橙子,17/01/2020,356,6.47,3.14,2303.32,1117.84,1185.48 +609,葡萄,08/08/2020,351,0.31,0.2,108.81,70.2,38.61 +717,西葫芦,19/09/2020,351,0.69,0.05,242.19,17.55,224.64 +844,苹果,03/11/2020,350,1.09,0.44,381.5,154,227.5 +220,葡萄,24/03/2020,347,6.47,4.19,2245.09,1453.93,791.16 +578,苹果,28/07/2020,347,2.05,0.33,711.35,114.51,596.84 +601,土豆,06/08/2020,347,1.53,0.74,530.91,256.78,274.13 +360,胡萝卜,10/05/2020,346,1.24,0.2,429.04,69.2,359.84 +478,胡萝卜,22/06/2020,346,0.69,0.44,238.74,152.24,86.5 +541,石榴,14/07/2020,345,1.09,0.44,376.05,151.8,224.25 +1000,橙子,01/01/2021,345,1.03,0.75,355.35,258.75,96.6 +138,石榴,20/02/2020,339,1.09,0.17,369.51,57.63,311.88 +758,胡萝卜,04/10/2020,336,1.53,1.11,514.08,372.96,141.12 +855,洋葱,09/11/2020,336,1.53,0.74,514.08,248.64,265.44 +274,西葫芦,15/04/2020,334,2.54,0.41,848.36,136.94,711.42 +848,葡萄,06/11/2020,331,4.34,1.05,1436.54,347.55,1088.99 +307,橙子,22/04/2020,328,1.52,0.73,498.56,239.44,259.12 +299,苹果,20/04/2020,326,1.53,0.61,498.78,198.86,299.92 +147,胡萝卜,23/02/2020,322,1.11,0.17,357.42,54.74,302.68 +874,洋葱,13/11/2020,322,1.52,0.36,489.44,115.92,373.52 +64,苹果,27/01/2020,321,1.09,0.7,349.89,224.7,125.19 +242,葡萄,02/04/2020,316,0.9,0.14,284.4,44.24,240.16 +469,西葫芦,18/06/2020,316,1.03,0.41,325.48,129.56,195.92 +772,石榴,08/10/2020,312,4.34,2.46,1354.08,767.52,586.56 +282,葡萄,16/04/2020,311,4.19,2.03,1303.09,631.33,671.76 +471,胡萝卜,19/06/2020,309,1.53,0.49,472.77,151.41,321.36 +330,苹果,01/05/2020,308,2.54,0.41,782.32,126.28,656.04 +174,洋葱,06/03/2020,305,1.24,0.5,378.2,152.5,225.7 +383,洋葱,19/05/2020,303,1.09,0.35,330.27,106.05,224.22 +959,葡萄,16/12/2020,303,1.45,0.23,439.35,69.69,369.66 +243,洋葱,03/04/2020,302,1.24,0.6,374.48,181.2,193.28 +240,石榴,01/04/2020,298,2.54,0.2,756.92,59.6,697.32 +677,土豆,05/09/2020,298,6.64,3.22,1978.72,959.56,1019.16 +6,橙子,02/01/2020,297,2.05,0.99,608.85,294.03,314.82 +663,橙子,01/09/2020,296,4.34,2.46,1284.64,728.16,556.48 +732,橙子,25/09/2020,296,1.53,0.74,452.88,219.04,233.84 +304,葡萄,21/04/2020,292,6.47,4.19,1889.24,1223.48,665.76 +851,洋葱,08/11/2020,291,1.11,0.62,323.01,180.42,142.59 +267,西葫芦,10/04/2020,289,0.73,0.23,210.97,66.47,144.5 +675,胡萝卜,04/09/2020,287,4.34,0.35,1245.58,100.45,1145.13 +727,土豆,23/09/2020,287,1.09,0.7,312.83,200.9,111.93 +899,橙子,23/11/2020,287,6.47,3.67,1856.89,1053.29,803.6 +569,苹果,25/07/2020,286,1.09,0.44,311.74,125.84,185.9 +620,土豆,14/08/2020,284,1.09,0.52,309.56,147.68,161.88 +483,西葫芦,23/06/2020,283,6.64,0.53,1879.12,149.99,1729.13 +747,葡萄,01/10/2020,282,6.64,1.07,1872.48,301.74,1570.74 +780,胡萝卜,12/10/2020,282,1.52,0.12,428.64,33.84,394.8 +634,石榴,19/08/2020,281,6.64,4.3,1865.84,1208.3,657.54 +785,西葫芦,13/10/2020,281,2.05,0.33,576.05,92.73,483.32 +161,土豆,02/03/2020,280,1.09,0.61,305.2,170.8,134.4 +33,胡萝卜,17/01/2020,278,0.31,0.17,86.18,47.26,38.92 +320,洋葱,27/04/2020,278,6.64,2.15,1845.92,597.7,1248.22 +339,西葫芦,05/05/2020,278,2.54,0.2,706.12,55.6,650.52 +721,苹果,20/09/2020,275,1.45,0.94,398.75,258.5,140.25 +880,苹果,15/11/2020,275,1.09,0.35,299.75,96.25,203.5 +29,橙子,15/01/2020,274,6.47,2.09,1772.78,572.66,1200.12 +482,洋葱,23/06/2020,274,1.11,0.35,304.14,95.9,208.24 +558,葡萄,19/07/2020,270,6.64,2.69,1792.8,726.3,1066.5 +619,西葫芦,14/08/2020,269,4.19,2.71,1127.11,728.99,398.12 +633,橙子,19/08/2020,267,0.9,0.65,240.3,173.55,66.75 +950,苹果,11/12/2020,267,6.64,2.15,1772.88,574.05,1198.83 +106,石榴,10/02/2020,265,1.53,0.37,405.45,98.05,307.4 +749,苹果,02/10/2020,265,6.47,2.09,1714.55,553.85,1160.7 +371,西葫芦,15/05/2020,264,6.47,3.67,1708.08,968.88,739.2 +592,橙子,03/08/2020,263,1.09,0.52,286.67,136.76,149.91 +76,苹果,31/01/2020,262,1.03,0.58,269.86,151.96,117.9 +125,西葫芦,16/02/2020,262,0.69,0.11,180.78,28.82,151.96 +813,葡萄,23/10/2020,260,2.54,0.61,660.4,158.6,501.8 +872,洋葱,13/11/2020,260,1.03,0.08,267.8,20.8,247 +369,西葫芦,14/05/2020,258,2.05,1.32,528.9,340.56,188.34 +526,洋葱,09/07/2020,257,1.52,0.12,390.64,30.84,359.8 +944,土豆,07/12/2020,257,0.69,0.39,177.33,100.23,77.1 +597,苹果,05/08/2020,256,0.52,0.04,133.12,10.24,122.88 +221,土豆,24/03/2020,255,1.52,0.86,387.6,219.3,168.3 +767,橙子,07/10/2020,255,6.47,3.67,1649.85,935.85,714 +9,西葫芦,03/01/2020,254,4.19,1.35,1064.26,342.9,721.36 +244,橙子,03/04/2020,254,0.73,0.35,185.42,88.9,96.52 +8,土豆,03/01/2020,253,4.19,0.33,1060.07,83.49,976.58 +289,胡萝卜,18/04/2020,251,1.11,0.8,278.61,200.8,77.81 +725,胡萝卜,22/09/2020,246,4.19,2.37,1030.74,583.02,447.72 +795,葡萄,17/10/2020,244,0.69,0.05,168.36,12.2,156.16 +979,洋葱,23/12/2020,243,0.9,0.14,218.7,34.02,184.68 +48,西葫芦,22/01/2020,242,6.47,2.62,1565.74,634.04,931.7 +335,胡萝卜,03/05/2020,241,2.54,1.23,612.14,296.43,315.71 +462,橙子,15/06/2020,241,1.52,1.1,366.32,265.1,101.22 +499,土豆,26/06/2020,241,1.53,0.74,368.73,178.34,190.39 +538,土豆,13/07/2020,240,2.05,0.66,492,158.4,333.6 +794,石榴,17/10/2020,240,6.47,4.71,1552.8,1130.4,422.4 +903,胡萝卜,24/11/2020,238,0.73,0.53,173.74,126.14,47.6 +344,洋葱,05/05/2020,236,1.45,0.47,342.2,110.92,231.28 +415,胡萝卜,29/05/2020,236,4.34,3.16,1024.24,745.76,278.48 +990,橙子,30/12/2020,235,1.09,0.17,256.15,39.95,216.2 +882,胡萝卜,16/11/2020,233,0.31,0.22,72.23,51.26,20.97 +126,橙子,16/02/2020,232,1.53,0.86,354.96,199.52,155.44 +968,洋葱,19/12/2020,230,0.52,0.33,119.6,75.9,43.7 +583,橙子,29/07/2020,229,4.34,1.75,993.86,400.75,593.11 +846,洋葱,04/11/2020,229,1.53,0.49,350.37,112.21,238.16 +923,石榴,30/11/2020,225,2.05,0.16,461.25,36,425.25 +103,胡萝卜,09/02/2020,222,1.45,0.11,321.9,24.42,297.48 +326,胡萝卜,29/04/2020,221,1.09,0.35,240.89,77.35,163.54 +332,洋葱,02/05/2020,221,0.69,0.27,152.49,59.67,92.82 +464,葡萄,16/06/2020,221,6.64,2.69,1467.44,594.49,872.95 +698,葡萄,11/09/2020,221,4.34,0.7,959.14,154.7,804.44 +901,葡萄,23/11/2020,219,1.52,0.98,332.88,214.62,118.26 +627,橙子,17/08/2020,215,1.53,0.49,328.95,105.35,223.6 +967,洋葱,19/12/2020,213,6.64,2.15,1414.32,457.95,956.37 +5,洋葱,02/01/2020,212,4.34,2.11,920.08,447.32,472.76 +645,土豆,25/08/2020,212,2.54,1.85,538.48,392.2,146.28 +701,胡萝卜,12/09/2020,211,0.31,0.1,65.41,21.1,44.31 +689,土豆,09/09/2020,210,0.73,0.47,153.3,98.7,54.6 +820,石榴,25/10/2020,210,1.09,0.61,228.9,128.1,100.8 +392,石榴,21/05/2020,207,2.54,0.41,525.78,84.87,440.91 +59,苹果,26/01/2020,206,0.52,0.12,107.12,24.72,82.4 +431,葡萄,04/06/2020,206,1.53,0.49,315.18,100.94,214.24 +792,西葫芦,16/10/2020,205,4.19,0.67,858.95,137.35,721.6 +14,橙子,04/01/2020,203,6.64,2.15,1347.92,436.45,911.47 +111,苹果,11/02/2020,202,4.34,1.4,876.68,282.8,593.88 +279,石榴,16/04/2020,201,6.64,4.84,1334.64,972.84,361.8 +929,胡萝卜,02/12/2020,200,6.64,4.84,1328,968,360 +956,葡萄,14/12/2020,200,1.52,0.49,304,98,206 +53,葡萄,24/01/2020,199,1.52,0.98,302.48,195.02,107.46 +589,胡萝卜,01/08/2020,199,1.52,0.24,302.48,47.76,254.72 +170,苹果,04/03/2020,197,2.54,1.85,500.38,364.45,135.93 +233,石榴,26/03/2020,197,1.53,0.12,301.41,23.64,277.77 +621,石榴,15/08/2020,197,1.24,0.7,244.28,137.9,106.38 +232,葡萄,26/03/2020,194,6.47,4.19,1255.18,812.86,442.32 +765,土豆,07/10/2020,193,1.52,0.49,293.36,94.57,198.79 +100,苹果,06/02/2020,192,2.54,1.64,487.68,314.88,172.8 +348,胡萝卜,07/05/2020,192,1.52,0.73,291.84,140.16,151.68 +859,葡萄,09/11/2020,192,0.9,0.51,172.8,97.92,74.88 +90,葡萄,04/02/2020,189,1.53,0.86,289.17,162.54,126.63 +259,橙子,08/04/2020,189,0.69,0.11,130.41,20.79,109.62 +653,葡萄,29/08/2020,189,1.09,0.52,206.01,98.28,107.73 +518,西葫芦,07/07/2020,187,6.64,4.84,1241.68,905.08,336.6 +750,葡萄,02/10/2020,186,0.52,0.37,96.72,68.82,27.9 +745,西葫芦,30/09/2020,185,2.54,1.02,469.9,188.7,281.2 +155,橙子,27/02/2020,184,6.64,2.15,1221.76,395.6,826.16 +36,土豆,17/01/2020,182,0.73,0.41,132.86,74.62,58.24 +115,西葫芦,14/02/2020,181,2.05,0.33,371.05,59.73,311.32 +363,橙子,11/05/2020,180,2.54,0.82,457.2,147.6,309.6 +489,土豆,24/06/2020,179,6.64,4.84,1188.56,866.36,322.2 +505,洋葱,01/07/2020,177,1.52,0.12,269.04,21.24,247.8 +579,苹果,28/07/2020,177,4.19,3.05,741.63,539.85,201.78 +1,胡萝卜,01/01/2020,176,4.19,1.35,737.44,237.6,499.84 +355,苹果,09/05/2020,176,2.05,1.32,360.8,232.32,128.48 +308,橙子,22/04/2020,175,6.47,3.14,1132.25,549.5,582.75 +976,葡萄,22/12/2020,167,2.05,1.16,342.35,193.72,148.63 +372,苹果,15/05/2020,166,2.05,0.49,340.3,81.34,258.96 +215,西葫芦,20/03/2020,165,1.53,0.74,252.45,122.1,130.35 +406,西葫芦,26/05/2020,165,2.54,0.82,419.1,135.3,283.8 +768,葡萄,07/10/2020,165,6.47,4.19,1067.55,691.35,376.2 +345,苹果,06/05/2020,164,6.47,3.67,1061.08,601.88,459.2 +863,石榴,10/11/2020,164,0.9,0.07,147.6,11.48,136.12 +417,橙子,30/05/2020,162,0.73,0.11,118.26,17.82,100.44 +769,西葫芦,07/10/2020,161,1.03,0.41,165.83,66.01,99.82 +742,洋葱,28/09/2020,160,1.11,0.44,177.6,70.4,107.2 +430,葡萄,04/06/2020,158,6.64,1.07,1049.12,169.06,880.06 +896,胡萝卜,20/11/2020,157,6.64,3.76,1042.48,590.32,452.16 +917,西葫芦,28/11/2020,157,0.52,0.37,81.64,58.09,23.55 +253,苹果,06/04/2020,156,4.19,2.71,653.64,422.76,230.88 +822,胡萝卜,26/10/2020,156,6.64,1.61,1035.84,251.16,784.68 +972,洋葱,21/12/2020,156,1.45,0.35,226.2,54.6,171.6 +686,葡萄,09/09/2020,153,1.09,0.17,166.77,26.01,140.76 +23,土豆,10/01/2020,152,1.52,0.12,231.04,18.24,212.8 +549,土豆,17/07/2020,151,4.34,2.11,655.34,318.61,336.73 +351,石榴,08/05/2020,149,2.05,0.83,305.45,123.67,181.78 +85,胡萝卜,02/02/2020,148,0.69,0.11,102.12,16.28,85.84 +605,葡萄,07/08/2020,146,1.09,0.35,159.14,51.1,108.04 +108,石榴,11/02/2020,143,1.53,0.49,218.79,70.07,148.72 +584,橙子,29/07/2020,142,4.34,2.11,616.28,299.62,316.66 +62,橙子,27/01/2020,140,2.05,1.16,287,162.4,124.6 +629,洋葱,18/08/2020,138,6.64,1.07,916.32,147.66,768.66 +470,葡萄,19/06/2020,137,1.11,0.53,152.07,72.61,79.46 +908,胡萝卜,25/11/2020,137,1.03,0.25,141.11,34.25,106.86 +37,西葫芦,18/01/2020,136,1.09,0.26,148.24,35.36,112.88 +154,胡萝卜,26/02/2020,136,2.05,1.16,278.8,157.76,121.04 +171,石榴,04/03/2020,135,1.52,0.98,205.2,132.3,72.9 +876,土豆,14/11/2020,133,4.19,0.67,557.27,89.11,468.16 +512,橙子,04/07/2020,132,4.19,2.71,553.08,357.72,195.36 +310,土豆,23/04/2020,129,2.05,0.83,264.45,107.07,157.38 +522,苹果,07/07/2020,129,1.52,0.86,196.08,110.94,85.14 +204,石榴,16/03/2020,128,6.64,3.22,849.92,412.16,437.76 +205,石榴,17/03/2020,128,1.53,1.11,195.84,142.08,53.76 +423,橙子,01/06/2020,128,4.34,0.35,555.52,44.8,510.72 +553,西葫芦,18/07/2020,128,1.53,1.11,195.84,142.08,53.76 +775,橙子,11/10/2020,127,1.11,0.44,140.97,55.88,85.09 +446,胡萝卜,11/06/2020,125,6.47,4.71,808.75,588.75,220 +945,胡萝卜,08/12/2020,125,6.64,1.61,830,201.25,628.75 +883,胡萝卜,16/11/2020,123,6.47,1.04,795.81,127.92,667.89 +699,苹果,11/09/2020,118,4.19,0.33,494.42,38.94,455.48 +193,石榴,13/03/2020,116,2.54,0.82,294.64,95.12,199.52 +219,土豆,24/03/2020,116,6.47,1.04,750.52,120.64,629.88 +716,葡萄,18/09/2020,116,1.53,0.74,177.48,85.84,91.64 +197,橙子,15/03/2020,113,4.19,0.67,473.47,75.71,397.76 +300,土豆,20/04/2020,113,1.45,0.47,163.85,53.11,110.74 +935,胡萝卜,05/12/2020,112,6.47,4.19,724.64,469.28,255.36 +32,苹果,17/01/2020,110,0.9,0.07,99,7.7,91.3 +361,葡萄,10/05/2020,106,1.11,0.62,117.66,65.72,51.94 +177,石榴,07/03/2020,105,1.09,0.7,114.45,73.5,40.95 +809,土豆,20/10/2020,105,2.05,1.32,215.25,138.6,76.65 +364,西葫芦,12/05/2020,104,2.05,0.16,213.2,16.64,196.56 +422,苹果,01/06/2020,104,0.69,0.22,71.76,22.88,48.88 +658,洋葱,30/08/2020,104,1.11,0.26,115.44,27.04,88.4 +997,西葫芦,31/12/2020,103,6.64,3.76,683.92,387.28,296.64 +890,葡萄,18/11/2020,102,1.53,0.49,156.06,49.98,106.08 +30,葡萄,15/01/2020,101,0.52,0.16,52.52,16.16,36.36 +132,葡萄,18/02/2020,101,0.69,0.5,69.69,50.5,19.19 +367,石榴,13/05/2020,101,1.03,0.75,104.03,75.75,28.28 +771,胡萝卜,08/10/2020,99,6.47,0.52,640.53,51.48,589.05 +199,胡萝卜,15/03/2020,98,1.09,0.44,106.82,43.12,63.7 +331,洋葱,01/05/2020,98,0.73,0.47,71.54,46.06,25.48 +158,西葫芦,29/02/2020,93,6.47,3.67,601.71,341.31,260.4 +567,石榴,24/07/2020,92,1.53,0.74,140.76,68.08,72.68 +323,苹果,27/04/2020,86,6.47,2.09,556.42,179.74,376.68 +427,洋葱,04/06/2020,86,1.09,0.52,93.74,44.72,49.02 +20,西葫芦,07/01/2020,85,2.05,1.49,174.25,126.65,47.6 +333,胡萝卜,02/05/2020,85,0.69,0.11,58.65,9.35,49.3 +858,西葫芦,09/11/2020,82,6.64,4.3,544.48,352.6,191.88 +182,西葫芦,09/03/2020,81,1.11,0.26,89.91,21.06,68.85 +590,胡萝卜,01/08/2020,79,6.64,4.84,524.56,382.36,142.2 +618,石榴,14/08/2020,76,1.52,0.61,115.52,46.36,69.16 +354,橙子,09/05/2020,74,2.54,0.82,187.96,60.68,127.28 +426,葡萄,02/06/2020,73,2.05,1.16,149.65,84.68,64.97 +504,洋葱,01/07/2020,73,1.52,0.49,110.96,35.77,75.19 +834,石榴,29/10/2020,73,6.47,1.04,472.31,75.92,396.39 +921,胡萝卜,29/11/2020,73,0.9,0.36,65.7,26.28,39.42 +73,土豆,30/01/2020,71,1.09,0.44,77.39,31.24,46.15 +359,西葫芦,10/05/2020,70,2.54,0.61,177.8,42.7,135.1 +542,石榴,15/07/2020,69,1.53,0.37,105.57,25.53,80.04 +781,葡萄,12/10/2020,67,0.69,0.22,46.23,14.74,31.49 +313,土豆,26/04/2020,64,4.34,1.4,277.76,89.6,188.16 +472,苹果,20/06/2020,63,1.09,0.44,68.67,27.72,40.95 +86,胡萝卜,03/02/2020,62,2.54,0.41,157.48,25.42,132.06 +196,苹果,14/03/2020,60,1.52,1.1,91.2,66,25.2 +722,土豆,21/09/2020,58,1.11,0.8,64.38,46.4,17.98 +709,橙子,16/09/2020,56,0.69,0.39,38.64,21.84,16.8 +456,苹果,14/06/2020,55,1.52,0.24,83.6,13.2,70.4 +695,苹果,10/09/2020,54,0.73,0.53,39.42,28.62,10.8 +386,土豆,20/05/2020,52,4.19,2.71,217.88,140.92,76.96 +393,洋葱,21/05/2020,51,6.47,4.19,329.97,213.69,116.28 +119,西葫芦,14/02/2020,50,2.54,1.02,127,51,76 +424,土豆,02/06/2020,50,1.52,1.1,76,55,21 +650,胡萝卜,28/08/2020,49,6.64,3.76,325.36,184.24,141.12 +774,胡萝卜,10/10/2020,49,0.69,0.44,33.81,21.56,12.25 +273,橙子,14/04/2020,47,0.31,0.1,14.57,4.7,9.87 +704,胡萝卜,14/09/2020,47,1.52,0.73,71.44,34.31,37.13 +12,石榴,03/01/2020,46,1.52,0.61,69.92,28.06,41.86 +47,橙子,22/01/2020,45,2.54,0.41,114.3,18.45,95.85 +811,石榴,21/10/2020,45,1.53,0.37,68.85,16.65,52.2 +893,葡萄,19/11/2020,45,0.69,0.27,31.05,12.15,18.9 +989,洋葱,28/12/2020,43,6.47,4.71,278.21,202.53,75.68 +94,葡萄,05/02/2020,41,1.52,0.36,62.32,14.76,47.56 +11,苹果,03/01/2020,40,1.24,0.3,49.6,12,37.6 +587,葡萄,31/07/2020,40,1.03,0.66,41.2,26.4,14.8 +391,胡萝卜,21/05/2020,39,6.47,4.19,252.33,163.41,88.92 +137,石榴,20/02/2020,36,0.73,0.35,26.28,12.6,13.68 +509,橙子,03/07/2020,36,4.34,1.05,156.24,37.8,118.44 +933,胡萝卜,04/12/2020,36,2.54,1.64,91.44,59.04,32.4 +664,洋葱,01/09/2020,35,2.05,0.49,71.75,17.15,54.6 +828,西葫芦,28/10/2020,35,6.64,1.61,232.4,56.35,176.05 +991,土豆,30/12/2020,35,4.19,2.37,146.65,82.95,63.7 +458,石榴,15/06/2020,34,0.52,0.29,17.68,9.86,7.82 +566,苹果,24/07/2020,33,2.54,1.44,83.82,47.52,36.3 +814,橙子,23/10/2020,33,2.54,0.61,83.82,20.13,63.69 +338,橙子,04/05/2020,31,4.19,0.67,129.89,20.77,109.12 +746,西葫芦,30/09/2020,30,1.52,0.73,45.6,21.9,23.7 +753,葡萄,03/10/2020,27,0.31,0.12,8.37,3.24,5.13 +868,葡萄,11/11/2020,27,4.19,1.69,113.13,45.63,67.5 +565,橙子,24/07/2020,26,4.34,1.75,112.84,45.5,67.34 +301,苹果,20/04/2020,24,2.54,1.02,60.96,24.48,36.48 +436,胡萝卜,07/06/2020,23,6.64,1.07,152.72,24.61,128.11 +258,洋葱,08/04/2020,22,1.09,0.61,23.98,13.42,10.56 +291,西葫芦,19/04/2020,21,1.52,0.49,31.92,10.29,21.63 +497,洋葱,26/06/2020,21,1.53,0.37,32.13,7.77,24.36 +249,土豆,04/04/2020,18,1.09,0.35,19.62,6.3,13.32 +399,苹果,24/05/2020,15,1.09,0.35,16.35,5.25,11.1 +128,橙子,17/02/2020,14,1.11,0.53,15.54,7.42,8.12 +797,石榴,18/10/2020,13,4.19,3.05,54.47,39.65,14.82 +121,葡萄,15/02/2020,11,1.45,0.11,15.95,1.21,14.74 +452,西葫芦,13/06/2020,10,2.54,0.41,25.4,4.1,21.3 +425,苹果,02/06/2020,9,6.47,3.67,58.23,33.03,25.2 +306,石榴,21/04/2020,8,4.19,2.03,33.52,16.24,17.28 +728,胡萝卜,24/09/2020,8,1.53,0.49,12.24,3.92,8.32 +129,胡萝卜,17/02/2020,5,6.47,3.14,32.35,15.7,16.65 +319,石榴,26/04/2020,4,1.03,0.41,4.12,1.64,2.48 +680,橙子,05/09/2020,2,6.64,1.61,13.28,3.22,10.06 diff --git "a/excel/excel\346\212\245\350\241\250/pie_logo.png" "b/excel/excel\346\212\245\350\241\250/pie_logo.png" new file mode 100644 index 0000000..60d37a2 Binary files /dev/null and "b/excel/excel\346\212\245\350\241\250/pie_logo.png" differ diff --git "a/excel/excel\346\212\245\350\241\250/win_excel.py" "b/excel/excel\346\212\245\350\241\250/win_excel.py" new file mode 100644 index 0000000..d98680e --- /dev/null +++ "b/excel/excel\346\212\245\350\241\250/win_excel.py" @@ -0,0 +1,185 @@ +import time +import pathlib +import pandas as pd +import xlwings as xw +import matplotlib.pyplot as plt + +pd.set_option('display.unicode.ambiguous_as_wide', True) +pd.set_option('display.unicode.east_asian_width', True) + +df = pd.read_csv(r"fruit_and_veg_sales.csv") +print(df) + +# 创建原始数据表并复制数据 +wb = xw.Book() +sht = wb.sheets["Sheet1"] +sht.name = "fruit_and_veg_sales" +sht.range("A1").options(index=False).value = df + +# 查看所有列名 +print(df.columns) + +# 总利润透视表 +pv_total_profit = pd.pivot_table(df, index='类别', values='总利润(美元)', aggfunc='sum') +print(pv_total_profit) + +# 销售数量透视表 +pv_quantity_sold = pd.pivot_table(df, index='类别', values='销售数量', aggfunc='sum') +print(pv_quantity_sold) + +# 查看每列的数据类型 +print(df.dtypes) +df["销售日期"] = pd.to_datetime(df["销售日期"]) + +# 每日的数据情况 +gb_date_sold = df.groupby( + df["销售日期"].dt.to_period('m')).sum()[["销售数量", '总收入(美元)', '总成本(美元)', "总利润(美元)"]] +gb_date_sold.index = gb_date_sold.index.to_series().astype(str) +print(gb_date_sold) + +# 总收入前8的日期数据 +gb_top_revenue = (df.groupby(df["销售日期"]).sum().sort_values( + '总收入(美元)', ascending=False).head(8))[["销售数量", '总收入(美元)', '总成本(美元)', "总利润(美元)"]] +print(gb_top_revenue) + +# 创建展示表 +wb.sheets.add('Dashboard') +sht_dashboard = wb.sheets('Dashboard') + +# 设置背景颜色, 从A1单元格到Z1000单元格的矩形区域 +sht_dashboard.range('A1:Z1000').color = (198, 224, 180) + +# A、B列的列宽 +sht_dashboard.range('A:B').column_width = 2.22 + +# B2单元格, 文字内容、字体、字号、粗体、颜色、行高(主标题) +sht_dashboard.range('B2').value = '销售数据报表' +sht_dashboard.range('B2').api.Font.Name = '黑体' +sht_dashboard.range('B2').api.Font.Size = 48 +sht_dashboard.range('B2').api.Font.Bold = True +sht_dashboard.range('B2').api.Font.Color = 0x000000 +sht_dashboard.range('B2').row_height = 61.2 + +# B2单元格到W2单元格的矩形区域, 下边框的粗细及颜色 +sht_dashboard.range('B2:W2').api.Borders(9).Weight = 4 +sht_dashboard.range('B2:W2').api.Borders(9).Color = 0x00B050 + +# 不同产品总的收益情况图表名称、字体、字号、粗体、颜色(副标题) +sht_dashboard.range('M2').value = '每种产品的收益情况' +sht_dashboard.range('M2').api.Font.Name = '黑体' +sht_dashboard.range('M2').api.Font.Size = 20 +sht_dashboard.range('M2').api.Font.Bold = True +sht_dashboard.range('M2').api.Font.Color = 0x000000 + +# 主标题和副标题的分割线, 粗细、颜色、线型 +sht_dashboard.range('L2').api.Borders(7).Weight = 3 +sht_dashboard.range('L2').api.Borders(7).Color = 0x00B050 +sht_dashboard.range('L2').api.Borders(7).LineStyle = -4115 + + +# 表格生成函数. +def create_formatted_summary(header_cell, title, df_summary, color): + """ + Parameters + ---------- + header_cell : Str + 左上角单元格位置, 放置数据 + + title : Str + 当前表格的标题 + + df_summary : DataFrame + 表格的数据 + + color : Str + 表格填充色 + """ + + # 可选择的表格填充色 + colors = { + "purple": [(112, 48, 160), (161, 98, 208)], + "blue": [(0, 112, 192), (155, 194, 230)], + "green": [(0, 176, 80), (169, 208, 142)], + "yellow": [(255, 192, 0), (255, 217, 102)] + } + + # 设置表格标题的列宽 + sht_dashboard.range(header_cell).column_width = 1.5 + + # 获取单元格的行列数 + row, col = sht_dashboard.range(header_cell).row, sht_dashboard.range(header_cell).column + + # 设置表格的标题及相关信息, 如：字号、行高、向左居中对齐、颜色、粗体、表格的背景颜色等 + summary_title_range = sht_dashboard.range(row, col) + summary_title_range.value = title + summary_title_range.api.Font.Size = 14 + summary_title_range.row_height = 32.5 + # 垂直对齐方式 + summary_title_range.api.VerticalAlignment = xw.constants.HAlign.xlHAlignCenter + summary_title_range.api.Font.Color = 0xFFFFFF + summary_title_range.api.Font.Bold = True + sht_dashboard.range( + (row, col), + (row, col + len(df_summary.columns) + 1)).color = colors[color][0] # Darker color + + # 设置表格内容、起始单元格、数据填充、字体大小、粗体、颜色填充 + summary_header_range = sht_dashboard.range(row + 1, col + 1) + summary_header_range.value = df_summary + summary_header_range = summary_header_range.expand('right') + summary_header_range.api.Font.Size = 11 + summary_header_range.api.Font.Bold = True + sht_dashboard.range( + (row + 1, col), + (row + 1, col + len(df_summary.columns) + 1)).color = colors[color][1] # Darker color + sht_dashboard.range((row + 1, col + 1), + (row + len(df_summary), col + len(df_summary.columns) + 1)).autofit() + + for num in range(1, len(df_summary) + 2, 2): + sht_dashboard.range((row + num, col), + (row + num, col + len(df_summary.columns) + 1)).color = colors[color][1] + + # 找到表格的最后一行 + last_row = sht_dashboard.range(row + 1, col + 1).expand('down').last_cell.row + side_border_range = sht_dashboard.range((row + 1, col), (last_row, col)) + + # 给表格左边添加带颜色的边框 + sht_dashboard.range(side_border_range).api.Borders(7).Weight = 3 + sht_dashboard.range(side_border_range).api.Borders(7).Color = xw.utils.rgb_to_int( + colors[color][1]) + sht_dashboard.range(side_border_range).api.Borders(7).LineStyle = -4115 + + +# 生成4个表格 +create_formatted_summary('B5', '每种产品的收益情况', pv_total_profit, 'green') +create_formatted_summary('B17', '每种产品的售出情况', pv_quantity_sold, 'purple') +create_formatted_summary('F17', '每月的销售情况', gb_date_sold, 'blue') +create_formatted_summary('F5', '每日总收入排名Top8 ', gb_top_revenue, 'yellow') + +# 中文显示 +plt.rcParams['font.sans-serif'] = ['SimHei'] + +# 使用Matplotlib绘制可视化图表, 饼图 +fig, ax = plt.subplots(figsize=(6, 3)) +pv_total_profit.plot(color='g', kind='bar', ax=ax) + +# 添加图表到Excel +sht_dashboard.pictures.add(fig, + name='ItemsChart', + left=sht_dashboard.range("M5").left, + top=sht_dashboard.range("M5").top, + update=True) +time.sleep(3) +# 添加logo到Excel +logo = sht_dashboard.pictures.add(image=f"{pathlib.Path.cwd()}/pie_logo.png", + name='PC_3', + left=sht_dashboard.range("J2").left, + top=sht_dashboard.range("J2").top + 5, + update=True) + +# 设置logo的大小 +logo.width = 54 +logo.height = 54 +time.sleep(3) +# 保存Excel文件 +wb.save(rf"水果蔬菜销售报表.xlsx") +wb.close() diff --git "a/excel/excel\346\212\245\350\241\250/\346\260\264\346\236\234\350\224\254\350\217\234\351\224\200\345\224\256\346\212\245\350\241\250-1.xlsx" "b/excel/excel\346\212\245\350\241\250/\346\260\264\346\236\234\350\224\254\350\217\234\351\224\200\345\224\256\346\212\245\350\241\250-1.xlsx" new file mode 100644 index 0000000..f1691d8 Binary files /dev/null and "b/excel/excel\346\212\245\350\241\250/\346\260\264\346\236\234\350\224\254\350\217\234\351\224\200\345\224\256\346\212\245\350\241\250-1.xlsx" differ diff --git "a/excel/excel\346\212\245\350\241\250/\346\260\264\346\236\234\350\224\254\350\217\234\351\224\200\345\224\256\346\212\245\350\241\250.xlsx" "b/excel/excel\346\212\245\350\241\250/\346\260\264\346\236\234\350\224\254\350\217\234\351\224\200\345\224\256\346\212\245\350\241\250.xlsx" new file mode 100644 index 0000000..8a69e12 Binary files /dev/null and "b/excel/excel\346\212\245\350\241\250/\346\260\264\346\236\234\350\224\254\350\217\234\351\224\200\345\224\256\346\212\245\350\241\250.xlsx" differ diff --git a/excel/template.xlsx b/excel/template.xlsx new file mode 100644 index 0000000..aabb3de Binary files /dev/null and b/excel/template.xlsx differ diff --git "a/excel/\345\220\210\345\271\266sheet\345\271\266\350\275\254\344\270\272\345\244\232\344\270\252csv.ipynb" "b/excel/\345\220\210\345\271\266sheet\345\271\266\350\275\254\344\270\272\345\244\232\344\270\252csv.ipynb" new file mode 100644 index 0000000..1fc837d --- /dev/null +++ "b/excel/\345\220\210\345\271\266sheet\345\271\266\350\275\254\344\270\272\345\244\232\344\270\252csv.ipynb" @@ -0,0 +1,752 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "首先我们先看第一个读者的需求：原始数据有18个文件，取出每个文件中指定的几列，然后全部合并起来，<br>\n", + "存储到一个新的文件，命名为2000_2017年省份碳排放数据。<br>\n", + "\n", + "\n", + "结合文章讲解学习本代码更方便高效：[点击直达教程文章](https://mp.weixin.qq.com/s/878Co1CXMiSE7XdBwh7nxg)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# 导入需要的数据包\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# 0、新建一个数据存储对象（我们用pandas中的Dataframe）<br>\n", + "df_concat = pd.DataFrame()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# 生成一个列表，存储时间\n", + "date_year = [str(i) for i in range(2000, 2018)]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# 1、读取目标Excel文件<br>\n", + "file_path = 'data/2000年-2017年碳排放清单/2000年30个省份排放清单.xlsx'\n", + "data = pd.read_excel(file_path, sheet_name=None)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# 2、遍历取出每个sheet中需要的几行数据，存储到新建的Dataframe中<br>\n", + "'''\n", + "取出单个sheet中需要的数据\n", + "'''\n", + "def get_sheet_data(data, sheet_name, year):\n", + " # 取需要的几行数据\n", + " df_concat = data[sheet_name].loc[[2,3,48,49]]\n", + " # 给 Unnamed: 0 列进行重命名\n", + " df_concat = df_concat.rename(columns={'Unnamed: 0':'类别'})\n", + " # 插入两列数据 省份\t年份\n", + " df_concat.insert(loc=0,column='省份',value=sheet_name)\n", + " df_concat.insert(loc=1,column='年份',value=i)\n", + " # 将Total这列移动到第四列\n", + " df_temp = df_concat['Total']\n", + " df_concat = df_concat.drop(['Total'],axis=1) # 先删除该列\n", + " df_concat.insert(loc=3,column='Total',value=df_temp) # 然后插入到第四列位置\n", + " return df_concat\n", + "\n", + "'''\n", + "取出单个Excel中需要的数据\n", + "'''\n", + "def get_excel_data(data, year):\n", + " df_concat = pd.DataFrame()\n", + " for sheet_name in list(data.keys()):\n", + " if sheet_name == 'Sum':\n", + " continue\n", + " df_temp = get_sheet_data(data, sheet_name, year)\n", + " df_concat = pd.concat([df_concat, df_temp])\n", + " return df_concat" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1620568255.1883297\n", + "1620568272.432948\n" + ] + } + ], + "source": [ + "import time \n", + "\n", + "print(time.time())\n", + "df_concat = pd.DataFrame()\n", + "for i in date_year:\n", + " file_path = 'data/2000年-2017年碳排放清单/%s年30个省份排放清单.xlsx'%i\n", + " data = pd.read_excel(file_path, sheet_name=None)\n", + " df_temp = get_excel_data(data, i)\n", + " df_concat = pd.concat([df_concat, df_temp])\n", + "print(time.time())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(2160, 25)" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "# 查看数据规模\n", + "df_concat.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "开始存储数据\n", + "数据保存成功\n" + ] + } + ], + "source": [ + "print(\"开始存储数据\")\n", + "df_concat.to_excel(\"data/2000_2017年省份碳排放数据.xlsx\", \"2000_2017\",index=None, encoding=\"utf-8\")\n", + "# writer.save()\n", + "print(\"数据保存成功\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "开始存储数据\n", + "数据保存成功\n" + ] + } + ], + "source": [ + "# 完整代码\n", + "import pandas as pd\n", + "import time\n", + "\n", + "'''\n", + "取出单个sheet中需要的数据\n", + "'''\n", + "def get_sheet_data(data, sheet_name, year):\n", + " # 取需要的几行数据\n", + " df_concat = data[sheet_name].loc[[2,3,48,49]]\n", + " # 给 Unnamed: 0 列进行重命名\n", + " df_concat = df_concat.rename(columns={'Unnamed: 0':'类别'})\n", + " # 插入两列数据 省份\t年份\n", + " df_concat.insert(loc=0,column='省份',value=sheet_name)\n", + " df_concat.insert(loc=1,column='年份',value=i)\n", + " # 将Total这列移动到第四列\n", + " df_temp = df_concat['Total']\n", + " df_concat = df_concat.drop(['Total'],axis=1) # 先删除该列\n", + " df_concat.insert(loc=3,column='Total',value=df_temp) # 然后插入到第四列位置\n", + " return df_concat\n", + "\n", + "'''\n", + "取出单个Excel中需要的数据\n", + "'''\n", + "def get_excel_data(data, year):\n", + " df_concat = pd.DataFrame()\n", + " for sheet_name in list(data.keys()):\n", + " if sheet_name == 'Sum':\n", + " continue\n", + " df_temp = get_sheet_data(data, sheet_name, year)\n", + " df_concat = pd.concat([df_concat, df_temp])\n", + " return df_concat\n", + " \n", + "# 0、新建一个数据存储对象（我们用pandas中的Dataframe）\n", + "df_concat = pd.DataFrame()\n", + "\n", + "# 生成一个列表，存储时间\n", + "date_year = [str(i) for i in range(2000, 2018)]\n", + "\n", + "# 1、遍历取出每个Excel中的每个sheet中需要的几行数据，存储到新建的Dataframe中\n", + "for i in date_year:\n", + " file_path = 'data/2000年-2017年碳排放清单/%s年30个省份排放清单.xlsx'%i\n", + " data = pd.read_excel(file_path, sheet_name=None)\n", + " df_temp = get_excel_data(data, i)\n", + " df_concat = pd.concat([df_concat, df_temp])\n", + "\n", + "# 2、写入数据\n", + "print(\"开始存储数据\")\n", + "df_concat.to_excel(\"data/2000_2017年省份碳排放数据.xlsx\", \"2000_2017\", index=None, encoding=\"utf-8\")\n", + "print(\"数据保存成功\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我们来看第二个读者的需求：原数据只有一个文件，里面有8个sheet，<br>\n", + "需要将每个sheet中的几列取出来，然后根据日期存储为一个一个的csv文件。<br>\n", + "\n", + "结合文章讲解学习本代码更方便高效：[点击直达教程文章](https://mp.weixin.qq.com/s/878Co1CXMiSE7XdBwh7nxg)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "meteo_china_tmin_2018\n53529\n53519\n53543\n53446\n53513\n53352\n53336\n53231\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " ymd lat lon tmin\n", + "0 20180101 51.024022 75.567044 -54.335985\n", + "1 20180102 1.715915 79.802810 -41.935287\n", + "2 20180103 64.537409 58.437196 -13.290637\n", + "3 20180104 0.315354 43.847884 -93.777330\n", + "4 20180105 6.556039 20.173486 -13.857637\n", + ".. ... ... ... ...\n", + "360 20181227 94.531570 41.184321 -7.202198\n", + "361 20181228 44.625031 87.786459 -82.287922\n", + "362 20181229 26.811828 66.436228 -69.500716\n", + "363 20181230 45.456553 17.346756 -31.927623\n", + "364 20181231 46.135540 45.562136 -74.711277\n", + "\n", + "[2920 rows x 4 columns]" + ], + "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>ymd</th>\n <th>lat</th>\n <th>lon</th>\n <th>tmin</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>20180101</td>\n <td>51.024022</td>\n <td>75.567044</td>\n <td>-54.335985</td>\n </tr>\n <tr>\n <th>1</th>\n <td>20180102</td>\n <td>1.715915</td>\n <td>79.802810</td>\n <td>-41.935287</td>\n </tr>\n <tr>\n <th>2</th>\n <td>20180103</td>\n <td>64.537409</td>\n <td>58.437196</td>\n <td>-13.290637</td>\n </tr>\n <tr>\n <th>3</th>\n <td>20180104</td>\n <td>0.315354</td>\n <td>43.847884</td>\n <td>-93.777330</td>\n </tr>\n <tr>\n <th>4</th>\n <td>20180105</td>\n <td>6.556039</td>\n <td>20.173486</td>\n <td>-13.857637</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>360</th>\n <td>20181227</td>\n <td>94.531570</td>\n <td>41.184321</td>\n <td>-7.202198</td>\n </tr>\n <tr>\n <th>361</th>\n <td>20181228</td>\n <td>44.625031</td>\n <td>87.786459</td>\n <td>-82.287922</td>\n </tr>\n <tr>\n <th>362</th>\n <td>20181229</td>\n <td>26.811828</td>\n <td>66.436228</td>\n <td>-69.500716</td>\n </tr>\n <tr>\n <th>363</th>\n <td>20181230</td>\n <td>45.456553</td>\n <td>17.346756</td>\n <td>-31.927623</td>\n </tr>\n <tr>\n <th>364</th>\n <td>20181231</td>\n <td>46.135540</td>\n <td>45.562136</td>\n <td>-74.711277</td>\n </tr>\n </tbody>\n</table>\n<p>2920 rows × 4 columns</p>\n</div>" + }, + "metadata": {}, + "execution_count": 15 + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "'''\n", + "1、读取、取出需要的数据并合并\n", + "'''\n", + "file_path = 'data/meteo_china_tmin_2018.xlsx'\n", + "data = pd.read_excel(file_path, sheet_name=None)\n", + "df_concat = pd.DataFrame()\n", + "\n", + "'''\n", + "2、遍历获取所有sheet中的数据\n", + "'''\n", + "for sheet_name in list(data.keys()):\n", + " print(sheet_name)\n", + " if sheet_name == 'meteo_china_tmin_2018':\n", + " continue\n", + " df_temp = data[sheet_name][['ymd', 'lat', 'lon', 'tmin']]\n", + " df_concat = pd.concat([df_concat, df_temp])\n", + "df_concat" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "20181001\n", + "20181002\n", + "20181003\n", + "20181004\n", + "20181005\n", + "20181006\n", + "20181007\n", + "20181008\n", + 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"20180815\n", + "20180816\n", + "20180817\n", + "20180818\n", + "20180819\n", + "20180820\n", + "20180821\n", + "20180822\n", + "20180823\n", + "20180824\n", + "20180825\n", + "20180826\n", + "20180827\n", + "20180828\n", + "20180829\n", + "20180830\n", + "20180831\n", + "20180901\n", + "20180902\n", + "20180903\n", + "20180904\n", + "20180905\n", + "20180906\n", + "20180907\n", + "20180908\n", + "20180909\n", + "20180910\n", + "20180911\n", + "20180912\n", + "20180913\n", + "20180914\n", + "20180915\n", + "20180916\n", + "20180917\n", + "20180918\n", + "20180919\n", + "20180920\n", + "20180921\n", + "20180922\n", + "20180923\n", + "20180924\n", + "20180925\n", + "20180926\n", + "20180927\n", + "20180928\n", + "20180929\n", + "20180930\n" + ] + } + ], + "source": [ + "'''\n", + "3、按时间进行分组，并保存为csv文件\n", + "文件格式：hetao-ymd_tmin\n", + "'''\n", + "ymd_set = set(df_concat['ymd'])\n", + "# 单文件测试\n", + "# ymd_data = df_concat[df_concat['ymd']==20180716]\n", + "# ymd_data.to_csv('./hetao-%d_tmin.csv'%20180716, index=False, header=None, columns=['lat', 'lon', 'tmin'])\n", + "# 循环操作所以数据\n", + "for ymd in ymd_set:\n", + " print(ymd)\n", + " ymd_data = df_concat[df_concat['ymd']==ymd]\n", + " ymd_data.to_csv('./data/hetao/hetao-%d_tmin.csv'%ymd, columns=['lat', 'lon', 'tmin'], header=None, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#完整代码\n", + "import pandas as pd\n", + "\n", + "'''\n", + "读取、取出需要的数据并合并\n", + "'''\n", + "file_path = './data/meteo_china_tmin_2018.xlsx'\n", + "data = pd.read_excel(file_path, sheet_name=None)\n", + "df_concat = pd.DataFrame()\n", + "\n", + "for sheet_name in list(data.keys()):\n", + " if sheet_name == 'meteo_china_tmin_2018':\n", + " continue\n", + " df_temp = data[sheet_name][['ymd', 'lat', 'lon', 'tmin']]\n", + " df_concat = pd.concat([df_concat, df_temp])\n", + "\n", + "'''\n", + "按时间进行分组，并保存为csv文件\n", + "文件格式：hetao-ymd_tmin\n", + "'''\n", + "# 获取所有日期\n", + "ymd_set = set(df_concat['ymd'])\n", + "# 循环操作所有数据\n", + "for ymd in ymd_set:\n", + " ymd_data = df_concat[df_concat['ymd']==ymd]\n", + " ymd_data.to_csv('./data/hetao/hetao-%d_tmin.csv'%ymd, columns=['lat', 'lon', 'tmin'], header=None, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "name": "python389jvsc74a57bd03b1827d4b6462cc460901af0bc0d075c933010817877a813d51f78a107cbf6e5", + "display_name": "Python 3.8.9 64-bit" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/faker_data/gen_data.py b/faker_data/gen_data.py new file mode 100644 index 0000000..4a13b47 --- /dev/null +++ b/faker_data/gen_data.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +from faker import Faker +import pandas as pd + +fake = Faker(["zh_CN"]) +Faker.seed(0) + + +def get_data(): + """ + Generates fake data for a person. + + Returns: + dict: A dictionary containing fake data for a person. + """ + key_list = [ + "姓名", + "详细地址", + "所在省份", + "手机号", + "身份证号", + "出生年月", + "邮箱", + ] + name = fake.name() + address = fake.address() + province = address[:3] + number = fake.phone_number() + id_card = fake.ssn() + birth_date = id_card[6:14] + email = fake.email() + info_list = [name, address, province, number, id_card, birth_date, email] + return dict(zip(key_list, info_list)) + + +df = pd.DataFrame( + columns=["姓名", "详细地址", "所在省份", "手机号", "身份证号", "出生年月", "邮箱"] +) +for _ in range(10000): + person_info = [get_data()] + df1 = pd.DataFrame(person_info) + df = pd.concat([df, df1]) +df.to_excel("模拟数据.xlsx", index=None) diff --git a/file_operation/delete_files.py b/file_operation/delete_files.py new file mode 100644 index 0000000..4602e20 --- /dev/null +++ b/file_operation/delete_files.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import os +import shutil +import glob +import datetime + +def backup_file(file_path): + """备份文件""" + backup_dir = os.path.dirname(file_path) + backup_name = os.path.basename(file_path) + '_' + datetime.datetime.now().strftime('%Y%m%d%H%M%S') + backup_path = os.path.join(backup_dir, backup_name) + shutil.copy2(file_path, backup_path) + print(f"备份文件 {file_path} 到 {backup_path}") + +def rollback_file(backup_path, file_path): + """回滚文件""" + shutil.copy2(backup_path, file_path) + print(f"回滚文件 {file_path} 到 {backup_path}") + +def delete_files(path, pattern, backup=True): + """删除指定路径下匹配某个模式的文件""" + file_list = glob.glob(os.path.join(path, pattern)) + if not file_list: + print(f"没有找到匹配 {pattern} 的文件") + return + + for file_path in file_list: + if backup: + backup_file(file_path) + os.remove(file_path) + print(f"删除文件 {file_path} 成功") + +def main(): + path = input("请输入文件路径：") + pattern = input("请输入匹配模式：") + backup = input("是否备份文件（y/n）：").lower() == 'y' + + delete_files(path, pattern, backup) + + if backup: + rollback = input("是否回滚文件（y/n）：").lower() == 'y' + if rollback: + backup_list = glob.glob(os.path.join(path, pattern + '_*')) + if not backup_list: + print("没有找到备份文件") + return + + print("备份文件列表：") + for i, backup_path in enumerate(backup_list): + print(f"{i + 1}. {backup_path}") + + index = int(input("请选择要回滚的备份文件编号：")) + if index < 1 or index > len(backup_list): + print("无效的编号") + return + + backup_path = backup_list[index - 1] + file_path = backup_path[:-(len(datetime.datetime.now().strftime('%Y%m%d%H%M%S')) + 1)] + rollback_file(backup_path, file_path) + +if __name__ == '__main__': + main() diff --git a/file_operation/remove_files_with_rollback.py b/file_operation/remove_files_with_rollback.py new file mode 100644 index 0000000..03cf858 --- /dev/null +++ b/file_operation/remove_files_with_rollback.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import os +import shutil + + +def remove_files_with_rollback(target_dir, file_names): + # 创建回滚目录 + rollback_dir = os.path.join(target_dir, "rollback") + os.makedirs(rollback_dir, exist_ok=True) + + # 查找要删除的文件 + file_paths = [] + for file_name in file_names: + file_path = os.path.join(target_dir, file_name) + if os.path.exists(file_path): + file_paths.append(file_path) + + # 将文件移动到回滚目录 + for file_path in file_paths: + backup_path = os.path.join(rollback_dir, os.path.basename(file_path)) + shutil.move(file_path, backup_path) + + # 删除文件 + for file_path in file_paths: + os.remove(file_path) + + return True + + +def rollback_removed_files(target_dir): + # 获取回滚目录下的文件列表 + rollback_dir = os.path.join(target_dir, "rollback") + if not os.path.exists(rollback_dir): + print("回滚目录不存在，无法回滚") + return False + + file_names = os.listdir(rollback_dir) + + # 将文件从回滚目录复制回原位置 + for file_name in file_names: + backup_path = os.path.join(rollback_dir, file_name) + target_path = os.path.join(target_dir, file_name) + shutil.copy(backup_path, target_path) + + # 删除回滚目录 + shutil.rmtree(rollback_dir) + + return True + + +if __name__ == '__main__': + # 示例：删除/tmp目录中的test1.txt和test2.txt文件，并支持回滚操作 + target_dir = "/tmp" + file_names = ["test1.txt", "test2.txt"] + remove_files_with_rollback(target_dir, file_names) + + # 回滚删除的文件 + rollback_removed_files(target_dir) diff --git a/func/convert_fahrenheit_to_celsius.py b/func/convert_fahrenheit_to_celsius.py new file mode 100644 index 0000000..a70d841 --- /dev/null +++ b/func/convert_fahrenheit_to_celsius.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +""" +提示：华氏温度到摄氏温度的转换公式为：$C=(F - 32) \div 1.8$。 +""" +from __future__ import print_function + + +def convert_temperature(temperature): + """ + 华氏温度到摄氏温度的转换 + """ + try: + fahrenheit = float(temperature) + except Exception as err: + print(err) + else: + celsius = (fahrenheit - 32) / 1.8 + print('%.1f华氏度 = %.1f摄氏度' % (fahrenheit, celsius)) + return celsius + + +if __name__ == '__main__': + convert_temperature(100) diff --git a/func/gcd_lcm.py b/func/gcd_lcm.py new file mode 100644 index 0000000..84fb7a6 --- /dev/null +++ b/func/gcd_lcm.py @@ -0,0 +1,18 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +def gcd(x, y): + """求最大公约数""" + (x, y) = (y, x) if x > y else (x, y) + for factor in range(x, 0, -1): + if x % factor == 0 and y % factor == 0: + return factor + + +def lcm(x, y): + """求最小公倍数""" + return x * y // gcd(x, y) + + +if __name__ == '__main__': + print(gcd(4, 6)) + print(lcm(4, 6)) diff --git a/func/is_leap.py b/func/is_leap.py new file mode 100644 index 0000000..f3a812c --- /dev/null +++ b/func/is_leap.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- + +def is_leap(year): + """ + 输入年份 如果是闰年输出True 否则输出False + """ + try: + year = int(year) + except Exception as err: + print(err) + else: + return year % 4 == 0 and year % 100 != 0 or year % 400 == 0 + + +if __name__ == '__main__': + year = input('请输入年份: ') + res = is_leap(year) + print(f'{year} 是否闰年：{res}') diff --git a/func/rate_limit.py b/func/rate_limit.py index da96ff6..41c0f2a 100644 --- a/func/rate_limit.py +++ b/func/rate_limit.py @@ -2,6 +2,7 @@ import threading from functools import wraps + def rate_limited(max_per_second, mode='wait', delay_first_call=False): """ Decorator that make functions not be called faster than @@ -13,8 +14,10 @@ def rate_limited(max_per_second, mode='wait', delay_first_call=False): """ lock = threading.Lock() min_interval = 1.0 / float(max_per_second) + def decorate(func): last_time_called = [0.0] + @wraps(func) def rate_limited_function(*args, **kwargs): def run_func(): @@ -22,6 +25,7 @@ def run_func(): ret = func(*args, **kwargs) last_time_called[0] = time.perf_counter() return ret + lock.acquire() elapsed = time.perf_counter() - last_time_called[0] left_to_wait = min_interval - elapsed @@ -46,40 +50,71 @@ def run_func(): elif mode == 'kill': lock.release() return + return rate_limited_function + return decorate + @rate_limited(2, mode='wait') def print_num_wait(num): - print (num ) + """ + It prints the number and waits for the user to press enter. + :param num: the number to print + """ + print(num) -@rate_limited(1/2, mode='kill') + +@rate_limited(1 / 2, mode='kill') def print_num_kill(num): + """ + It prints the number of kills. + :param num: the number of kills + """ print(num) + @rate_limited(2, mode='kill', delay_first_call=True) def print_num_kill_delay(num): + """ + It prints the number of seconds to wait before killing a process + :param num: the number of times to run the test + """ print(num) -@rate_limited(1/3, mode='wait', delay_first_call=True) + +@rate_limited(1 / 3, mode='wait', delay_first_call=True) def print_num_wait_delay(num): + """ + It prints the number and waits for a second. + :param num: The number to print + """ print(num) -@rate_limited(1/3, mode='refresh_timer') + +@rate_limited(1 / 3, mode='refresh_timer') def print_num_wait_refresh(num): + """ + This function prints the number passed to it, waits for the + user to press enter, and then clears the + screen. + :param num: The number to print + """ print(num) + + if __name__ == "__main__": print('Rate limited at 2 per second at most') print() print("Mode is Kill") print("1 000 000 print requests sent to decorated function") - for i in range(1,1000000): + for i in range(1, 1000000): print_num_kill(i) print() print('Mode is Wait - default') print("10 print requests sent to decorated function") - for i in range(1,11): + for i in range(1, 11): print_num_wait(i) print() diff --git a/func/timeout_limit.py b/func/timeout_limit.py index a34e79f..b1ed272 100644 --- a/func/timeout_limit.py +++ b/func/timeout_limit.py @@ -6,10 +6,13 @@ import errno import os import signal +import time + class TimeoutError(Exception): pass + def timeout(seconds=10, error_message=os.strerror(errno.ETIME)): def decorator(func): def _handle_timeout(signum, frame): @@ -29,48 +32,52 @@ def wrapper(*args, **kwargs): return decorator - class timeout: def __init__(self, seconds=1, error_message='Timeout'): self.seconds = seconds self.error_message = error_message + def handle_timeout(self, signum, frame): raise TimeoutError(self.error_message) + def __enter__(self): signal.signal(signal.SIGALRM, self.handle_timeout) signal.alarm(self.seconds) + def __exit__(self, type, value, traceback): signal.alarm(0) with timeout(seconds=3): - sleep(4) - - + time.sleep(4) -import time - def RateLimited(maxPerSecond): minInterval = 1.0 / float(maxPerSecond) + def decorate(func): lastTimeCalled = [0.0] - def rateLimitedFunction(*args,**kargs): + + def rateLimitedFunction(*args, **kargs): elapsed = time.clock() - lastTimeCalled[0] leftToWait = minInterval - elapsed - if leftToWait>0: + if leftToWait > 0: time.sleep(leftToWait) - ret = func(*args,**kargs) + ret = func(*args, **kargs) lastTimeCalled[0] = time.clock() return ret + return rateLimitedFunction + return decorate + @RateLimited(2) # 2 per second at most def PrintNumber(num): - print num + print(num) + if __name__ == "__main__": - print "This should print 1,2,3... at about 2 per second." - for i in range(1,100): + print("This should print 1,2,3... at about 2 per second.") + for i in range(1, 100): PrintNumber(i) diff --git a/inotify/simple.py b/inotify/simple.py new file mode 100644 index 0000000..b3fc17e --- /dev/null +++ b/inotify/simple.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +''' +@File : simple.py +@Time : 2023/09/17 20:29:55 +@Author : jumploop +@Version : 1.0 +@Desc : None +''' + +import pyinotify + +wm = pyinotify.WatchManager() + + +class EventHandler(pyinotify.ProcessEvent): + """事件处理""" + + def process_IN_CREATE(self, event): + """处理文件创建""" + print("create:", event.pathname) + + def process_IN_DELETE(self, event): + """处理文件删除""" + print("delete:", event.pathname) + + +def main(): + """Program entrance""" + handler = EventHandler() + wm.add_watch('/tmp', pyinotify.IN_DELETE | pyinotify.IN_CREATE, rec=True) + notifier = pyinotify.Notifier(wm, handler) + notifier.loop() + + +if __name__ == '__main__': + main() diff --git a/inotify/simple2.py b/inotify/simple2.py new file mode 100644 index 0000000..6f82bd4 --- /dev/null +++ b/inotify/simple2.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +import pyinotify +import os +import pwd +import datetime + + +class EventHandler(pyinotify.ProcessEvent): + def process_default(self, event): + if event.mask & pyinotify.IN_CREATE: + file_info = os.stat(event.pathname) + modified_time = datetime.datetime.fromtimestamp(file_info.st_mtime) + modified_user = pwd.getpwuid(file_info.st_uid).pw_name + + with open("log.txt", "a") as log_file: + log_file.write("文件/目录创建:\n") + log_file.write("路径: {0}\n".format(event.pathname)) + log_file.write("创建时间: {0}\n".format(modified_time)) + log_file.write("创建用户: {0}\n\n".format(modified_user)) + if event.mask & pyinotify.IN_MODIFY: + file_info = os.stat(event.pathname) + modified_time = datetime.datetime.fromtimestamp(file_info.st_mtime) + modified_user = pwd.getpwuid(file_info.st_uid).pw_name + + with open("log.txt", "a") as log_file: + log_file.write("文件/目录修改:\n") + log_file.write("路径: {0}\n".format(event.pathname)) + log_file.write("修改时间: {0}\n".format(modified_time)) + log_file.write("修改用户: {0}\n\n".format(modified_user)) + elif event.mask & pyinotify.IN_DELETE: + deleted_time = datetime.datetime.now() + deleted_user = pwd.getpwuid(os.getuid()).pw_name + with open("log.txt", "a") as log_file: + log_file.write("文件/目录删除:\n") + log_file.write("路径: {0}\n".format(event.pathname)) + log_file.write("修改时间: {0}\n".format(deleted_time)) + log_file.write("修改用户: {0}\n\n".format(deleted_user)) + + +def main(): + """Program entrance""" + # 创建监控器实例并关联监控器类和目录 + watch_manager = pyinotify.WatchManager() + event_handler = EventHandler() + + watch_manager.add_watch('/opt/mirrors', pyinotify.ALL_EVENTS, rec=True) + notifier = pyinotify.Notifier(watch_manager, event_handler) + + # 启动监控器并开始监听目录的变化 + notifier.loop() + + +if __name__ == '__main__': + main() diff --git a/inotify/watchd.py b/inotify/watchd.py new file mode 100644 index 0000000..1e37f7b --- /dev/null +++ b/inotify/watchd.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +''' +@File : watchd.py +@Time : 2023/09/17 19:16:14 +@Author : jumploop +@Version : 1.0 +@Desc : None +''' + +import datetime +import time + +from watchdog.events import * +from watchdog.observers import Observer + + +class Logger: + """log record""" + + def __init__(self): + self.logger = logging.getLogger('watching') + self.logger.setLevel(logging.INFO) + + def statup(self, path, stream=True): + """statup""" + write_handler = logging.handlers.TimedRotatingFileHandler( + path, + when='midnight', + interval=1, + backupCount=7, + atTime=datetime.time(0, 0, 0, 0), + ) + write_handler.setFormatter( + logging.Formatter( + "%(asctime)s - %(levelname)s - %(module)s[:%(lineno)d] - %(message)s" + ) + ) + + if stream: + stream_handler = logging.StreamHandler() + stream_handler.setFormatter( + logging.Formatter( + "%(asctime)s - %(levelname)s - %(module)s[:%(lineno)d] - %(message)s" + ) + ) + self.logger.addHandler(stream_handler) + self.logger.addHandler(write_handler) + + return self.logger + + +class FileEventHandler(FileSystemEventHandler): + """file event handler""" + + def __init__(self): + FileSystemEventHandler.__init__(self) + + def on_moved(self, event): + if event.is_directory: + logger.info( + "directory moved from %s to %s", event.src_path, event.dest_path + ) + else: + logger.info("file moved from %s to %s", event.src_path, event.dest_path) + + def on_created(self, event): + if event.is_directory: + logger.info("directory created:%s", event.src_path) + else: + logger.info("file created:%s", event.src_path) + + def on_deleted(self, event): + if event.is_directory: + logger.info("directory deleted:%s", event.src_path) + else: + logger.info("file deleted:%s", event.src_path) + + def on_modified(self, event): + if event.is_directory: + logger.info("directory modified:%s", event.src_path) + else: + logger.info("file modified:%s", event.src_path) + + +if __name__ == "__main__": + logger = Logger().statup("test.log", stream=True) + observer = Observer() + event_handler = FileEventHandler() + observer.schedule(event_handler, "/opt", True) + observer.start() + try: + while True: + time.sleep(1) + except KeyboardInterrupt: + observer.stop() + observer.join() diff --git a/inotify/watching.py b/inotify/watching.py new file mode 100644 index 0000000..d577d9c --- /dev/null +++ b/inotify/watching.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +''' +@File : watching.py +@Time : 2023/09/17 17:34:17 +@Author : jumploop +@Version : 1.0 +@Desc : None +''' + +import os +import pwd +import shutil +import time +import datetime +import logging +import logging.handlers +from concurrent.futures import ThreadPoolExecutor + +import pyinotify + + +def copy_files(path): + """copy file""" + logger.info('copy file %s starting', path) + shutil.copy(path, "/tmp") + logger.info('copy file %s finished', path) + + +class Logger: + """log record""" + + def __init__(self): + self.logger = logging.getLogger('watching') + self.logger.setLevel(logging.INFO) + + def statup(self, path, stream=True): + """statup""" + write_handler = logging.handlers.TimedRotatingFileHandler( + path, + when='midnight', + interval=1, + backupCount=7, + atTime=datetime.time(0, 0, 0, 0), + ) + write_handler.setFormatter( + logging.Formatter( + "%(asctime)s - %(levelname)s - %(module)s[:%(lineno)d] - %(message)s" + ) + ) + + if stream: + stream_handler = logging.StreamHandler() + stream_handler.setFormatter( + logging.Formatter( + "%(asctime)s - %(levelname)s - %(module)s[:%(lineno)d] - %(message)s" + ) + ) + self.logger.addHandler(stream_handler) + self.logger.addHandler(write_handler) + + return self.logger + + +class MonitorEvent(pyinotify.ProcessEvent): + """monitor event""" + + def __init__(self, func): + super().__init__() + self.func = func + self.flag = time.time() + + def process_IN_CREATE(self, event): + """ + 文件被创建 + """ + if time.time() - self.flag > 0: + logger.info("%s 被创建！", event.pathname) + self.flag = time.time() + # 这里就可以做想做的事情了 + + def process_IN_MODIFY(self, event): + """文件被修改""" + if time.time() - self.flag > 3: + file_info = os.stat(event.pathname) + modified_time = datetime.datetime.fromtimestamp(file_info.st_mtime) + modified_user = pwd.getpwuid(file_info.st_uid).pw_name + logger.info( + "文件/目录修改:\n路径:%s, 修改时间: %s, 修改用户:%s", + event.pathname, + modified_time, + modified_user, + ) + self.flag = time.time() + # 这里就可以做想做的事情了 + with ThreadPoolExecutor(max_workers=10) as executor: + executor.submit(self.func, event.pathname) + + def process_IN_DELETE(self, event): + """文件被删除""" + if time.time() - self.flag > 0: + logger.info("%s 被删除！", event.pathname) + self.flag = time.time() + # 这里就可以做想做的事情了 + deleted_time = datetime.datetime.now() + deleted_user = pwd.getpwuid(os.getuid()).pw_name + logger.info( + "文件/目录删除:\n路径:%s, 修改时间: %s, 修改用户:%s", + event.pathname, + deleted_time, + deleted_user, + ) + + def process_IN_CLOSE_WRITE(self, event): + """文件写入完毕""" + if time.time() - self.flag > 0: + logger.info("%s 写入完毕！", event.pathname) + self.flag = time.time() + # 这里就可以做想做的事情了 + + +def main(): + """main function""" + path = '/opt' # 监控目录 + watch_manager = pyinotify.WatchManager() + watch_manager.add_watch(path, pyinotify.ALL_EVENTS, rec=True) + event = MonitorEvent(func=copy_files) + notifier = pyinotify.ThreadedNotifier(watch_manager, event) + notifier.loop() + + +if __name__ == '__main__': + logger = Logger().statup("test.log", stream=True) + main() diff --git a/iterator/demo.txt b/iterator/demo.txt new file mode 100644 index 0000000..9685a8b --- /dev/null +++ b/iterator/demo.txt @@ -0,0 +1,139 @@ +# $OpenBSD: sshd_config,v 1.100 2016/08/15 12:32:04 naddy Exp $ + +# This is the sshd server system-wide configuration file. See +# sshd_config(5) for more information. + +# This sshd was compiled with PATH=/usr/local/bin:/usr/bin + +# The strategy used for options in the default sshd_config shipped with +# OpenSSH is to specify options with their default value where +# possible, but leave them commented. Uncommented options override the +# default value. + +# If you want to change the port on a SELinux system, you have to tell +# SELinux about this change. +# semanage port -a -t ssh_port_t -p tcp #PORTNUMBER +# +#Port 22 +#AddressFamily any +#ListenAddress 0.0.0.0 +#ListenAddress :: + +HostKey /etc/ssh/ssh_host_rsa_key +#HostKey /etc/ssh/ssh_host_dsa_key +HostKey /etc/ssh/ssh_host_ecdsa_key +HostKey /etc/ssh/ssh_host_ed25519_key + +# Ciphers and keying +#RekeyLimit default none + +# Logging +#SyslogFacility AUTH +SyslogFacility AUTHPRIV +#LogLevel INFO + +# Authentication: + +#LoginGraceTime 2m +#PermitRootLogin yes +#StrictModes yes +#MaxAuthTries 6 +#MaxSessions 10 + +#PubkeyAuthentication yes + +# The default is to check both .ssh/authorized_keys and .ssh/authorized_keys2 +# but this is overridden so installations will only check .ssh/authorized_keys +AuthorizedKeysFile .ssh/authorized_keys + +#AuthorizedPrincipalsFile none + +#AuthorizedKeysCommand none +#AuthorizedKeysCommandUser nobody + +# For this to work you will also need host keys in /etc/ssh/ssh_known_hosts +#HostbasedAuthentication no +# Change to yes if you don't trust ~/.ssh/known_hosts for +# HostbasedAuthentication +#IgnoreUserKnownHosts no +# Don't read the user's ~/.rhosts and ~/.shosts files +#IgnoreRhosts yes + +# To disable tunneled clear text passwords, change to no here! +#PasswordAuthentication yes +#PermitEmptyPasswords no +PasswordAuthentication yes + +# Change to no to disable s/key passwords +#ChallengeResponseAuthentication yes +ChallengeResponseAuthentication no + +# Kerberos options +#KerberosAuthentication no +#KerberosOrLocalPasswd yes +#KerberosTicketCleanup yes +#KerberosGetAFSToken no +#KerberosUseKuserok yes + +# GSSAPI options +GSSAPIAuthentication yes +GSSAPICleanupCredentials no +#GSSAPIStrictAcceptorCheck yes +#GSSAPIKeyExchange no +#GSSAPIEnablek5users no + +# Set this to 'yes' to enable PAM authentication, account processing, +# and session processing. If this is enabled, PAM authentication will +# be allowed through the ChallengeResponseAuthentication and +# PasswordAuthentication. Depending on your PAM configuration, +# PAM authentication via ChallengeResponseAuthentication may bypass +# the setting of "PermitRootLogin without-password". +# If you just want the PAM account and session checks to run without +# PAM authentication, then enable this but set PasswordAuthentication +# and ChallengeResponseAuthentication to 'no'. +# WARNING: 'UsePAM no' is not supported in Red Hat Enterprise Linux and may cause several +# problems. +UsePAM yes + +#AllowAgentForwarding yes +#AllowTcpForwarding yes +#GatewayPorts no +X11Forwarding yes +#X11DisplayOffset 10 +#X11UseLocalhost yes +#PermitTTY yes +#PrintMotd yes +#PrintLastLog yes +#TCPKeepAlive yes +#UseLogin no +#UsePrivilegeSeparation sandbox +#PermitUserEnvironment no +#Compression delayed +#ClientAliveInterval 0 +#ClientAliveCountMax 3 +#ShowPatchLevel no +#UseDNS yes +#PidFile /var/run/sshd.pid +#MaxStartups 10:30:100 +#PermitTunnel no +#ChrootDirectory none +#VersionAddendum none + +# no default banner path +#Banner none + +# Accept locale-related environment variables +AcceptEnv LANG LC_CTYPE LC_NUMERIC LC_TIME LC_COLLATE LC_MONETARY LC_MESSAGES +AcceptEnv LC_PAPER LC_NAME LC_ADDRESS LC_TELEPHONE LC_MEASUREMENT +AcceptEnv LC_IDENTIFICATION LC_ALL LANGUAGE +AcceptEnv XMODIFIERS + +# override default of no subsystems +Subsystem sftp /usr/libexec/openssh/sftp-server + +# Example of overriding settings on a per-user basis +#Match User anoncvs +# X11Forwarding no +# AllowTcpForwarding no +# PermitTTY no +# ForceCommand cvs server diff --git a/iterator/fib.py b/iterator/fib.py new file mode 100644 index 0000000..f28152e --- /dev/null +++ b/iterator/fib.py @@ -0,0 +1,44 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# @Time : 2022/3/5 15:58 +# @Author : 一叶知秋 +# @File : fib.py +# @Software: PyCharm +class Fib: + def __init__(self, num): + self.num = num + self.a, self.b = 0, 1 + self.idx = 0 + + def __iter__(self): + return self + + def __next__(self): + if self.idx < self.num: + self.a, self.b = self.b, self.a + self.b + self.idx += 1 + return self.a + raise StopIteration() + + +def fib(num): + a, b = 0, 1 + idx = 0 + while idx < num: + a, b = b, a + b + idx += 1 + yield a + + +def fib2(num): + a, b = 0, 1 + for _ in range(num): + a, b = b, a + b + yield a + + +if __name__ == '__main__': + f = Fib(10) + print(list(f)) + print(list(fib(10))) + print(list(fib2(10))) diff --git a/iterator/itertools_demo.py b/iterator/itertools_demo.py new file mode 100644 index 0000000..936d7ff --- /dev/null +++ b/iterator/itertools_demo.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from itertools import dropwhile, islice, permutations, combinations, chain, zip_longest + +with open('demo.txt', 'r', encoding='utf-8') as file: + # 按照真值函数丢弃掉列表和迭代器前面的元素 + for line in dropwhile(lambda line: line.startswith('#'), file): + print(line, end='') + +for line in dropwhile(lambda e: e < 5, range(10)): + print(line, end='') +print() +print('--------------------------------') + +items = ['a', 'b', 'c', 1, 4, 10, 15] +# 对迭代器进行切片 +for x in islice(items, 3, None): + print(x) + +items = ['a', 'b', 'c'] +# 产生指定数目的元素的所有排列(顺序有关) +for p in permutations(items): + print(p) + +for p in permutations(items, 2): + print(p) + +# 求列表或生成器中指定数目的元素不重复的所有组合 +for c in combinations(items, 3): + print(c) + +for c in combinations(items, 2): + print(c) + +# 连接多个列表或者迭代器。 +for c in chain(range(3), range(4), [3, 2, 1]): + print(c, end='') +print() +print('--------------------------------') +# 类似于zip，不过已较长的列表和迭代器的长度为准 +for item in zip_longest('ABCD', 'xy'): + print(item, end='') diff --git a/json_loads/json_loads.py b/json_loads/json_loads.py new file mode 100644 index 0000000..91b1089 --- /dev/null +++ b/json_loads/json_loads.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +解决Python 2下的json.loads()导致的unicode编码问题 +How to get string objects instead of Unicode from JSON? +Updated for Python 2.7 and 3.x compatibility. +https://stackoverflow.com/questions/956867/how-to-get-string-objects-instead-of-unicode-from-json/33571117#33571117 +""" +import json + + +def json_load_byteified(file_handle): + """ + It converts all unicode strings in a JSON object to UTF-8 encoded strings + :param file_handle: The file handle to the JSON file that you want to load + """ + return _byteify(json.load(file_handle, object_hook=_byteify), ignore_dicts=True) + + +def json_loads_byteified(json_text): + """ + It converts all unicode strings in a JSON object to UTF-8 encoded strings + :param json_text: The JSON string that you want to convert to a Python object + """ + return _byteify(json.loads(json_text, object_hook=_byteify), ignore_dicts=True) + + +def _byteify(data, ignore_dicts=False): + """ + It converts all unicode strings in a data structure to UTF-8 encoded strings + :param data: the data to be converted + :param ignore_dicts: If this is True, then dictionaries are ignored, defaults + to False (optional) + """ + if isinstance(data, str): + return data + + # if this is a list of values, return list of byteified values + if isinstance(data, list): + return [_byteify(item, ignore_dicts=True) for item in data] + # if this is a dictionary, return dictionary of byteified keys and values + # but only if we haven't already byteified it + if isinstance(data, dict) and not ignore_dicts: + return { + _byteify(key, ignore_dicts=True): _byteify(value, ignore_dicts=True) + for key, value in data.items() # changed to .items() for python 2.7/3 + } + + # python 3 compatible duck-typing + # if this is a unicode string, return its string representation + if str(type(data)) == "<type 'unicode'>": + return data.encode('utf-8') + + # if it's anything else, return it in its original form + return data + + +if __name__ == '__main__': + # Example usage: + print(json_loads_byteified('{"Hello": "World"}')) + # {'Hello': 'World'} + print(json_loads_byteified('"I am a top-level string"')) + # 'I am a top-level string' + print(json_loads_byteified('7')) + # 7 + print(json_loads_byteified('["I am inside a list"]')) + # ['I am inside a list'] + print(json_loads_byteified('[[[[[[[["I am inside a big nest of lists"]]]]]]]]')) + # [[[[[[[['I am inside a big nest of lists']]]]]]]] + print(json_loads_byteified('{"foo": "bar", "things": [7, {"qux": "baz", "moo": {"cow": ["milk"]}}]}')) + # {'things': [7, {'qux': 'baz', 'moo': {'cow': ['milk']}}], 'foo': 'bar'} + print(json_load_byteified(open('somefile.json', encoding='utf-8'))) + # {'more json': 'from a file'} diff --git a/json_loads/somefile.json b/json_loads/somefile.json new file mode 100644 index 0000000..1219657 --- /dev/null +++ b/json_loads/somefile.json @@ -0,0 +1,3 @@ +{ + "more json": "from a file" +} \ No newline at end of file diff --git a/json_loads/unicode_convert.py b/json_loads/unicode_convert.py new file mode 100644 index 0000000..f3bb5c7 --- /dev/null +++ b/json_loads/unicode_convert.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +解决Python 2下的json.loads()导致的unicode编码问题 +""" +from __future__ import print_function + +import json + + +def unicode_convert(data): + """ + A recursive function that converts all unicode strings in a dictionary to utf-8 encoded strings. + :param data: The unicode string to be converted + """ + if isinstance(data, dict): + return {unicode_convert(key): unicode_convert(value) for key, value in data.items()} + if isinstance(data, list): + return [unicode_convert(element) for element in data] + return data.encode('utf-8') if str(type(data)) == "<type 'unicode'>" else data + + +json_text = json.dumps({'dns': None, 'ntp': None}) +print('json_text:', json_text) +dict_value = json.loads(json_text) +print(unicode_convert(dict_value)) + +datas = {"foo": "bar", "things": [7, {"qux": "baz", "moo": {"cow": ["milk"]}}]} +json_text2 = json.dumps(datas) +print('json_text2:', json_text2) +dict_value2 = json.loads(json_text2) +print('dict_value:', dict_value2) +print(unicode_convert(dict_value2)) + +print(unicode_convert(json.loads('{"foo": "bar", "things":"apple"}'))) + +print(unicode_convert(json.loads('{"foo": "bar"}'))) + +# python3 output: +# json_text: {"dns": null, "ntp": null} +# {'dns': None, 'ntp': None} +# json_text2: {"foo": "bar", "things": [7, {"qux": "baz", "moo": {"cow": ["milk"]}}]} +# dict_value: {'foo': 'bar', 'things': [7, {'qux': 'baz', 'moo': {'cow': ['milk']}}]} +# {'foo': 'bar', 'things': [7, {'qux': 'baz', 'moo': {'cow': ['milk']}}]} +# {'foo': 'bar', 'things': 'apple'} +# {'foo': 'bar'} + +# python2 output: +# json_text: {"ntp": null, "dns": null} +# {'ntp': None, 'dns': None} +# json_text2: {"things": [7, {"qux": "baz", "moo": {"cow": ["milk"]}}], "foo": "bar"} +# dict_value: {u'things': [7, {u'qux': u'baz', u'moo': {u'cow': [u'milk']}}], u'foo': u'bar'} +# {'things': [7, {'qux': 'baz', 'moo': {'cow': ['milk']}}], 'foo': 'bar'} +# {'things': 'apple', 'foo': 'bar'} +# {'foo': 'bar'} \ No newline at end of file diff --git a/leancloud/leancloud_api.py b/leancloud/leancloud_api.py index 4d0e28b..551d123 100644 --- a/leancloud/leancloud_api.py +++ b/leancloud/leancloud_api.py @@ -33,7 +33,7 @@ def save_obj(self, obj_dict): def get_skip_obj_list(self, skip_num=0, limit_num=30): query = self._query query.descending('ID') - query.skip(skip_num*limit_num) + query.skip(skip_num * limit_num) query.limit(limit_num) try: res = query.find() @@ -93,15 +93,15 @@ def solve_nums_class_obj(self, callback, nums, skip_num=0, limit_num=500): callback(obj_list) - if nums > (skip_total+limit_num): + if nums > (skip_total + limit_num): time.sleep(1) - self.solve_nums_class_obj(callback, nums, skip_num+1, limit_num) + self.solve_nums_class_obj(callback, nums, skip_num + 1, limit_num) def solve_all_class_obj(self, callback, skip_num=0, limit_num=500): """callback is a function that solve list of class object""" query = self._query query.descending('ID') - query.skip(skip_num*limit_num) + query.skip(skip_num * limit_num) query.limit(limit_num) try: obj_list = query.find() @@ -114,7 +114,7 @@ def solve_all_class_obj(self, callback, skip_num=0, limit_num=500): if len(obj_list) >= limit_num: time.sleep(1) - self.solve_all_class_obj(callback, skip_num+1, limit_num) + self.solve_all_class_obj(callback, skip_num + 1, limit_num) def get_obj_by_ID(self, obj_ID): query = self._query @@ -153,11 +153,11 @@ def exist_file(self, filename): """filename have suffix, judge by filename, maybe other field""" query = self._query query.equal_to('filename', filename) - try: # finded + try: # finded obj = query.first() - print filename, '----existed----' + print(filename, '----existed----') return True - except: # not find + except: # not find return False @staticmethod @@ -166,11 +166,11 @@ def fetch_data(url, retries=5): data = requests.get(url, timeout=5) except: if retries > 0: - print 'fetch...', retries, url + print('fetch...', retries, url) time.sleep(3) - return LeanCloudApi.fetch_data(url, retries-1) + return LeanCloudApi.fetch_data(url, retries - 1) else: - print 'fetch failed', url + print('fetch failed', url) data = None return data return data @@ -189,30 +189,30 @@ def upload_file_by_url(self, filename, url, tag_list=None): img_file.set('tag_list', tag_list) try: img_file.save() - print filename, '----uploaded----' - self.add_img_info(img_file.id) # save img_info after save + print(filename, '----uploaded----') + self.add_img_info(img_file.id) # save img_info after save except: - print 'save file failed', url + print('save file failed', url) time.sleep(5) return def upload_file(self, file_abspath): - filename = os.path.basename(file_abspath) # filename have suffix + filename = os.path.basename(file_abspath) # filename have suffix with open(file_abspath, 'r') as f: upload_file = File(filename, f) upload_file.save() - print 'uploaded', file_abspath + print('uploaded', file_abspath) img_file = self._class() img_file.set('File', upload_file) img_file.set('filename', filename) tag_list = LeanCloudApi.get_tag_list(filename) img_file.set('tag_list', tag_list) img_file.save() - self.add_img_info(img_file.id) # save img_info after save + self.add_img_info(img_file.id) # save img_info after save @staticmethod def is_img_file(filename): - suffix = filename.split('.')[-1].lower() # note: remember ingore case + suffix = filename.split('.')[-1].lower() # note: remember ingore case img_types = set(['jpg', 'png', 'gif', 'jpeg', 'bmp']) return suffix in img_types @@ -222,4 +222,3 @@ def get_tag_list(filename): jieba.setLogLevel(60) seg_list = jieba.cut(txt) return [i for i in seg_list if len(i) >= 2] - diff --git a/log/demo.py b/log/demo.py new file mode 100644 index 0000000..da9858b --- /dev/null +++ b/log/demo.py @@ -0,0 +1,11 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from logger import Logger + + +logger = Logger().logger + +logger.info('hello world') +logger.warning('warning') +logger.error("error") +logger.critical('critical') diff --git a/log/example_logger.py b/log/example_logger.py new file mode 100644 index 0000000..0f2f728 --- /dev/null +++ b/log/example_logger.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import logging + +# 创建一个日志记录器 +logger = logging.getLogger('example_logger') +logger.setLevel(logging.DEBUG) + +# 创建一个日志处理程序，将日志输出到控制台 +console_handler = logging.StreamHandler() +console_handler.setLevel(logging.DEBUG) +formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') +console_handler.setFormatter(formatter) +logger.addHandler(console_handler) + +# 创建一个日志处理程序，将日志输出到文件 +file_handler = logging.FileHandler('example.log') +file_handler.setLevel(logging.DEBUG) +file_handler.setFormatter(formatter) +logger.addHandler(file_handler) + +# 记录一些日志 +logger.debug('This is a debug message') +logger.info('This is an info message') +logger.warning('This is a warning message') +logger.error('This is an error message') +logger.critical('This is a critical message') diff --git a/log/logger.py b/log/logger.py new file mode 100644 index 0000000..5902315 --- /dev/null +++ b/log/logger.py @@ -0,0 +1,86 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import logging +from logging import getLogger, StreamHandler, Formatter, DEBUG +from logging.handlers import RotatingFileHandler +from os import makedirs +from os.path import exists, join as path_join, dirname, basename + + +class Logger: + """ 封装的用于类的通用功能""" + + def __init__( + self, + name="root", + level=DEBUG, + filename: str = None, + max_size: int = 1, + backup_count: int = 8): + """ + :param name: 日志名称，默认为root + :param level: 日志等级，默认为DEBUG + :param filename: 日志文件名称，默认为该类所在的文件的 文件名.log + :param max_size: 单个日志文件最大大小，单位 MB，默认为1M + :param backup_count: 除名称为 filename的文件外, 备份日志的数量 + """ + self.log_handlers_clean() + self._logger = getLogger(name) + self._logger.setLevel(level) + self.level = level + formatter = Formatter('%(asctime)s - %(levelname)s - %(filename)s:%(lineno)s - %(message)s', + datefmt='%Y-%m-%d %H:%M:%S') + # ====================================================== + log_dir = path_join(dirname(__file__), 'logs') + # log_dir 目录，如果不存在则创建 + self.__check_dirs(log_dir) + filename = filename or basename(__name__) + self.filename = self.__check_log_suffix(filename) + # log 文件绝对路径 + self.log_file_path = path_join(log_dir, self.filename) + self.max_size = max_size * 1024 ** 2 + self.backup_count = backup_count + self.console_handler(formatter) + self.file_handler(formatter) + self.log_handlers_clean() + + def file_handler(self, formatter): + file_handler = RotatingFileHandler( + filename=self.log_file_path, + maxBytes=self.max_size, + backupCount=self.backup_count, + encoding='utf-8' + ) + file_handler.setLevel(self.level) + file_handler.setFormatter(formatter) + self._logger.addHandler(file_handler) + + def console_handler(self, formatter): + # log print + console_handler = StreamHandler() + console_handler.setLevel(self.level) + console_handler.setFormatter(formatter) + self._logger.addHandler(console_handler) + + @staticmethod + def __check_dirs(dir_path: str): + """检查目录是否存在，不存在则递归创建""" + if not exists(dir_path): + makedirs(dir_path) + + @staticmethod + def __check_log_suffix(log_name: str) -> str: + """确保log_name是以'.log'""" + suffix = '.log' + if not log_name.endswith(suffix): + log_name = f"{log_name}{suffix}" + + return log_name + + @property + def logger(self): + return self._logger + + def log_handlers_clean(self): + """clean logging root log handlers""" + logging.root.handlers = [] diff --git a/log/simple_log.py b/log/simple_log.py new file mode 100644 index 0000000..f354296 --- /dev/null +++ b/log/simple_log.py @@ -0,0 +1,13 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import logging + +# 配置日志记录器 +logging.basicConfig(filename='example.log', level=logging.DEBUG) + +# 记录日志 +logging.debug('Debug message') +logging.info('Info message') +logging.warning('Warning message') +logging.error('Error message') +logging.critical('Critical message') diff --git a/mail/cloudsend.py b/mail/cloudsend.py index a4ce02b..9348ffa 100644 --- a/mail/cloudsend.py +++ b/mail/cloudsend.py @@ -17,4 +17,4 @@ } r = requests.post(url, files={}, data=params) -print r.text +print(r.text) diff --git a/mail/send_mail_demo2.py b/mail/send_mail_demo2.py new file mode 100644 index 0000000..3b73825 --- /dev/null +++ b/mail/send_mail_demo2.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# smtplib模块主要负责发送邮件：是一个发送邮件的动作，连接邮箱服务器，登录邮箱，发送邮件（有发件人，收信人，邮件内容）。 +import smtplib +# email模块主要负责构造邮件：指的是邮箱页面显示的一些构造，如发件人，收件人，主题，正文，附件等 +from email.mime.text import MIMEText +from email.header import Header + +import openpyxl + +""" +需要安装 openpyxl库 + +pip install openpyxl +""" +# ============================= +# 定义变量 +# ============================= +# 第三方 SMTP 服务 + +mail_host = "smtp.qq.com" # 设置服务器 +mail_user = "172212595@qq.com" # 用户名 +mail_pass = "*******" # 授权码 qq邮箱获取地址：https://jingyan.baidu.com/article/6079ad0eb14aaa28fe86db5a.html + +sender = '172212595@qq.com' # 发送邮件的邮箱 +to_addrs = ['172212595@qq.com'] # 接收邮件，可设置为你的QQ邮箱或者其他邮箱 + +# ============================= +# 配置邮件内容；参考：https://www.cnblogs.com/yufeihlf/p/5726619.html +# ============================= +mail_msg = """ +<p>Python 邮件发送测试,此处是正文...</p> +<p><a href="http://www.runoob.com">这是一个链接</a></p> +""" +message = MIMEText(mail_msg, 'html', 'utf-8') +message['From'] = Header("菜鸟教程", 'utf-8') # 发送者 +message['To'] = Header("测试", 'utf-8') # 接收者 + +subject = 'Python SMTP 邮件测试' +message['Subject'] = Header(subject, 'utf-8') + + +def send_mail(): + # ============================= + # 发送邮件配置 + # ============================= + try: + smtpObj = smtplib.SMTP() # 实例化SMTP() + smtpObj.connect(mail_host, 25) # mail_host 设置服务器；25 为 SMTP 默认端口号 + smtpObj.login(mail_user, mail_pass) # mail_user 发件人用户名；mail_pass 发件人邮箱授权码 + smtpObj.sendmail(sender, to_addrs, + message.as_string()) # sender 发件人邮箱；to_addrs 邮件接收者地址。多个采用字符串列表['接收地址1','接收地址2','接收地址3',...]单个：'接收地址' ； message 发送的内容 + smtpObj.quit() # 用于结束SMTP会话。 + print("邮件发送成功") + except smtplib.SMTPException: + print("Error: 无法发送邮件") + +if __name__ == '__main__': + send_mail() \ No newline at end of file diff --git a/math_demo/find_daffodils_number.py b/math_demo/find_daffodils_number.py new file mode 100644 index 0000000..a9f81fc --- /dev/null +++ b/math_demo/find_daffodils_number.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +寻找水仙花数 +说明： +水仙花数也被称为超完全数字不变数、自恋数、自幂数、阿姆斯特朗数，它是一个3位数， +该数字每个位上数字的立方之和正好等于它本身，例如：$1^3 + 5^3+ 3^3=153$。 +""" +from __future__ import print_function + + +def find_daffodils_number(): + """寻找水仙花数""" + for num in range(100, 1000): + low = num % 10 + mid = num // 10 % 10 + high = num // 100 + if low**3 + mid**3 + high**3 == num: + print(f'水仙花数：{num}') + + +def main(): + """do main""" + find_daffodils_number() + + +if __name__ == '__main__': + main() diff --git a/monitoring_files/obserber.py b/monitoring_files/obserber.py new file mode 100644 index 0000000..f3f187b --- /dev/null +++ b/monitoring_files/obserber.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import sys +import logging +import time +from watchdog.observers import Observer +from watchdog.events import LoggingEventHandler + +# https://mp.weixin.qq.com/s/G6Jf3zr14ELpNbriY_qvJg + +if __name__ == '__main__': + logging.basicConfig(level=logging.INFO, format='%(asctime)s-%(message)s', datefmt='%Y-%m-%d %H:%M:%S') + path = sys.argv[1] if len(sys.argv) > 1 else '.' + # 生成事件处理器对象 + event_handle = LoggingEventHandler() + # 生成监控器对象 + observer = Observer() + # 注册事件处理器，配置监控目录 + observer.schedule(event_handle, path, recursive=True) + # 监控器启动-创建线程 + observer.start() + # 以下代码是为了保持主线程运行 + try: + while 1: + time.sleep(1) + except KeyboardInterrupt: + observer.stop() + # 等待其他的子线程执行结束之后，主线程在终止 + observer.join() diff --git a/network/ip_to_binary.py b/network/ip_to_binary.py new file mode 100644 index 0000000..3f07c9b --- /dev/null +++ b/network/ip_to_binary.py @@ -0,0 +1,17 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +def ip_to_binary(ip): + # 将IP地址拆分成四个数字 + nums = ip.split('.') + # 将每个数字转换为8位的二进制数 + binary_nums = [bin(int(num))[2:].zfill(8) for num in nums] + # 将四个二进制数拼接起来 + binary_str = ''.join(binary_nums) + # 将32位二进制数转换为字符串格式并返回 + return binary_str + + +if __name__ == '__main__': + ip = '192.168.1.1' + binary_ip = ip_to_binary(ip) + print(binary_ip) diff --git a/network/ip_to_num.py b/network/ip_to_num.py new file mode 100644 index 0000000..942fbc8 --- /dev/null +++ b/network/ip_to_num.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +def ip_to_num(ip): + """ip to num""" + parts = list(map(int, ip.split("."))) + seg0 = parts[0] << 24 + seg1 = parts[1] << 16 + seg2 = parts[2] << 8 + seg3 = parts[3] + return seg0 | seg1 | seg2 | seg3 + + +def ip2num(ip): + "将点分十进制IP地址转换成十进制整数" + items = [int(x) for x in ip.split(".")] + return sum([items[i] << [24, 16, 8, 0][i] for i in range(4)]) + + +if __name__ == "__main__": + print(ip_to_num('192.168.0.1')) + print(ip2num('192.168.0.1')) diff --git a/network/num_to_ip.py b/network/num_to_ip.py new file mode 100644 index 0000000..55a4fd6 --- /dev/null +++ b/network/num_to_ip.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +def num_to_ip(num): + """num to ip converts""" + parts = [num >> 24 & 0xff, num >> 16 & 0xff, num >> 8 & 0xff, num & 0xff] + return ".".join(map(str, parts)) + + +def num2ip(num): + "将十进制整数IP转换成点分十进制的字符串IP地址" + return ".".join([str(num >> x & 0xff) for x in [24, 16, 8, 0]]) + + +if __name__ == '__main__': + print(num_to_ip(3232235521)) + print(num2ip(3232235521)) diff --git a/office_automation/read_data.py b/office_automation/read_data.py new file mode 100644 index 0000000..9e3419a --- /dev/null +++ b/office_automation/read_data.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import pandas as pd +import json +import pymysql +from sqlalchemy import create_engine + +# 打开数据库连接 +conn = pymysql.connect(host='localhost', + port=3306, + user='root', + passwd='xxxx', + charset='utf8' + ) +engine = create_engine('mysql+pymysql://root:xxxx@localhost/mysql?charset=utf8') + + +def read_excel(file): + df_excel = pd.read_excel(file) + return df_excel + + +def read_json(file): + with open(file, 'r') as json_f: + df_json = pd.read_json(json_f) + return df_json + + +def read_sql(table): + sql_cmd = 'SELECT * FROM %s' % table + df_sql = pd.read_sql(sql_cmd, engine) + return df_sql + + +def read_csv(file): + df_csv = pd.read_csv(file) + return df_csv diff --git a/office_automation/send_mail.py b/office_automation/send_mail.py new file mode 100644 index 0000000..1967320 --- /dev/null +++ b/office_automation/send_mail.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import smtplib +from email import encoders +from email.header import Header +from email.utils import parseaddr, formataddr +from email.mime.application import MIMEApplication +from email.mime.multipart import MIMEMultipart +from email.mime.text import MIMEText + +# 发件人邮箱 +asender = "PyDataAnalysis@163.com" +# 收件人邮箱 +areceiver = "PyDataAnalysis@163.com" +# 抄送人邮箱 +acc = "数据分析@163.com" +# 邮箱主题 +asubject = "谢谢关注" +# 发件人地址 +from_addr = "PyDataAnalysis@163.com" +# 邮箱授权码 +password = "####" +# 邮件设置 +msg = MIMEMultipart() +msg['Subject'] = asubject +msg['to'] = areceiver +msg['Cc'] = acc +msg['from'] = "数据分析" +# 邮件正文 +body = "你好，欢迎关注@公众号：数据分析，您的关注就是我继续创作的动力！" +msg.attach(MIMEText(body, 'plain', 'utf-8')) +# 添加附件 +htmlFile = 'C:/Users/10799/problem.html' +html = MIMEApplication(open(htmlFile, 'rb').read()) +html.add_header('Content-Disposition', 'attachment', filename='html') + +msg.attach(html) +# 设置邮箱服务器地址和接口 +smtp_server = "smtp.163.com" +server = smtplib.SMTP(smtp_server, 25) +server.set_debuglevel(1) +# 登录邮箱 +server.login(from_addr, password) +# 发生邮箱 +server.sendmail(from_addr, areceiver.split(',') + acc.split(','), msg.as_string()) +# 断开服务器连接 +server.quit() diff --git a/orm/SQLAlchemy_demo/hello/hello.cfg b/orm/SQLAlchemy_demo/hello/hello.cfg new file mode 100644 index 0000000..c8502bf --- /dev/null +++ b/orm/SQLAlchemy_demo/hello/hello.cfg @@ -0,0 +1,5 @@ +SQLALCHEMY_DATABASE_URI = 'sqlite:///test.db' +SQLALCHEMY_TRACK_MODIFICATIONS = False +SQLALCHEMY_ECHO = False +SECRET_KEY = '\xfb\x12\xdf\xa1@i\xd6>V\xc0\xbb\x8fp\x16#Z\x0b\x81\xeb\x16' +DEBUG = True diff --git a/orm/SQLAlchemy_demo/hello/hello.py b/orm/SQLAlchemy_demo/hello/hello.py new file mode 100644 index 0000000..707eac2 --- /dev/null +++ b/orm/SQLAlchemy_demo/hello/hello.py @@ -0,0 +1,66 @@ +from datetime import datetime + +from flask import flash +from flask import Flask +from flask import redirect +from flask import render_template +from flask import request +from flask import url_for +from flask_sqlalchemy import SQLAlchemy + + +app = Flask(__name__) +app.config.from_pyfile("hello.cfg") +db = SQLAlchemy(app) + + +class Todo(db.Model): + __tablename__ = "todos" + id = db.Column("todo_id", db.Integer, primary_key=True) + title = db.Column(db.String(60)) + text = db.Column(db.String) + done = db.Column(db.Boolean) + pub_date = db.Column(db.DateTime) + + def __init__(self, title, text): + self.title = title + self.text = text + self.done = False + self.pub_date = datetime.utcnow() + + +@app.route("/") +def show_all(): + return render_template( + "show_all.html", todos=Todo.query.order_by(Todo.pub_date.desc()).all() + ) + + +@app.route("/new", methods=["GET", "POST"]) +def new(): + if request.method == "POST": + if not request.form["title"]: + flash("Title is required", "error") + elif not request.form["text"]: + flash("Text is required", "error") + else: + todo = Todo(request.form["title"], request.form["text"]) + db.session.add(todo) + db.session.commit() + flash("Todo item was successfully created") + return redirect(url_for("show_all")) + return render_template("new.html") + + +@app.route("/update", methods=["POST"]) +def update_done(): + for todo in Todo.query.all(): + todo.done = f"done.{todo.id}" in request.form + flash("Updated status") + db.session.commit() + return redirect(url_for("show_all")) + + +if __name__ == "__main__": + db.create_all() + app.run() diff --git a/orm/SQLAlchemy_demo/hello/templates/layout.html b/orm/SQLAlchemy_demo/hello/templates/layout.html new file mode 100644 index 0000000..672c3b9 --- /dev/null +++ b/orm/SQLAlchemy_demo/hello/templates/layout.html @@ -0,0 +1,10 @@ +<!doctype html> +<title>SQLAlchemy Todo +

SQLAlchemy Todo

+{%- for category, message in get_flashed_messages(with_categories=true) %} +

{{ + "Error: " if category == 'error' }}{{ message }}

+{%- endfor %} +{% block body %}{% endblock %} +
+
SQLAlchemy and Flask powered todo application
diff --git a/orm/SQLAlchemy_demo/hello/templates/new.html b/orm/SQLAlchemy_demo/hello/templates/new.html new file mode 100644 index 0000000..63ce5e7 --- /dev/null +++ b/orm/SQLAlchemy_demo/hello/templates/new.html @@ -0,0 +1,14 @@ +{% extends "layout.html" %} +{% block body %} +

New Todo Item

+
+

+ Title: + +

Text: +

+

+ + Back to list +

+{% endblock %} diff --git a/orm/SQLAlchemy_demo/hello/templates/show_all.html b/orm/SQLAlchemy_demo/hello/templates/show_all.html new file mode 100644 index 0000000..62fe44c --- /dev/null +++ b/orm/SQLAlchemy_demo/hello/templates/show_all.html @@ -0,0 +1,25 @@ +{% extends "layout.html" %} +{% block body %} +

All Items

+
+ + + + + + {%- endfor %} +
# + Title + Date + Done? + {%- for todo in todos %} +
{{ todo.id }} + {{ todo.title }} + {{ todo.pub_date.strftime('%Y-%m-%d %H:%M') }} + +
{{ todo.text }}
+

+ + New item +

+{% endblock %} diff --git a/orm/SQLAlchemy_demo/students/student.cfg b/orm/SQLAlchemy_demo/students/student.cfg new file mode 100644 index 0000000..986b7e8 --- /dev/null +++ b/orm/SQLAlchemy_demo/students/student.cfg @@ -0,0 +1,5 @@ +SQLALCHEMY_DATABASE_URI = 'sqlite:///students.sqlite3' +SQLALCHEMY_TRACK_MODIFICATIONS = False +SQLALCHEMY_ECHO = False +SECRET_KEY = '\xfb\x12\xdf\xa1@i\xd6>V\xc0\xbb\x8fp\x16#Z\x0b\x81\xeb\x16' +DEBUG = True diff --git a/orm/SQLAlchemy_demo/students/students.py b/orm/SQLAlchemy_demo/students/students.py new file mode 100644 index 0000000..dd5090e --- /dev/null +++ b/orm/SQLAlchemy_demo/students/students.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# https://www.yiibai.com/flask/flask_sqlalchemy.html +from flask import Flask, render_template, redirect, flash, url_for, request +from flask_sqlalchemy import SQLAlchemy + +app = Flask(__name__) +app.config.from_pyfile("student.cfg") + +db = SQLAlchemy(app) + + +class Students(db.Model): + __tablename__ = 'students' + id = db.Column('student_id', db.Integer, primary_key=True) + name = db.Column(db.String(100)) + city = db.Column(db.String(50)) + addr = db.Column(db.String(200)) + pin = db.Column(db.String(10)) + + def __init__(self, name, city, address, pin): + self.name = name + self.city = city + self.addr = address + self.pin = pin + + +@app.route('/') +def show_all(): + return render_template('show_all.html', students=Students.query.all()) + + +@app.route('/new', methods=['GET', 'POST']) +def new(): + if request.method == 'POST': + if request.form['name'] and request.form['city'] and request.form['addr']: + student = Students(request.form['name'], request.form['city'], + request.form['addr'], request.form['pin']) + + db.session.add(student) + db.session.commit() + + flash('Record was successfully added') + return redirect(url_for('show_all')) + else: + flash('Please enter all the fields', 'error') + return render_template('new.html') + + +if __name__ == '__main__': + db.create_all() + app.run() diff --git a/orm/SQLAlchemy_demo/students/templates/layout.html b/orm/SQLAlchemy_demo/students/templates/layout.html new file mode 100644 index 0000000..1e3f317 --- /dev/null +++ b/orm/SQLAlchemy_demo/students/templates/layout.html @@ -0,0 +1,10 @@ + +Flask示例 +

学生信息 - Flask SQLAlchemy示例

+{%- for category, message in get_flashed_messages(with_categories=true) %} +

{{ + "Error: " if category == 'error' }}{{ message }}

+{%- endfor %} +{% block body %}{% endblock %} +
+
SQLAlchemy and Flask powered students application
diff --git a/orm/SQLAlchemy_demo/students/templates/new.html b/orm/SQLAlchemy_demo/students/templates/new.html new file mode 100644 index 0000000..d8a02aa --- /dev/null +++ b/orm/SQLAlchemy_demo/students/templates/new.html @@ -0,0 +1,24 @@ +{% extends "layout.html" %} + +{% block body %} + +
+
+
+
+
+
+
+
+
+ +
+{% endblock %} diff --git a/orm/SQLAlchemy_demo/students/templates/show_all.html b/orm/SQLAlchemy_demo/students/templates/show_all.html new file mode 100644 index 0000000..72d395a --- /dev/null +++ b/orm/SQLAlchemy_demo/students/templates/show_all.html @@ -0,0 +1,29 @@ +{% extends "layout.html" %} + +{% block body %} + +

学生 (添加 + )

+ + + + + + + + + + + + + {% for student in students %} + + + + + + + {% endfor %} + +
姓名城市地址Pin
{{ student.name }}{{ student.city }}{{ student.addr }}{{ student.pin }}
+{% endblock %} \ No newline at end of file diff --git a/os/kill_process.py b/os/kill_process.py index f22323d..43362a6 100644 --- a/os/kill_process.py +++ b/os/kill_process.py @@ -1,6 +1,6 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- - +import os import subprocess, signal def kill_process_name(process_name): @@ -8,7 +8,7 @@ def kill_process_name(process_name): out, err = p.communicate() for line in out.splitlines(): - if name in line: + if process_name in line: pid = int(line.split(None, 1)[0]) os.kill(pid, signal.SIGKILL) diff --git a/os/vpstest.sh b/os/vpstest.sh new file mode 100644 index 0000000..9013a2d --- /dev/null +++ b/os/vpstest.sh @@ -0,0 +1,270 @@ +#!/usr/bin/env bash +PATH=/bin:/sbin:/usr/bin:/usr/sbin:/usr/local/bin:/usr/local/sbin:~/bin +export PATH +# Description: Auto test download & I/O speed script +# Copyright (C) 2015 - 2016 Teddysun +# Thanks: LookBack +# Teddysun: https://teddysun.com/444.html +# Toyo: https://doub.io +# For https://VPS.BEST +# VPS测试脚本，方便你更好地了解你的服务器 +# 地址：https://github.com/ToyoDAdoubiBackup/vpstest + +RED='\033[0;31m' && GREEN='\033[0;32m' && YELLOW='\033[0;33m' && PLAIN='\033[0m' +next() { printf "%-70s\n" "-" | sed 's/\s/-/g'; } +get_opsy() { + [[ -f /etc/redhat-release ]] && awk '{print ($1,$3~/^[0-9]/?$3:$4)}' /etc/redhat-release && return + [[ -f /etc/os-release ]] && awk -F'[= "]' '/PRETTY_NAME/{print $3,$4,$5}' /etc/os-release && return + [[ -f /etc/lsb-release ]] && awk -F'[="]+' '/DESCRIPTION/{print $2}' /etc/lsb-release && return +} +check_sys() { + if [[ -f /etc/redhat-release ]]; then + release="centos" + elif grep -q -E -i "debian" /etc/issue; then + release="debian" + elif grep -q -E -i "ubuntu" /etc/issue; then + release="ubuntu" + elif grep -q -E -i "centos|red hat|redhat" /etc/issue; then + release="centos" + elif grep -q -E -i "debian" /proc/version; then + release="debian" + elif grep -q -E -i "ubuntu" /proc/version; then + release="ubuntu" + elif grep -q -E -i "centos|red hat|redhat" /proc/version; then + release="centos" + fi + bit=$(uname -m) +} +Installation_dependency() { + if [[ ${release} == "centos" ]]; then + yum update + yum -y install mtr curl time virt-what + [[ ${action} == "a" ]] && yum -y install make gcc gcc-c++ gdbautomake autoconf + else + apt-get update + apt-get -y install curl mtr time virt-what + [[ ${action} == "a" ]] && apt-get -y install make gcc gdb automake autoconf + fi +} +get_info() { + logfile="test.log" + IP=$(curl -s myip.ipip.net | awk -F ' ' '{print $2}' | awk -F '：' '{print $2}') + IPaddr=$(curl -s myip.ipip.net | awk -F '：' '{print $3}') + if [[ -z "$IP" ]]; then + IP=$(curl -s ip.cn | awk -F ' ' '{print $2}' | awk -F '：' '{print $2}') + IPaddr=$(curl -s ip.cn | awk -F '：' '{print $3}') + fi + time=$(date '+%Y-%m-%d %H:%I:%S') + backtime=$(date +%Y-%m-%d) + vm=$(virt-what) + [[ -z ${vm} ]] && vm="none" + cname=$(awk -F: '/model name/ {name=$2} END {print name}' /proc/cpuinfo | sed 's/^[ \t]*//;s/[ \t]*$//') + cores=$(awk -F: '/model name/ {core++} END {print core}' /proc/cpuinfo) + freq=$(awk -F: '/cpu MHz/ {freq=$2} END {print freq}' /proc/cpuinfo | sed 's/^[ \t]*//;s/[ \t]*$//') + tram=$(free -m | awk '/Mem/ {print $2}') + uram=$(free -m | awk '/Mem/ {print $3}') + swap=$(free -m | awk '/Swap/ {print $2}') + uswap=$(free -m | awk '/Swap/ {print $3}') + up=$(awk '{a=$1/86400;b=($1%86400)/3600;c=($1%3600)/60} {printf("%d days, %d hour %d min\n",a,b,c)}' /proc/uptime) + load=$(w | head -1 | awk -F'load average:' '{print $2}' | sed 's/^[ \t]*//;s/[ \t]*$//') + opsy=$(get_opsy) + arch=$(uname -m) + lbit=$(getconf LONG_BIT) + kern=$(uname -r) + ipv6=$(wget -qO- -t1 -T2 ipv6.icanhazip.com) + disk_size1=("$(LANG=C df -ahPl | grep -wvE '\-|none|tmpfs|devtmpfs|by-uuid|chroot|Filesystem' | awk '{print $2}')") + disk_size2=("$(LANG=C df -ahPl | grep -wvE '\-|none|tmpfs|devtmpfs|by-uuid|chroot|Filesystem' | awk '{print $3}')") + disk_total_size=$(calc_disk "${disk_size1[@]}") + disk_used_size=$(calc_disk "${disk_size2[@]}") +} +system_info() { + clear + echo "========== 开始记录测试信息 ==========" >$logfile + echo "测试时间：$time" | tee -a $logfile + next | tee -a $logfile + echo "CPU model : $cname" | tee -a $logfile + echo "Number of cores : $cores" | tee -a $logfile + echo "CPU frequency : $freq MHz" | tee -a $logfile + echo "Total size of Disk : $disk_total_size GB ($disk_used_size GB Used)" | tee -a $logfile + echo "Total amount of Mem : $tram MB ($uram MB Used)" | tee -a $logfile + echo "Total amount of Swap : $swap MB ($uswap MB Used)" | tee -a $logfile + echo "System uptime : $up" | tee -a $logfile + echo "Load average : $load" | tee -a $logfile + echo "OS : $opsy" | tee -a $logfile + echo "Arch : $arch ($lbit Bit)" | tee -a $logfile + echo "Kernel : $kern" | tee -a $logfile + echo "ip : $IP" | tee -a "$logfile" + echo "ipaddr : $IPaddr" | tee -a "$logfile" + echo "vm : $vm" | tee -a "$logfile" + next | tee -a $logfile +} +calc_disk() { + local total_size + local array + total_size=0 + array=("$@") + for size in "${array[@]}"; do + [[ "${size}" == "0" ]] && size_t=0 || size_t=${size:0:${#size}-1} + [[ "${size:(-1)}" == "M" ]] && size=$(awk 'BEGIN{printf "%.1f", '"$size_t"' / 1024}') + [[ "${size:(-1)}" == "T" ]] && size=$(awk 'BEGIN{printf "%.1f", '"$size_t"' * 1024}') + [[ "${size:(-1)}" == "G" ]] && size=${size_t} + total_size=$(awk 'BEGIN{printf "%.1f", '"$total_size"' + '"$size"'}') + done + echo "${total_size}" +} +io_test_1() { + (LANG=C dd if=/dev/zero of=test_$$ bs=64k count=4k oflag=dsync && rm -f test_$$) 2>&1 | awk -F, '{io=$NF} END { print io}' | sed 's/^[ \t]*//;s/[ \t]*$//' +} +io_test_2() { + (LANG=C dd if=/dev/zero of=test_$$ bs=8 count=256 conv=fdatasync && rm -f test_$$) 2>&1 | awk -F, '{io=$NF} END { print io}' | sed 's/^[ \t]*//;s/[ \t]*$//' +} +io_test() { + io1=$($1) + echo "I/O speed(1st run) : $io1" | tee -a $logfile + io2=$($1) + echo "I/O speed(2nd run) : $io2" | tee -a $logfile + io3=$($1) + echo "I/O speed(3rd run) : $io3" | tee -a $logfile + ioraw1=$(echo "$io1" | awk 'NR==1 {print $1}') + [[ "$(echo "$io1" | awk 'NR==1 {print $2}')" == "GB/s" ]] && ioraw1=$(awk 'BEGIN{print '"$ioraw1"' * 1024}') + ioraw2=$(echo "$io2" | awk 'NR==1 {print $1}') + [[ "$(echo "$io2" | awk 'NR==1 {print $2}')" == "GB/s" ]] && ioraw2=$(awk 'BEGIN{print '"$ioraw2"' * 1024}') + ioraw3=$(echo "$io3" | awk 'NR==1 {print $1}') + [[ "$(echo "$io3" | awk 'NR==1 {print $2}')" == "GB/s" ]] && ioraw3=$(awk 'BEGIN{print '"$ioraw3"' * 1024}') + ioall=$(awk 'BEGIN{print '"$ioraw1"' + '"$ioraw2"' + '"$ioraw3"'}') + ioavg=$(awk 'BEGIN{printf "%.1f", '"$ioall"' / 3}') + echo "Average I/O speed : $ioavg MB/s" | tee -a $logfile + next | tee -a $logfile +} +speed_test() { + local speedtest + local ipaddress + local nodeName + speedtest=$(wget -4O /dev/null -T300 "$1" 2>&1 | awk '/\/dev\/null/ {speed=$3 $4} END {gsub(/\(|\)/,"",speed); print speed}') + ipaddress="$(ping -c1 -n "$(awk -F'/' '{print $3}' <<<"$1")" | awk -F'[()]' '{print $2;exit}')" + nodeName=$2 + printf "${YELLOW}%-32s${GREEN}%-24s${RED}%-14s${PLAIN}\n" "${nodeName}:" "${ipaddress}:" "${speedtest}" +} +speed() { + printf "%-32s%-24s%-14s\n" "Node Name:" "IPv4 address:" "Download Speed" + # speed_test 'http://cachefly.cachefly.net/100mb.test' 'CacheFly' + speed_test 'http://speedtest.tokyo.linode.com/100MB-tokyo.bin' 'Linode, Tokyo, JP' + speed_test 'http://speedtest.tokyo2.linode.com/100MB-tokyo2.bin' 'Linode, Tokyo2, JP' + speed_test 'http://speedtest.singapore.linode.com/100MB-singapore.bin' 'Linode, Singapore, SG' + speed_test 'http://speedtest.fremont.linode.com/100MB-fremont.bin' 'Linode, Fremont, CA' + speed_test 'http://speedtest.newark.linode.com/100MB-newark.bin' 'Linode, Newark, NJ' + speed_test 'http://speedtest.london.linode.com/100MB-london.bin' 'Linode, London, UK' + speed_test 'http://speedtest.frankfurt.linode.com/100MB-frankfurt.bin' 'Linode, Frankfurt, DE' + speed_test 'http://speedtest.tok02.softlayer.com/downloads/test100.zip' 'Softlayer, Tokyo, JP' + speed_test 'http://speedtest.sng01.softlayer.com/downloads/test100.zip' 'Softlayer, Singapore, SG' + speed_test 'http://speedtest.sng01.softlayer.com/downloads/test100.zip' 'Softlayer, Seoul, KR' + speed_test 'http://speedtest.hkg02.softlayer.com/downloads/test100.zip' 'Softlayer, HongKong, CN' + speed_test 'http://speedtest.dal13.softlayer.com/downloads/test100.zip' 'Softlayer, Dallas, TX' + speed_test 'http://speedtest.sea01.softlayer.com/downloads/test100.zip' 'Softlayer, Seattle, WA' + speed_test 'http://speedtest.fra02.softlayer.com/downloads/test100.zip' 'Softlayer, Frankfurt, DE' + speed_test 'http://speedtest.par01.softlayer.com/downloads/test100.zip' 'Softlayer, Paris, FR' + speed_test 'http://mirror.hk.leaseweb.net/speedtest/100mb.bin' 'Leaseweb, HongKong, CN' + speed_test 'http://mirror.sg.leaseweb.net/speedtest/100mb.bin' 'Leaseweb, Singapore, SG' + speed_test 'http://chi.testfiles.ubiquityservers.com/100mb.txt' 'Leaseweb, Chicago, US' + # speed_test 'http://phx.testfiles.ubiquityservers.com/100mb.txt' 'Leaseweb, Phoenix, US' + speed_test 'http://mirror.wdc1.us.leaseweb.net/speedtest/100mb.bin' 'Leaseweb, Washington D.C., US' + speed_test 'http://chi.testfiles.ubiquityservers.com/100mb.txt' 'Leaseweb, Chicago, US' + speed_test 'http://mirror.sfo12.us.leaseweb.net/speedtest/100mb.bin' 'Leaseweb, San Francisco, US' + speed_test 'http://mirror.nl.leaseweb.net/speedtest/100mb.bin' 'Leaseweb, Netherlands, NL' + speed_test 'http://proof.ovh.ca/files/100Mio.dat' 'OVH, Montreal, CA' + speed_test 'http://183.60.137.161/dl.softmgr.qq.com/original/game/DuiZhanSetup1_8_4_2042_win10.exe' 'ChinaTelecom, Dongguan, CN' + speed_test 'http://14.29.72.152/dl.softmgr.qq.com/original/game/DuiZhanSetup1_8_4_2042_win10.exe' 'ChinaTelecom, Foshan, CN' + speed_test 'http://222.73.131.40/dl.softmgr.qq.com/original/game/DuiZhanSetup1_8_4_2042_win10.exe' 'ChinaTelecom, Shanghai, CN' + speed_test 'http://163.177.153.71/dl.softmgr.qq.com/original/game/DuiZhanSetup1_8_4_2042_win10.exe' 'ChinaUnicom, Foshan, CN' + speed_test 'http://112.90.51.172/dl.softmgr.qq.com/original/game/DuiZhanSetup1_8_4_2042_win10.exe' 'ChinaUnicom, Zhongshan, CN' + speed_test 'http://111.202.98.38/dl.softmgr.qq.com/original/game/DuiZhanSetup1_8_4_2042_win10.exe' 'ChinaUnicom, Beijing, CN' + speed_test 'http://223.82.245.41/dl.softmgr.qq.com/original/game/DuiZhanSetup1_8_4_2042_win10.exe' 'ChinaMobile, Jiangxi, CN' + speed_test 'http://61.233.79.5/setup.exe' 'ChinaTieTong, Henan, CN' + speed_test 'http://101.4.60.106/setup.exe' 'CERNET, Beijing, CN' + speed_test 'http://mirrors.opencas.org/apache/ode/apache-ode-war-1.3.6.zip' 'CSTNET, Beijing, CN' + speed_test 'http://tpdb.speed2.hinet.net/test_100m.zip' 'Hinet, Taiwan, TW' + next | tee -a $logfile +} +speed_test_cli() { + echo "===== 开始speedtest =====" | tee -a $logfile + wget -q --no-check-certificate https://raw.githubusercontent.com/sivel/speedtest-cli/master/speedtest.py && + python speedtest.py --share | tee -a $logfile + echo -e "===== speedtest完成 =====" | tee -a $logfile + rm -rf speedtest.py + next | tee -a $logfile +} +mtrgo() { + mtrurl=$1 + nodename=$2 + echo "===== 测试 [$nodename] 到此服务器的去程路由 =====" | tee -a $logfile + mtrgostr=$(curl -s "$mtrurl") + echo -e "$mtrgostr" >mtrlog.log + mtrgostrback=$(curl -s -d @mtrlog.log "http://test.91yun.org/traceroute.php") + rm -rf mtrlog.log + echo -e "$mtrgostrback" | awk -F '^' '{printf("%-2s\t%-16s\t%-35s\t%-30s\t%-25s\n",$1,$2,$3,$4,$5)}' | tee -a $logfile + echo -e "===== [$nodename] 去程路由测试结束 =====" | tee -a $logfile +} +mtrback() { + echo "===== 测试 [$2] 的回程路由 =====" | tee -a $logfile + mtr -r -c 10 $1 | tee -a $logfile + echo -e "===== 回程 [$2] 路由测试结束 =====" | tee -a $logfile +} +tracetest() { + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=254&ip=$IP" "北京电信" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=275&ip=$IP" "上海电信" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=163&ip=$IP" "金华电信" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=274&ip=$IP" "广州电信" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=20&ip=$IP" "厦门电信CN2" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=7&ip=$IP" "天津联通" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=12&ip=$IP" "重庆联通" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=298&ip=$IP" "金华联通" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=339&ip=$IP" "福州联通" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=2&ip=$IP" "天津移动" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=315&ip=$IP" "镇江移动" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=160&ip=$IP" "北京教育网" + mtrgo "http://www.ipip.net/traceroute.php?as=1&a=get&n=1&id=41&ip=$IP" "北京鹏博士" + next | tee -a $logfile +} +backtracetest() { + mtrback "183.60.137.161" "东莞电信" + mtrback "14.29.72.152" "佛山电信" + mtrback "222.73.131.40" "上海电信" + mtrback "163.177.153.71" "佛山联通" + mtrback "112.90.51.172" "舟山联通" + mtrback "111.202.98.38" "北京联通" + mtrback "223.82.245.41" "江西移动" + mtrback "101.4.60.106" "北京科技网" + next | tee -a $logfile +} +benchtest() { + if ! wget -qc http://lamp.teddysun.com/files/UnixBench5.1.3.tgz; then + echo "UnixBench 5.1.3.tgz 下载失败" && exit 1 + fi + tar -xzf UnixBench5.1.3.tgz + cd UnixBench/ || exit + make + echo "===== 开始测试CPU性能測試 =====" | tee -a ../"${logfile}" + ./Run + benchfile=$(ls results/ | grep -v '\.') + cat results/"${benchfile}" >>../"${logfile}" + echo "===== CPU性能测试结束 =====" | tee -a ../"${logfile}" + cd .. + rm -rf UnixBench5.1.3.tgz UnixBench + next | tee -a $logfile +} +go() { + check_sys + Installation_dependency + get_info + system_info + io_test "io_test_1" + io_test "io_test_2" + speed_test_cli + speed + tracetest + backtracetest + [[ ${action} == "a" ]] && benchtest + echo "測試脚本执行完毕！日志文件: ${logfile}" +} +action=$1 +go diff --git a/pandas/data_handle.ipynb b/pandas/data_handle.ipynb new file mode 100644 index 0000000..6eb9ad0 --- /dev/null +++ b/pandas/data_handle.ipynb @@ -0,0 +1,6550 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 37, + "source": [ + "import pandas as pd\r\n", + "import numpy as np\r\n", + "df = pd.DataFrame({\"id\":[1001,1002,1003,1004,1005,1006],\r\n", + "\"date\":pd.date_range('20130102', periods=6),\r\n", + "\"city\":['Beijing ', 'SH', ' guangzhou ', 'Shenzhen', 'shanghai', 'BEIJING '],\r\n", + "\"age\":[23,44,54,32,34,32],\r\n", + "\"category\":['100-A','100-B','110-A','110-C','210-A','130-F'],\r\n", + "\"price\":[1200,np.nan,2133,5433,np.nan,4432]},\r\n", + "columns =['id','date','city','category','age',\r\n", + "'price'])\r\n", + "df.to_excel(\"name.xlsx\",index=False)\r\n" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 38, + "source": [ + "df=pd.read_excel(\"name.xlsx\")" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 39, + "source": [ + "df" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id date city category age price\n", + "0 1001 2013-01-02 Beijing 100-A 23 1200.0\n", + "1 1002 2013-01-03 SH 100-B 44 NaN\n", + "2 1003 2013-01-04 guangzhou 110-A 54 2133.0\n", + "3 1004 2013-01-05 Shenzhen 110-C 32 5433.0\n", + "4 1005 2013-01-06 shanghai 210-A 34 NaN\n", + "5 1006 2013-01-07 BEIJING 130-F 32 4432.0" + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "execution_count": 39 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 40, + "source": [ + "# 查看数据表的维度\r\n", + "df.shape" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(6, 6)" + ] + }, + "metadata": {}, + "execution_count": 40 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 41, + "source": [ + "#数据表信息\r\n", + "df.info()" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 6 entries, 0 to 5\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 id 6 non-null int64 \n", + " 1 date 6 non-null datetime64[ns]\n", + " 2 city 6 non-null object \n", + " 3 category 6 non-null object \n", + " 4 age 6 non-null int64 \n", + " 5 price 4 non-null float64 \n", + "dtypes: datetime64[ns](1), float64(1), int64(2), object(2)\n", + "memory usage: 416.0+ bytes\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 42, + "source": [ + "#查看数据表各列格式\r\n", + "df.dtypes" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "id int64\n", + "date datetime64[ns]\n", + "city object\n", + "category object\n", + "age int64\n", + "price float64\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 42 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 43, + "source": [ + "#查看单列格式\r\n", + "df[\"id\"].dtypes" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "metadata": {}, + "execution_count": 43 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 44, + "source": [ + "#检查数据空值\r\n", + "df.isnull()" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id date city category age price\n", + "0 False False False False False False\n", + "1 False False False False False True\n", + "2 False False False False False False\n", + "3 False False False False False False\n", + "4 False False False False False True\n", + "5 False False False False False False" + ], + "text/html": [ + "
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iddatecitycategoryagepricegenderm-pointpaygroupsign
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iddatecitycategory_xagepricegenderm-pointpaygroupsigncategory_ysize
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iddatecitycategory_xagepricegenderm-pointpaygroupsigncategory_ysize
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5210032013-01-04guangzhou110-A542133.0male20YlowNaN110A
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" + ] + }, + "metadata": {}, + "execution_count": 239 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 240, + "source": [ + "#标准差\r\n", + "df_inner['price'].std()" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1523.3516337339847" + ] + }, + "metadata": {}, + "execution_count": 240 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 241, + "source": [ + "#两个字段间的协方差\r\n", + "df_inner['price'].cov(df_inner['m-point'])" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "17263.0" + ] + }, + "metadata": {}, + "execution_count": 241 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 242, + "source": [ + "#数据表中所有字段间的协方差\r\n", + "df_inner.cov()" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id age price m-point sign\n", + "id 3.5 -0.7 1946.0 25.4 NaN\n", + "age -0.7 118.3 -1353.5 -31.0 NaN\n", + "price 1946.0 -1353.5 2320600.2 17263.0 NaN\n", + "m-point 25.4 -31.0 17263.0 214.0 NaN\n", + "sign NaN NaN NaN NaN NaN" + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "execution_count": 242 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 243, + "source": [ + "#相关性分析\r\n", + "df_inner['price'].corr(df_inner['m-point'])" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.7746565925361043" + ] + }, + "metadata": {}, + "execution_count": 243 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 244, + "source": [ + "#数据表相关性分析\r\n", + "df_inner.corr()" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id age price m-point sign\n", + "id 1.000000 -0.034401 0.682824 0.928096 NaN\n", + "age -0.034401 1.000000 -0.081689 -0.194833 NaN\n", + "price 0.682824 -0.081689 1.000000 0.774657 NaN\n", + "m-point 0.928096 -0.194833 0.774657 1.000000 NaN\n", + "sign NaN NaN NaN NaN NaN" + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "execution_count": 244 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 245, + "source": [ + "#输出到Excel格式\r\n", + "df_inner.to_excel('Excel_to_Python.xlsx',sheet_name='bluewhale_cc')" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 246, + "source": [ + "#输出到CSV格式\r\n", + "df_inner.to_csv('Excel_to_Python.csv')" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [], + "outputs": [], + "metadata": {} + } + ], + "metadata": { + "orig_nbformat": 4, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/pandas/simple.ipynb b/pandas/simple.ipynb new file mode 100644 index 0000000..14b9b61 --- /dev/null +++ b/pandas/simple.ipynb @@ -0,0 +1,1708 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 入门Pandas不可不知的技巧" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "\n", + "# 首先，导入包\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "1 小红 18 女 570 深圳\n", + "2 小孙 27 男 642 广州\n", + "3 王小 20 男 590 武汉\n", + "4 关宇 28 男 601 深圳\n", + "5 刘蓓 18 女 619 广州\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
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nameagesexscoreaddress
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" + }, + "metadata": {}, + "execution_count": 3 + } + ], + "source": [ + "# 方式1：通过字典直接创建\n", + "df1 = pd.DataFrame({\n", + " \"name\":[\"小明\",\"小红\",\"小孙\",\"王小\",\"关宇\",\"刘蓓\",\"张菲\"],\n", + " \"age\":[20,18,27,20,28,18,25],\n", + " \"sex\":[\"男\",\"女\",\"男\",\"男\",\"男\",\"女\",\"女\"],\n", + " \"score\":[669,570,642,590,601,619,701],\n", + " \"address\":[\"北京\",\"深圳\",\"广州\",\"武汉\",\"深圳\",\"广州\",\"长沙\"]\n", + "})\n", + "df1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df1.to_excel('学生信息.xlsx',index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "1 小红 18 女 570 深圳\n", + "2 小孙 27 男 642 广州\n", + "3 王小 20 男 590 武汉\n", + "4 关宇 28 男 601 深圳\n", + "5 刘蓓 18 女 619 广州\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
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nameagesexscoreaddress
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" + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "pd.read_excel('学生信息.xlsx')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(7, 5)" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ], + "source": [ + "# shape表示数据是由多少行和列组成：\n", + "\n", + "df1.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['name', 'age', 'sex', 'score', 'address'], dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "# 查看字段属性名称\n", + "df1.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "name object\n", + "age int64\n", + "sex object\n", + "score int64\n", + "address object\n", + "dtype: object" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "# 查看属性的数据类型\n", + "df1.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 False False False False False\n", + "1 False False False False False\n", + "2 False False False False False\n", + "3 False False False False False\n", + "4 False False False False False\n", + "5 False False False False False\n", + "6 False False False False False" + ], + "text/html": "
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" + }, + "metadata": {}, + "execution_count": 9 + } + ], + "source": [ + "# 查看数据是否缺失\n", + "df1.isnull() # # 如果缺失显示为True，否则显示False" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "name 0\n", + "age 0\n", + "sex 0\n", + "score 0\n", + "address 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "# 统计缺失值的个数。一个True计数一次\n", + "df1.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=7, step=1)" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "# 查看数据行索引\n", + "df1.index" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ], + "source": [ + "# 查看数据描述信息\n", + "df1.describe" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " age score\n", + "count 7.000000 7.000000\n", + "mean 22.285714 627.428571\n", + "std 4.270608 46.227182\n", + "min 18.000000 570.000000\n", + "25% 19.000000 595.500000\n", + "50% 20.000000 619.000000\n", + "75% 26.000000 655.500000\n", + "max 28.000000 701.000000" + ], + "text/html": "
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" + }, + "metadata": {}, + "execution_count": 13 + } + ], + "source": [ + "# 查看数据统计值\n", + "df1.describe() # 统计值信息只会显示类型为数值型的数据统计值信息：\n", + "# 统计值的结果包含：个数count、均值mean、方差std、最值min\\max、四分位数25%、中位数50%、四分之三分位数75%。" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "1 小红 18 女 570 深圳\n", + "2 小孙 27 男 642 广州\n", + "3 王小 20 男 590 武汉\n", + "4 关宇 28 男 601 深圳" + ], + "text/html": "
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nameagesexscoreaddress
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" + }, + "metadata": {}, + "execution_count": 14 + } + ], + "source": [ + "# 03 查看头尾文件\n", + "# 通过head和tail方法能够快速查看数据的头尾文件。\n", + "df1.head() # 默认是查看前5行数据\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "1 小红 18 女 570 深圳\n", + "2 小孙 27 男 642 广州" + ], + "text/html": "
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nameagesexscoreaddress
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" + }, + "metadata": {}, + "execution_count": 15 + } + ], + "source": [ + "df1.head(3) # 指定显示的行数\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "2 小孙 27 男 642 广州\n", + "3 王小 20 男 590 武汉\n", + "4 关宇 28 男 601 深圳\n", + "5 刘蓓 18 女 619 广州\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
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nameagesexscoreaddress
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" + }, + "metadata": {}, + "execution_count": 16 + } + ], + "source": [ + "df1.tail() # 默认尾部5行\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "4 关宇 28 男 601 深圳\n", + "5 刘蓓 18 女 619 广州\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
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nameagesexscoreaddress
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" + }, + "metadata": {}, + "execution_count": 17 + } + ], + "source": [ + "df1.tail(3) # 指定尾部3行数据\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0 小明\n", + "1 小红\n", + "2 小孙\n", + "3 王小\n", + "4 关宇\n", + "5 刘蓓\n", + "6 张菲\n", + "Name: name, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], + "source": [ + "# 04 花样取数\n", + "# 从pandas的DataFrame数据框中取出我们想要的数据，然后进行处理\n", + "# 取出某个字段的数据\n", + "# 我们取出name这列的数据：\n", + "name=df1['name']\n", + "name\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age\n", + "0 小明 20\n", + "1 小红 18\n", + "2 小孙 27\n", + "3 王小 20\n", + "4 关宇 28\n", + "5 刘蓓 18\n", + "6 张菲 25" + ], + "text/html": "
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" + }, + "metadata": {}, + "execution_count": 19 + } + ], + "source": [ + "# 取出多个字段的数据\n", + "name_age = df1[['name','age']]\n", + "name_age" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " age score\n", + "0 20 669\n", + "1 18 570\n", + "2 27 642\n", + "3 20 590\n", + "4 28 601\n", + "5 18 619\n", + "6 25 701" + ], + "text/html": "
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" + }, + "metadata": {}, + "execution_count": 20 + } + ], + "source": [ + "# 1、选择单个数据类型\n", + "df1.select_dtypes(include='int64')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "1 小红 18 女 570 深圳\n", + "2 小孙 27 男 642 广州\n", + "3 王小 20 男 590 武汉\n", + "4 关宇 28 男 601 深圳\n", + "5 刘蓓 18 女 619 广州\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
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nameagesexscoreaddress
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6张菲25女701长沙
\n
" + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "# 2、同时选择多个类型\n", + "df1.select_dtypes(include=['int64','object']) # 因为数据中只有int64,object，所以我们全部选出来了。" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name sex address\n", + "0 小明 男 北京\n", + "1 小红 女 深圳\n", + "2 小孙 男 广州\n", + "3 王小 男 武汉\n", + "4 关宇 男 深圳\n", + "5 刘蓓 女 广州\n", + "6 张菲 女 长沙" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
namesexaddress
0小明男北京
1小红女深圳
2小孙男广州
3王小男武汉
4关宇男深圳
5刘蓓女广州
6张菲女长沙
\n
" + }, + "metadata": {}, + "execution_count": 23 + } + ], + "source": [ + "# 选择排除某些数据类型之外的数据：\n", + "df1.select_dtypes(exclude='int64') # 选择除了int64类型之外的数据\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "3 王小 20 男 590 武汉" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddress
0小明20男669北京
3王小20男590武汉
\n
" + }, + "metadata": {}, + "execution_count": 24 + } + ], + "source": [ + "# 1、直接通过判断大小来取数：\n", + "df1[df1['age']==20] # 年龄等于20" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "1 小红 18 女 570 深圳\n", + "2 小孙 27 男 642 广州\n", + "4 关宇 28 男 601 深圳\n", + "5 刘蓓 18 女 619 广州\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddress
1小红18女570深圳
2小孙27男642广州
4关宇28男601深圳
5刘蓓18女619广州
6张菲25女701长沙
\n
" + }, + "metadata": {}, + "execution_count": 25 + } + ], + "source": [ + "df1[df1['age']!=20] # 年龄不等于20" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "2 小孙 27 男 642 广州\n", + "3 王小 20 男 590 武汉\n", + "4 关宇 28 男 601 深圳\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
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nameagesexscoreaddress
0小明20男669北京
2小孙27男642广州
3王小20男590武汉
4关宇28男601深圳
6张菲25女701长沙
\n
" + }, + "metadata": {}, + "execution_count": 26 + } + ], + "source": [ + "df1[df1['age']>=20] # 年龄大于等于20" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "3 王小 20 男 590 武汉\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
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nameagesexscoreaddress
0小明20男669北京
3王小20男590武汉
6张菲25女701长沙
\n
" + }, + "metadata": {}, + "execution_count": 28 + } + ], + "source": [ + "# 多个判断条件连用\n", + "df1[(df1[\"age\"] >= 20) & (df1[\"age\"] < 27)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京" + ], + "text/html": "
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nameagesexscoreaddress
0小明20男669北京
\n
" + }, + "metadata": {}, + "execution_count": 29 + } + ], + "source": [ + "# 根据字符串取数\n", + "# 1、通过单个条件取数\n", + "# 1、单条数据\n", + "df1[df1[\"name\"] == \"小明\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "2 小孙 27 男 642 广州\n", + "4 关宇 28 男 601 深圳" + ], + "text/html": "
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nameagesexscoreaddress
0小明20男669北京
2小孙27男642广州
4关宇28男601深圳
\n
" + }, + "metadata": {}, + "execution_count": 30 + } + ], + "source": [ + "# 2、通过多个条件取数\n", + "# 选择姓名是小明，或者年龄大于25的数据\n", + "\n", + "df1[(df1['name']=='小明') | (df1['age']>25)]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "1 小红 18 女 570 深圳\n", + "2 小孙 27 男 642 广州" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddress
0小明20男669北京
1小红18女570深圳
2小孙27男642广州
\n
" + }, + "metadata": {}, + "execution_count": 31 + } + ], + "source": [ + "# 字符串的开始、结尾、包含函数\n", + "# str.startswith(string)\n", + "# str.endswith(string)\n", + "# str.contains(string)\n", + "# 1、取出以“小”开头的姓名\n", + "df1[df1['name'].str.startswith('小')]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "4 关宇 28 男 601 深圳" + ], + "text/html": "
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nameagesexscoreaddress
4关宇28男601深圳
\n
" + }, + "metadata": {}, + "execution_count": 32 + } + ], + "source": [ + "# 以“关”开始\n", + "df1[df1['name'].str.startswith('关')]" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
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nameagesexscoreaddress
6张菲25女701长沙
\n
" + }, + "metadata": {}, + "execution_count": 33 + } + ], + "source": [ + "# 3、以“菲”结尾\n", + "df1[df1['name'].str.endswith('菲')]" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "1 小红 18 女 570 深圳\n", + "2 小孙 27 男 642 广州\n", + "3 王小 20 男 590 武汉" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddress
0小明20男669北京
1小红18女570深圳
2小孙27男642广州
3王小20男590武汉
\n
" + }, + "metadata": {}, + "execution_count": 34 + } + ], + "source": [ + "# 取出包含“小”的数据：不管小是在开头，还是结尾都会被选出来\n", + "df1[df1['name'].str.contains('小')] # 上面的王小不是小开头，但是包含小，所以也被选出来。" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "4 关宇 28 男 601 深圳\n", + "5 刘蓓 18 女 619 广州\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddress
4关宇28男601深圳
5刘蓓18女619广州
6张菲25女701长沙
\n
" + }, + "metadata": {}, + "execution_count": 35 + } + ], + "source": [ + "# 字符串取反操作，取反符号是波浪线：~\n", + "# 下面的例子是：取出名字name中不包含小的数据，只有3个人名字中没有小字。\n", + "df1[~df1['name'].str.contains('小')]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# 05 切片取数\n", + "# 切片是Python中存在的概念，在pandas中同样可以使用。切片中存在3个概念：start、stop、step\n", + "\n", + "# start：起始索引，包含\n", + "# stop：结束索引，不包含\n", + "# step：步长，可正可负；\n", + "# 写法为：[start:stop:step]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "1 小红 18 女 570 深圳\n", + "2 小孙 27 男 642 广州\n", + "3 王小 20 男 590 武汉" + ], + "text/html": "
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nameagesexscoreaddress
0小明20男669北京
1小红18女570深圳
2小孙27男642广州
3王小20男590武汉
\n
" + }, + "metadata": {}, + "execution_count": 37 + } + ], + "source": [ + "# 1、通过下面的3个案例说明：起始索引默认从0开始，步长默认是1\n", + "df1[:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "1 小红 18 女 570 深圳\n", + "2 小孙 27 男 642 广州\n", + "3 王小 20 男 590 武汉" + ], + "text/html": "
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nameagesexscoreaddress
0小明20男669北京
1小红18女570深圳
2小孙27男642广州
3王小20男590武汉
\n
" + }, + "metadata": {}, + "execution_count": 38 + } + ], + "source": [ + "df1[0:4] # 默认步长为1" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "1 小红 18 女 570 深圳\n", + "2 小孙 27 男 642 广州\n", + "3 王小 20 男 590 武汉" + ], + "text/html": "
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nameagesexscoreaddress
0小明20男669北京
1小红18女570深圳
2小孙27男642广州
3王小20男590武汉
\n
" + }, + "metadata": {}, + "execution_count": 39 + } + ], + "source": [ + "df1[0:4:1] # 指定起始、结束索引，步长" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "4 关宇 28 男 601 深圳\n", + "5 刘蓓 18 女 619 广州\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
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nameagesexscoreaddress
4关宇28男601深圳
5刘蓓18女619广州
6张菲25女701长沙
\n
" + }, + "metadata": {}, + "execution_count": 41 + } + ], + "source": [ + "# 2、指定起始索引，不指定结束索引，表示一直取到数据末尾\n", + "df1[4:] # 从索引4开始取到末尾\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "2 小孙 27 男 642 广州" + ], + "text/html": "
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nameagesexscoreaddress
0小明20男669北京
2小孙27男642广州
\n
" + }, + "metadata": {}, + "execution_count": 42 + } + ], + "source": [ + "# 3、改变步长的值\n", + "df1[0:4:2] # 改变步长：每隔2个值取一行数据\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "2 小孙 27 男 642 广州" + ], + "text/html": "
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nameagesexscoreaddress
0小明20男669北京
2小孙27男642广州
\n
" + }, + "metadata": {}, + "execution_count": 43 + } + ], + "source": [ + "df1[:4:2] # 默认从0开始\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "0 小明 20 男 669 北京\n", + "2 小孙 27 男 642 广州\n", + "4 关宇 28 男 601 深圳\n", + "6 张菲 25 女 701 长沙" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddress
0小明20男669北京
2小孙27男642广州
4关宇28男601深圳
6张菲25女701长沙
\n
" + }, + "metadata": {}, + "execution_count": 45 + } + ], + "source": [ + "# 4、只指定步长\n", + "df1[::2] # 从头到尾，步长为2" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "6 张菲 25 女 701 长沙\n", + "5 刘蓓 18 女 619 广州\n", + "4 关宇 28 男 601 深圳\n", + "3 王小 20 男 590 武汉\n", + "2 小孙 27 男 642 广州\n", + "1 小红 18 女 570 深圳\n", + "0 小明 20 男 669 北京" + ], + "text/html": "
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nameagesexscoreaddress
6张菲25女701长沙
5刘蓓18女619广州
4关宇28男601深圳
3王小20男590武汉
2小孙27男642广州
1小红18女570深圳
0小明20男669北京
\n
" + }, + "metadata": {}, + "execution_count": 46 + } + ], + "source": [ + "# 步长为负数\n", + "# 1、步长为-1，默认是倒序输出结果\n", + "df1[::-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "4 关宇 28 男 601 深圳\n", + "3 王小 20 男 590 武汉\n", + "2 小孙 27 男 642 广州\n", + "1 小红 18 女 570 深圳" + ], + "text/html": "
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nameagesexscoreaddress
4关宇28男601深圳
3王小20男590武汉
2小孙27男642广州
1小红18女570深圳
\n
" + }, + "metadata": {}, + "execution_count": 47 + } + ], + "source": [ + "# 2、步长为负，指定起始和终止索引，起始索引大于终止索引\n", + "df1[4:0:-1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address\n", + "6 张菲 25 女 701 长沙\n", + "5 刘蓓 18 女 619 广州\n", + "4 关宇 28 男 601 深圳\n", + "3 王小 20 男 590 武汉" + ], + "text/html": "
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nameagesexscoreaddress
6张菲25女701长沙
5刘蓓18女619广州
4关宇28男601深圳
3王小20男590武汉
\n
" + }, + "metadata": {}, + "execution_count": 48 + } + ], + "source": [ + "# 3、起始和终止索引为负数\n", + "df1[-1:-5:-1] # 最后一行记录索引为-1，不包含索引为-5的数据" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "# 06 常用函数\n", + "# 统计元素个数\n", + "# 很多时候我们需要统计某个列中每个元素出现的个数，相当于是做词频统计，使用：value_counts()方法，具体案例为：\n", + "\n", + "# ⚠️：新数据中df1增加了一列：班级class，后续有作用" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address class\n", + "0 小明 20 男 669 北京 one\n", + "1 小红 18 女 570 深圳 two\n", + "2 小孙 27 男 642 广州 one\n", + "3 王小 20 男 590 武汉 two\n", + "4 关宇 28 男 601 深圳 two\n", + "5 刘蓓 18 女 619 广州 one\n", + "6 张菲 25 女 701 长沙 one" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddressclass
0小明20男669北京one
1小红18女570深圳two
2小孙27男642广州one
3王小20男590武汉two
4关宇28男601深圳two
5刘蓓18女619广州one
6张菲25女701长沙one
\n
" + }, + "metadata": {}, + "execution_count": 50 + } + ], + "source": [ + "df1 = pd.DataFrame({\n", + " \"name\":[\"小明\",\"小红\",\"小孙\",\"王小\",\"关宇\",\"刘蓓\",\"张菲\"],\n", + " \"age\":[20,18,27,20,28,18,25],\n", + " \"sex\":[\"男\",\"女\",\"男\",\"男\",\"男\",\"女\",\"女\"],\n", + " \"score\":[669,570,642,590,601,619,701],\n", + " \"address\":[\"北京\",\"深圳\",\"广州\",\"武汉\",\"深圳\",\"广州\",\"长沙\"],\n", + " \"class\":[\"one\",\"two\",\"one\",\"two\",\"two\",\"one\",\"one\"]\n", + "})\n", + "df1" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "广州 2\n", + "深圳 2\n", + "长沙 1\n", + "北京 1\n", + "武汉 1\n", + "Name: address, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 51 + } + ], + "source": [ + "# 统计中每个城市各出现了多少次\n", + "address = df1[\"address\"].value_counts()\n", + "address\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "metadata": {}, + "execution_count": 52 + } + ], + "source": [ + "type(address) # Series类型" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " index address\n", + "0 广州 2\n", + "1 深圳 2\n", + "2 长沙 1\n", + "3 北京 1\n", + "4 武汉 1" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
indexaddress
0广州2
1深圳2
2长沙1
3北京1
4武汉1
\n
" + }, + "metadata": {}, + "execution_count": 69 + } + ], + "source": [ + "# 结果自动是降序排列的Series类型数据\n", + "# 索引重置\n", + "# 索引重置使用reset_index()\n", + "address_new=address.reset_index()\n", + "address_new" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "metadata": {}, + "execution_count": 70 + } + ], + "source": [ + "type(new_address) # 变成DataFrame类型" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address class\n", + "0 小明 20 男 669 北京 one\n", + "2 小孙 27 男 642 广州 one\n", + "3 王小 20 男 590 武汉 two\n", + "4 关宇 28 男 601 深圳 two" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddressclass
0小明20男669北京one
2小孙27男642广州one
3王小20男590武汉two
4关宇28男601深圳two
\n
" + }, + "metadata": {}, + "execution_count": 71 + } + ], + "source": [ + "# 还比如我们想从数据中单独取出sex=\"男\"的数据：\n", + "fale=df1[df1['sex']=='男']\n", + "fale" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " index name age sex score address class\n", + "0 0 小明 20 男 669 北京 one\n", + "1 2 小孙 27 男 642 广州 one\n", + "2 3 王小 20 男 590 武汉 two\n", + "3 4 关宇 28 男 601 深圳 two" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
indexnameagesexscoreaddressclass
00小明20男669北京one
12小孙27男642广州one
23王小20男590武汉two
34关宇28男601深圳two
\n
" + }, + "metadata": {}, + "execution_count": 72 + } + ], + "source": [ + "fale_1=fale.reset_index()\n", + "fale_1" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address class\n", + "0 小明 20 男 669 北京 one\n", + "1 小孙 27 男 642 广州 one\n", + "2 王小 20 男 590 武汉 two\n", + "3 关宇 28 男 601 深圳 two" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddressclass
0小明20男669北京one
1小孙27男642广州one
2王小20男590武汉two
3关宇28男601深圳two
\n
" + }, + "metadata": {}, + "execution_count": 73 + } + ], + "source": [ + "# 出现的结果中索引是我们想要的结果，但是出现了一列新的数据，就是原来的索引构成的数据，这不是我们想要的数据，需要去除：\n", + "fale_1 = fale.reset_index(drop=True) # 加上参数即可解决\n", + "fale_1" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " index address\n", + "0 广州 2\n", + "1 深圳 2\n", + "2 长沙 1\n", + "3 北京 1\n", + "4 武汉 1" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
indexaddress
0广州2
1深圳2
2长沙1
3北京1
4武汉1
\n
" + }, + "metadata": {}, + "execution_count": 74 + } + ], + "source": [ + "address_new" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " address number\n", + "0 广州 2\n", + "1 深圳 2\n", + "2 长沙 1\n", + "3 北京 1\n", + "4 武汉 1" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
addressnumber
0广州2
1深圳2
2长沙1
3北京1
4武汉1
\n
" + }, + "metadata": {}, + "execution_count": 75 + } + ], + "source": [ + "# 属性重命名\n", + "# 使用的是rename函数，传入columsn参数：\n", + "address_new=address_new.rename(columns={'index':'address','address':'number'})\n", + "address_new" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "# groupby使用\n", + "# groupby主要是实现分组统计的功能：\n", + "# 1、比如我们想统计男女各自的总分" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address class\n", + "0 小明 20 男 669 北京 one\n", + "1 小红 18 女 570 深圳 two\n", + "2 小孙 27 男 642 广州 one\n", + "3 王小 20 男 590 武汉 two\n", + "4 关宇 28 男 601 深圳 two\n", + "5 刘蓓 18 女 619 广州 one\n", + "6 张菲 25 女 701 长沙 one" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddressclass
0小明20男669北京one
1小红18女570深圳two
2小孙27男642广州one
3王小20男590武汉two
4关宇28男601深圳two
5刘蓓18女619广州one
6张菲25女701长沙one
\n
" + }, + "metadata": {}, + "execution_count": 77 + } + ], + "source": [ + "df1 # 统计男女的总分" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "sex\n", + "女 1890\n", + "男 2502\n", + "Name: score, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 78 + } + ], + "source": [ + "# 统计男女的总成绩：sum\n", + "sex_score=df1.groupby('sex')['score'].sum()\n", + "sex_score" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "sex\n", + "女 630.0\n", + "男 625.5\n", + "Name: score, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 79 + } + ], + "source": [ + "# 2、求男女各自的平均分mean\n", + "sex_score=df1.groupby('sex')['score'].mean()\n", + "sex_score" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "sex class\n", + "女 one 1320\n", + " two 570\n", + "男 one 1311\n", + " two 1191\n", + "Name: score, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 80 + } + ], + "source": [ + "# 3、根据男女性别sex、班级class求总分\n", + "sex_class =df1.groupby(['sex','class'])['score'].sum() # 先根据性别、班级求总分\n", + "sex_class" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " sex class score\n", + "0 女 one 1320\n", + "1 女 two 570\n", + "2 男 one 1311\n", + "3 男 two 1191" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
sexclassscore
0女one1320
1女two570
2男one1311
3男two1191
\n
" + }, + "metadata": {}, + "execution_count": 81 + } + ], + "source": [ + "# 索引重置\n", + "sex_class=sex_class.reset_index()\n", + "sex_class" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " sex class score\n", + "0 女 one 1320\n", + "1 女 two 570\n", + "2 男 one 1311\n", + "3 男 two 1191" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
sexclassscore
0女one1320
1女two570
2男one1311
3男two1191
\n
" + }, + "metadata": {}, + "execution_count": 82 + } + ], + "source": [ + "# 一行代码实现上面的功能：\n", + "df1.groupby(['sex','class'])['score'].sum().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address class\n", + "0 小明 20 男 669 北京 one\n", + "1 小红 18 女 570 深圳 two\n", + "2 小孙 27 男 642 广州 one\n", + "3 王小 20 男 590 武汉 two\n", + "4 关宇 28 男 601 深圳 two\n", + "5 刘蓓 18 女 619 广州 one\n", + "6 张菲 25 女 701 长沙 one" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddressclass
0小明20男669北京one
1小红18女570深圳two
2小孙27男642广州one
3王小20男590武汉two
4关宇28男601深圳two
5刘蓓18女619广州one
6张菲25女701长沙one
\n
" + }, + "metadata": {}, + "execution_count": 83 + } + ], + "source": [ + "# apply函数\n", + "# 还是上面的df1数据集：\n", + "df1" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address class\n", + "0 小明 20 1 669 北京 one\n", + "1 小红 18 0 570 深圳 two\n", + "2 小孙 27 1 642 广州 one\n", + "3 王小 20 1 590 武汉 two\n", + "4 关宇 28 1 601 深圳 two\n", + "5 刘蓓 18 0 619 广州 one\n", + "6 张菲 25 0 701 长沙 one" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddressclass
0小明201669北京one
1小红180570深圳two
2小孙271642广州one
3王小201590武汉two
4关宇281601深圳two
5刘蓓180619广州one
6张菲250701长沙one
\n
" + }, + "metadata": {}, + "execution_count": 85 + } + ], + "source": [ + "# 1、需求1：我们想将性别中的男变成1，女变成0\n", + "df2=df1.copy() # 生成一个副本\n", + "df2['sex']=df2['sex'].apply(lambda x: 1 if x ==\"男\" else 0) # 通过匿名函数解决\n", + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address class\n", + "0 小明 20 1 669 北京 one\n", + "1 小红 18 0 570 深圳 two\n", + "2 小孙 27 1 642 广州 one\n", + "3 王小 20 1 590 武汉 two\n", + "4 关宇 28 1 601 深圳 two\n", + "5 刘蓓 18 0 619 广州 one\n", + "6 张菲 25 0 701 长沙 one" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddressclass
0小明201669北京one
1小红180570深圳two
2小孙271642广州one
3王小201590武汉two
4关宇281601深圳two
5刘蓓180619广州one
6张菲250701长沙one
\n
" + }, + "metadata": {}, + "execution_count": 86 + } + ], + "source": [ + "# 我们还可以自定义一个函数来实现：\n", + "def apply_sex(x):\n", + " return 1 if x == \"男\" else 0\n", + "df3 = df1.copy() # 生成一个副本df3\n", + "df3[\"sex\"] = df3[\"sex\"].apply(apply_sex) # 通过自定义函数解决\n", + "df3" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " name age sex score address class\n", + "0 小明 20 男 669 北京市 one\n", + "1 小红 18 女 570 深圳市 two\n", + "2 小孙 27 男 642 广州市 one\n", + "3 王小 20 男 590 武汉市 two\n", + "4 关宇 28 男 601 深圳市 two\n", + "5 刘蓓 18 女 619 广州市 one\n", + "6 张菲 25 女 701 长沙市 one" + ], + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
nameagesexscoreaddressclass
0小明20男669北京市one
1小红18女570深圳市two
2小孙27男642广州市one
3王小20男590武汉市two
4关宇28男601深圳市two
5刘蓓18女619广州市one
6张菲25女701长沙市one
\n
" + }, + "metadata": {}, + "execution_count": 88 + } + ], + "source": [ + "# 2、还比如我们想给每个城市的后面加上一个“市”，变成北京市、深圳市等：\n", + "df4=df1.copy()\n", + "df4['address']=df4['address'].apply(lambda x: x+'市')\n", + "df4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "name": "python389jvsc74a57bd03b1827d4b6462cc460901af0bc0d075c933010817877a813d51f78a107cbf6e5", + "display_name": "Python 3.8.9 64-bit" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git "a/pandas/\345\255\246\347\224\237\344\277\241\346\201\257.xlsx" "b/pandas/\345\255\246\347\224\237\344\277\241\346\201\257.xlsx" new file mode 100644 index 0000000..c39669c Binary files /dev/null and "b/pandas/\345\255\246\347\224\237\344\277\241\346\201\257.xlsx" differ diff --git a/picture_watermark/image_watermark.py b/picture_watermark/image_watermark.py new file mode 100644 index 0000000..13f49b0 --- /dev/null +++ b/picture_watermark/image_watermark.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from PIL import Image + +# 打开等待加水印的图片 +image = Image.open('lifeistoshort.jpg') +# 打开水印图片 +watermark = Image.open('qrcode.jpg') +# 如果觉得水印图片太大，可以缩放，这里缩放比例为50% +factor = 0.5 +# 缩放图片 +watermark = watermark.resize(tuple(map(lambda x: int(x * factor), watermark.size))) +# 生成一个新的layer +layer = Image.new('RGBA', image.size) +# 把水印打到新的layer上去，后面参数是水印位置，此处是右下角 +layer.paste(watermark, (image.size[0] - watermark.size[0], image.size[1] - watermark.size[1])) +# 添加水印 +marked_img = Image.composite(layer, image, layer) +# 保存图片 +marked_img.save('maked_lifeistoshort_1.jpg') +# 打开生成的图片（缓存图片） +marked_img.show() \ No newline at end of file diff --git a/picture_watermark/image_watermark_2.py b/picture_watermark/image_watermark_2.py new file mode 100644 index 0000000..dadf96d --- /dev/null +++ b/picture_watermark/image_watermark_2.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from PIL import Image +import os + + +def watermark(mark, path, savepath): + ''' + 给图片加文字水印 + mark 水印文字 String + path 图片名称 String + savepath 保存路径 String + ''' + # 打开等待加水印的图片 + image = Image.open(path) + # 打开水印图片 + watermark = Image.open(mark) + # 如果觉得水印图片太大，可以缩放，这里缩放比例为50% + factor = 0.5 + # 缩放图片 + watermark = watermark.resize(tuple(map(lambda x: int(x * factor), watermark.size))) + # 生成一个新的layer + layer = Image.new('RGBA', image.size) + # 把水印打到新的layer上去，后面参数是水印位置，此处是右下角 + layer.paste(watermark, (image.size[0] - watermark.size[0], image.size[1] - watermark.size[1])) + # 更多位置参考上面的代码 + # 添加水印 + marked_img = Image.composite(layer, image, layer) + # 保存图片 + marked_img.save(os.path.join(savepath, os.path.basename(path))) + # 打开生成的图片（缓存图片） + marked_img.show() + + +def all_path(dirname, filters): + ''' + 获取路径下所有文件名称（完整路径） + dirname 路径名 String + filters 过滤文件类型 Array + ''' + result = [] # 所有的文件 + + for maindir, subdir, file_name_list in os.walk(dirname): + # maindir 当前主目录 + # subdir 当前主目录下的所有目录 + # file_name_list 当前主目录下的所有文件 + for filename in file_name_list: + apath = os.path.join(maindir, filename) # 合并成一个完整路径 + ext = os.path.splitext(apath)[1] # 获取文件后缀 [0]获取的是除了文件名以外的内容 + if ext in filters: + result.append(apath) + return result + + +if __name__ == '__main__': + + filters = [".jpg", ".jpeg", ".png"] # 设置过滤后的文件类型 + paths = all_path(r"C:\Users\liming\Downloads\mytest", filters) + mark = 'qrcode.jpg' + for path in paths: + watermark(mark, path, "D:\\watermark") + print('Done!') diff --git a/picture_watermark/lifeistoshort.jpg b/picture_watermark/lifeistoshort.jpg new file mode 100644 index 0000000..71f6be9 Binary files /dev/null and b/picture_watermark/lifeistoshort.jpg differ diff --git a/picture_watermark/qrcode.jpg b/picture_watermark/qrcode.jpg new file mode 100644 index 0000000..419aa66 Binary files /dev/null and b/picture_watermark/qrcode.jpg differ diff --git a/picture_watermark/text_watermark.py b/picture_watermark/text_watermark.py new file mode 100644 index 0000000..f2200bb --- /dev/null +++ b/picture_watermark/text_watermark.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from PIL import Image, ImageDraw, ImageFont + +# 打开要加水印的图片 +image = Image.open('lifeistoshort.jpg') +# 提示要打水印的文字 +text = input('输入你的水印文字:\n') +# 获得一个字体，你也可以自己下载相应字体，第二个值是字体大小 +font = ImageFont.truetype('C:\Windows\Fonts\simhei.ttf', 64) +# 将图片转换为RGBA图片 +layer = image.convert('RGBA') +# 依照目标图片大小生成一张新的图片 参数[模式,尺寸,颜色(默认为0)] +text_overlay = Image.new('RGBA', layer.size) +# 画图 +image_draw = ImageDraw.Draw(text_overlay) +# 获得字体大小,textsize(text, font=None) +text_size_x, text_size_y = image_draw.textsize(text, font=font) +# 设置文本位置 此处是右下角显示 +# text_xy = (layer.size[0] - text_size_x, layer.size[1] - text_size_y) + +# 设置文本位置 此处是下方居中显示 +# text_xy = (layer.size[0]//2-text_size_x//2, layer.size[1]-text_size_y) + +# 设置文本位置 此处是左下角下角显示 +# text_xy = (0, layer.size[1]-text_size_y) + +# 设置文本位置 此处是右上角显示 +# text_xy=(layer.size[0]-text_size_x,0) + +# 设置文本位置 此处是左上角角显示 +# text_xy=(0,0) + +# 设置文本位置 此处是居中显示 +text_xy = (layer.size[0]//2-text_size_x//2, layer.size[1]//2-text_size_y//2) + + +# 设置文字，位置,字体,颜色和透明度 +image_draw.text(text_xy, text, font=font, fill=(0, 0, 0, 85)) +# 将水印打到原图片上生成新的图片 +marked_img = Image.alpha_composite(layer, text_overlay) +# 保存图片 +marked_img.save('new_after.png') +# 显示图片（这里是生成一个临时文件，必须关闭图片 这段py代码才算结束） +marked_img.show() + diff --git a/picture_watermark/text_watermark_2.py b/picture_watermark/text_watermark_2.py new file mode 100644 index 0000000..64998e2 --- /dev/null +++ b/picture_watermark/text_watermark_2.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from PIL import Image, ImageDraw, ImageFont +import os + + +def watermark(text, filename, savepath): + ''' + 给图片加文字水印 + text 水印文字 String + filename 图片名称 String + savepath 保存路径 String + ''' + # 检查保存文件夹是否存在，如果不存在则生成一个 + if not os.path.exists(savepath): + os.makedirs(savepath) + # 打开要加水印的图片 + image = Image.open(filename) + # 获得一个字体，你也可以自己下载相应字体，第二个值是字体大小 + font = ImageFont.truetype(r'C:\Windows\Fonts\simhei.ttf', 64) + # 将图片转换为RGBA + layer = image.convert('RGBA') + # 依照目标图片大小生成一张新的图片 参数[模式,尺寸,颜色(默认为0)] + text_overlay = Image.new('RGBA', layer.size) + # 画图 + image_draw = ImageDraw.Draw(text_overlay) + # 获得字体大小,textsize(text, font=None) + text_size_x, text_size_y = image_draw.textsize(text, font=font) + # 设置文本位置 此处是右下角显示 + text_xy = (layer.size[0] - text_size_x, layer.size[1] - text_size_y) + # 设置文字，位置,字体,颜色和透明度 + image_draw.text(text_xy, text, font=font, fill=(0, 0, 0, 85)) + # 将水印打到原图片上生成新的图片 + marked_img = Image.alpha_composite(layer, text_overlay) + # 保存图片 + marked_img.save(os.path.join(savepath, os.path.basename(filename).split('.')[0] + ".png")) + + +def all_path(dirname, filters): + ''' + 获取路径下所有文件名称（完整路径） + dirname 路径名 String + filters 过滤文件类型 Array + ''' + result = [] # 所有的文件 + for maindir, subdir, file_name_list in os.walk(dirname): + # maindir 当前主目录 + # subdir 当前主目录下的所有目录 + # file_name_list 当前主目录下的所有文件 + for filename in file_name_list: + apath = os.path.join(maindir, filename) # 合并成一个完整路径 + ext = os.path.splitext(apath)[1] # 获取文件后缀 [0]获取的是除了文件名以外的内容 + if ext in filters: + result.append(apath) + return result + + +if __name__ == '__main__': + + filters = [".jpg", ".jpeg", ".png"] # 设置过滤后的文件类型 + paths = all_path(r"C:\Users\liming\Downloads\mytest", filters) + text = input('输入你的水印文字:\n') + for path in paths: + watermark(text, path, r"D:\watermark") + print('Done!') diff --git a/raw/parse.py b/raw/parse.py index 8a0b466..d6588ca 100644 --- a/raw/parse.py +++ b/raw/parse.py @@ -11,18 +11,18 @@ def solve_china_city(): with open('china_city.txt', 'r', encoding="utf-8") as f: for l in f: l = l.strip() - unicode.endswith + # unicode.endswith if l.endswith(tuple(['市', '区', '县'])): - print l[:-1] + print(l[:-1]) else: - print l + print(l) def solve_school(): for k, v in SCHOOL_UNIVERSITY.iteritems(): - print v + print(v) print(len(SCHOOL_UNIVERSITY)) -#solve_school() +# solve_school() solve_china_city() diff --git a/rsync/rsync.sh b/rsync/rsync.sh new file mode 100644 index 0000000..0bbd80d --- /dev/null +++ b/rsync/rsync.sh @@ -0,0 +1,87 @@ +#!/bin/bash +#this script for start|stop rsync daemon service + +# status1=$(ps -ef | egrep "rsync --daemon.*rsyncd.conf" | grep -v 'grep') +# 指定rsyncd.conf文件的路径 +RSYNC_CONF="/etc/rsyncd.conf" +RSYNC_PID_FILE="/var/run/rsyncd.pid" # rsync服务器进程ID文件路径 +START_RSYNC="rsync --daemon --config=$RSYNC_CONF" + +start() { + status1=$(ps -ef | grep -E "rsync --daemon.*rsyncd.conf" | grep -v 'grep') + if [ "${status1}X" == "X" ]; then + rm -f $RSYNC_PID_FILE + # 启动rsync服务器 + ${START_RSYNC} + # rsync --daemon --config=$RSYNC_CONF + status2=$(ps -ef | grep -E "rsync --daemon.*rsyncd.conf" | grep -v 'grep') + if [ "${status2}X" != "X" ]; then + echo "rsync service start.......OK" + fi + else + echo "rsync service is running !" + fi + +} + +stop() { + # 停止rsync服务器 + status1=$(ps -ef | grep -E "rsync --daemon.*rsyncd.conf" | grep -v 'grep') + if [ "${status1}X" != "X" ]; then + kill -9 "$(cat $RSYNC_PID_FILE)" #读取并结束 进程 pid号 + status2=$(ps -ef | grep -E "rsync --daemon.*rsyncd.conf" | grep -v 'grep') + if [ "${status2}X" == "X" ]; then + echo "rsync service stop.......OK" + fi + else + echo "rsync service is not running !" + fi +} + +restart() { + # 重启rsync服务器 + stop + start +} + +status() { + # 检查rsync服务器的状态 + status1=$(ps -ef | grep -E "rsync --daemon.*rsyncd.conf" | grep -v 'grep') + if [ "${status1}X" != "X" ]; then + echo "rsync service is running !" + else + echo "rsync service is not running !" + fi +} + +add_boot_up() { + # 加入开机自启动 + if ! grep -E "rsync --daemon" /etc/rc.local >/dev/null; then + echo "rsync --daemon --config=$RSYNC_CONF" >>/etc/rc.local #加入开机自启动 + fi +} + +main() { + add_boot_up + case "$1" in + start) + start + ;; + stop) + stop + ;; + restart) + restart + ;; + status) + status + ;; + *) + echo "Usage: $0 {start|stop|restart|status}" + exit 1 + ;; + esac + +} + +main "$@" diff --git a/rsync/rsync_file_from_server.sh b/rsync/rsync_file_from_server.sh new file mode 100644 index 0000000..4a9893a --- /dev/null +++ b/rsync/rsync_file_from_server.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +# 定义rsync服务器的IP地址和源文件夹 +RSYNC_SERVER="rsync://192.168.98.129" +SOURCE_FOLDER="/mirrors/" + +# 定义要同步的目标文件夹 +DEST_FOLDER="/opt/mirrors/" + +# 定义日志文件的路径 +LOG_FILE="/tmp/logfile.log" + +# 同步操作 +rsync -avzp --delete "$RSYNC_SERVER:$DEST_FOLDER" "$SOURCE_FOLDER">> $LOG_FILE + +# 检查rsync命令的返回状态 +if [ $? -eq 0 ]; then + echo "同步成功！" +else + echo "同步失败，请检查日志文件 $LOG_FILE 以获取更多详细信息。" +fi diff --git a/rsync/rsync_file_to_server.sh b/rsync/rsync_file_to_server.sh new file mode 100644 index 0000000..00f51c0 --- /dev/null +++ b/rsync/rsync_file_to_server.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +# 定义rsync服务器的IP地址和目标文件夹 +RSYNC_SERVER="rsync://192.168.98.129" +DEST_FOLDER="/mirrors/" + +# 定义要同步的源文件夹 +SOURCE_FOLDER="/opt/mirrors/" + +# 定义日志文件的路径 +LOG_FILE="/tmp/logfile.log" + +# 同步操作 +rsync -avzp --delete "$SOURCE_FOLDER" "$RSYNC_SERVER:$DEST_FOLDER" >> $LOG_FILE + +# 检查rsync命令的返回状态 +if [ $? -eq 0 ]; then + echo "同步成功！" +else + echo "同步失败，请检查日志文件 $LOG_FILE 以获取更多详细信息。" +fi diff --git a/rsync/rsyncd.conf b/rsync/rsyncd.conf new file mode 100644 index 0000000..5ab6702 --- /dev/null +++ b/rsync/rsyncd.conf @@ -0,0 +1,19 @@ +uid = root +gid = root +use chroot = yes +log file = /var/log/rsyncd.log +pid file = /var/run/rsyncd.pid +lock file = /var/run/rsync.lock +port = 873 +# 模块定义 +[mirrors] +comment = "backup" +path = /opt/mirrors #模块对应的位置（路径） +ignore errors #忽略错误程序 +read only = false #是否只读（这里是假，表示能写入） +list = false #是否可以列表* +# hosts allow = 172.16.2.0/24 #准许访问rsync服务器客户范围（白名单） +#hosts deny = 0.0.0.0/32 #禁止访问rsync服务器客户范围（黑名单） +#auth users = rsync_backup #不存在的用户，只用于认证 +#设置进行连接认证的密钥文件： +#secrets file = /etc/rsync.password #认证时密钥文件 diff --git a/shell/ trapezoid.sh b/shell/ trapezoid.sh new file mode 100644 index 0000000..ac08ab0 --- /dev/null +++ b/shell/ trapezoid.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +echo -n "please input a number:" +read -r a +for ((i = 1; i <= "$a"; i++)); do + printf "%-${i}s\n" "+" | sed 's/ /+/g' +done diff --git a/shell/Isosceles_trapezoid.sh b/shell/Isosceles_trapezoid.sh new file mode 100644 index 0000000..4f9f94e --- /dev/null +++ b/shell/Isosceles_trapezoid.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +echo "please a number" +read -r num + +sum=$((num * 2 + 1)) + +for ((a = 1; a <= num; a++)); do + + for ((b = 1; b <= $(((sum - 2 * a - 1) / 2)); b++)); do + echo -n " " + done + + for ((b = 1; b <= $((2 * a - 1)); b++)); do + echo -n "+" + done + + for ((b = 1; b <= $(((sum - 2 * a - 1) / 2)); b++)); do + echo -n " " + done + + echo -e "\n" +done diff --git a/shell/check_lv.sh b/shell/check_lv.sh new file mode 100644 index 0000000..4db588f --- /dev/null +++ b/shell/check_lv.sh @@ -0,0 +1,63 @@ +#!/bin/bash +[ $UID -ne 0 ] && echo "please use root run" && exit 22 +[ -f /etc/init.d/functions ] && . /etc/init.d/functions +#定义 vip 和real server ip +vip=10.0.0.90 +vip_netmask=10.0.0.90/24 +RealServer=( + 10.0.0.56 + 10.0.0.57 +) + +status_del() { + for Rs_ip in ${RealServer[*]}; do + error=0 #定义检测失败次数 + #进行两次检测 + for ((i = 1; i <= 2; i++)); do + x=$(nmap -p 80 "$Rs_ip" | grep 'open' | wc -l) + if [ "$x" -eq 0 ]; then + let error+=1 + fi + sleep 2 + done + #检查real主机ip 是否在 ipvsadm列表中 + vs_ip=$(ipvsadm -ln | grep $Rs_ip | wc -l) #两次检测失败，而且real ip存在ipvsadm列表中，执行向ipvsadm中添加命令 + if [ $error -eq 2 -a "$vs_ip" -ne 0 ]; then + ipvsadm -d -t "$vip":80 -r "$Rs_ip":80 + echo "Real server $Rs_ip is down,delete it" #两次检测失败，而且不存在ipvsadm列表中，输出 down + elif [ $error -eq 2 -a "$vs_ip" -eq 0 ]; then + echo "Real server $Rs_ip is down" #检测失败次数为0或者1，列表中存在，输出 running + elif [ $error -ne 2 -a "$vs_ip" -ne 0 ]; then + echo "Real server $Rs_ip is running" + fi + done +} + +status_add() { + for Rs_ip in ${RealServer[*]}; do + bingo=0 + for ((i = 1; i <= 2; i++)); do + x=$(nmap -p 80 "$Rs_ip" | grep 'open' | wc -l) + if [ "$x" -eq 1 ]; then + let bingo+=1 + fi + sleep 2 + done + vs_ip=$(ipvsadm -ln | grep $Rs_ip | wc -l) #两次检测成功，real ip不在ipvsadm中，执行向ipvsadm 添加ip 命令 + if [ $bingo -eq 2 -a "$vs_ip" -eq 0 ]; then + ipvsadm -a -t "$vip":80 -r "$Rs_ip":80 + echo "Real server $Rs_ip is recover,add it" + fi + done +} + +main() { + while true; do + status_del + sleep 10 + status_add + sleep 10 + done +} + +main diff --git a/shell/check_nginx_proxy_health.sh b/shell/check_nginx_proxy_health.sh new file mode 100644 index 0000000..8f8653d --- /dev/null +++ b/shell/check_nginx_proxy_health.sh @@ -0,0 +1,42 @@ +#!/bin/bash +#Author: Stanley Wang +#mail: +#Version: 1.0 +#Description: This is a script for nginx proxy health check. +# +###def vars########## +RS=( + 172.16.1.191 + 172.16.1.192 +) +PORT=80 +html_file="/var/html/www/index.html" +declare -a RSTATUS +###main############## +function checkrs() { + local I=0 + for ((I = 0; I < ${#RS[*]}; I++)); do + RSTATUS[$I]=$(nmap "${RS[$I]}" -p $PORT | grep "open" | wc -l) + done +} +function output() { + if [ "${RSTATUS[0]}" -eq 0 ]; then + #echo "${RS[$i]} is down!" + sed -i '22 s/.*/Down!<\/font><\/td>/g' $html_file + elif [ "${RSTATUS[0]}" -eq 1 ]; then + #echo "${RS[$i]} is OK!" + sed -i '22 s/.*/OK!<\/font><\/td>/g' $html_file + fi + if [ "${RSTATUS[1]}" -eq 0 ]; then + #echo "${RS[$i]} is down!" + sed -i '28 s/.*/Down!<\/font><\/td>/g' $html_file + elif [ "${RSTATUS[1]}" -eq 1 ]; then + #echo "${RS[$i]} is OK!" + sed -i '28 s/.*/OK!<\/font><\/td>/g' $html_file + fi +} +while true; do + checkrs + output + sleep 2 +done diff --git a/shell/check_nginx_status,sh b/shell/check_nginx_status,sh new file mode 100644 index 0000000..c134388 --- /dev/null +++ b/shell/check_nginx_status,sh @@ -0,0 +1,24 @@ +#!/bin/bash + +html_file=/var/www/html/status.html +array=( + 192.168.0.104 +) + +check() { + array[0]=$(netstat -anptl | grep "httpd" | wc -l) +} + +output() { + if [ ${array[0]} -eq 0 ]; then + echo 'OK!' >$html_file + elif [ ${array[0]} -eq 1 ]; then + echo 'OK!' >$html_file + fi +} + +while true; do + check + output + sleep 20 +done diff --git a/shell/check_url.sh b/shell/check_url.sh new file mode 100644 index 0000000..8c8e350 --- /dev/null +++ b/shell/check_url.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +[ -f /etc/init.d/functions ] && source /etc/init.d/functions + +array=( + http://www.163.com + http://www.taobao.com + http://oldboy.blog.51cto.com + http://10.0.0.7 +) + +wait() { + echo -n "wait" + for ((a = 1; a <= 3; a++)); do + echo -n "." + sleep 1 + done +} + +check_url() { + wget -T 5 -t 2 --spider "$1" &>/dev/null + RETVAL=$? + if [ $RETVAL -eq 0 ]; then + action "check $1" /bin/true + else + action "check $1" /bin/false + fi + return $RETVAL +} + +main() { + for ((i = 0; i < ${#array[*]}; i++)); do + wait + check_url "${array[i]}" + done +} + +main diff --git a/shell/decrypt.sh b/shell/decrypt.sh new file mode 100644 index 0000000..dac1d1b --- /dev/null +++ b/shell/decrypt.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +array=(21029299 f2b61ccf a3da1677 1f6d12dd 8721469a) +count=0 + +for i in $(seq 0 32767); do + variable=$(echo $i | md5sum | cut -c 1-8) + if [[ ${array[*]} =~ $variable ]]; then + let count++ + echo "$count: $i ==>> $variable" + fi + [ $count -eq 5 ] && exit +done diff --git a/shell/inotify_log.sh b/shell/inotify_log.sh new file mode 100644 index 0000000..d32ae09 --- /dev/null +++ b/shell/inotify_log.sh @@ -0,0 +1,12 @@ +#!/bin/bash +inotifywait -mrq --timefmt '%y/%m/%d %H:%M' --format '%T %w%f %e' --event delete,modify,create,attrib /data/web | while read date time file event; do + case $event in + MODIFY | CREATE | MOVE | MODIFY,ISDIR | CREATE,ISDIR | MODIFY,ISDIR) + echo $event'-'$file'-'$date'-'$time >>/var/log/web_watch.log + ;; + + MOVED_FROM | MOVED_FROM,ISDIR | DELETE | DELETE,ISDIR) + echo $event'-'$file'-'$date'-'$time /var/log/web_watch.log + ;; + esac +done diff --git a/shell/inotify_rsync_file.sh b/shell/inotify_rsync_file.sh new file mode 100644 index 0000000..28838a0 --- /dev/null +++ b/shell/inotify_rsync_file.sh @@ -0,0 +1,10 @@ +#!/bin/sh + +SRC=/var/www/channel/ +DST=/var/www/webroot/channel/ +INWT=/usr/local/bin/inotifywait +RSYNC=/usr/bin/rsync +$INWT -mrq -e create,move,delete,modify $SRC | while read dir event filename; do + echo "inotify $dir $event $filename" + rsync -aHqzt $SRC $DST +done diff --git a/shell/ip_vs.sh b/shell/ip_vs.sh new file mode 100644 index 0000000..f9ac99a --- /dev/null +++ b/shell/ip_vs.sh @@ -0,0 +1,70 @@ +#!/bin/bash + +[ $UID -ne 0 ] && echo "please use root run" && exit 22 +[ -f /etc/init.d/functions ] && . /etc/init.d/functions +vip=10.0.0.90 +vip_netmask=10.0.0.90/24 +RealServer=( + 10.0.0.56 + 10.0.0.57 +) + +start() { + ip addr show | grep "$vip" &>/dev/null + #判断网卡上 vip 是否存在，不存在添加 + if [ "$?" -ne 0 ]; then + ip addr add "$vip_netmask" dev ens32 + action "ip address add vip $vip_netmask" /bin/true + else + echo "ip address vip $vip_netmask already exists" + fi + #判断ipvsadm中 vip是否存在，不存在则添加 + lvs_table=$(ipvsadm -ln | grep "$vip" | wc -l) + + if [ "$lvs_table" -eq 1 ]; then + echo "ipvsadm vip already exist" + else + ipvsadm -A -t "$vip":80 -s rr + action "ipvsadm add vip $vip" /bin/true + fi + + #判断ipvsadm中 real server是否存在，不存在则添加 + for ip in ${RealServer[*]}; do + rs_num=$(ipvsadm -ln | grep "$ip" | wc -l) + if [ "$rs_num" -eq 1 ]; then + echo "real server $ip already exists" + else + ipvsadm -a -t "$vip":80 -r "$ip" -g + action "ipvsadm add real server $ip" /bin/true + fi + done +} + +stop() { + ip addr show | grep $vip &>/dev/null + if [ "$?" -ne 0 ]; then + echo "ip address vip $vip is not exist" + else + ip addr del $vip_netmask dev ens32 + action "ip address delete vip $vip" /bin/true + fi + + ipvsadm -C && action "clear all lvs table." /bin/true +} + +case "$1" in +start) + start + ;; +stop) + stop + ;; +restart) + stop + sleep 2 + start + ;; +*) + echo "please input {start|stop|restart}" + ;; +esac diff --git a/shell/lazy_find.sh b/shell/lazy_find.sh new file mode 100644 index 0000000..b62670c --- /dev/null +++ b/shell/lazy_find.sh @@ -0,0 +1,57 @@ +#!/bin/sh +# lazy find + +# GNU All-Permissive License +# Copying and distribution of this file, with or without modification, +# are permitted in any medium without royalty provided the copyright +# notice and this notice are preserved. This file is offered as-is, +# without any warranty. + +## help function + +function helpu { + echo " " + echo "Fuzzy search for filename." + echo "$0 [--match-case|--path] filename" + echo " " + exit +} + +## set variables + +MATCH="-iname" +SEARCH="." + +## parse options + +while [ True ]; do + if [ "$1" = "--help" -o "$1" = "-h" ]; then + helpu + elif [ "$1" = "--match-case" -o "$1" = "-m" ]; then + MATCH="-name" + shift 1 + elif [ "$1" = "--path" -o "$1" = "-p" ]; then + SEARCH="${2}" + shift 2 + else + break + fi +done + +## sanitize input filenames +## create array, retain spaces + +ARG=("${@}") +set -e + +## catch obvious input error + +if [ "X$ARG" = "X" ]; then + helpu +fi + +## perform search + +for query in ${ARG[*]}; do + /usr/bin/find "${SEARCH}" "${MATCH}" "*${ARG}*" +done diff --git a/shell/lottery.sh b/shell/lottery.sh new file mode 100644 index 0000000..ab9f9b2 --- /dev/null +++ b/shell/lottery.sh @@ -0,0 +1,29 @@ +#!/bin/bash + +###### +# 需要一个抓阄的程序： + +# 要求： + +# 1、执行脚本后，想去的同学输入英文名字全拼，产生随机数01-99之间的数字，数字越大就去参加项目实践，前面已经抓到的数字，下次不能在出现相同数字。 + +# 2、第一个输入名字后，屏幕输出信息，并将名字和数字记录到文件里，程序不能退出继续等待别的学生输入。 +##### +namelist=/tmp/name.txt +#判断数字是否重复 +judgeNum() { + if grep -w "$1" $namelist; then + a=1 + else + a=0 + fi +} + +while read -rp "please input your name:" name; do + a=1 + while [ $a -eq 1 ]; do #数字不重复就跳出第二个while ，重复就再生成一个 + num=$((RANDOM % 100 + 1)) + judgeNum $num + done + echo "$name:$num" | tee -a $namelist #结果显示到屏幕 并 追加到文件中 +done diff --git a/shell/lvs_client.sh b/shell/lvs_client.sh new file mode 100644 index 0000000..4d73b9c --- /dev/null +++ b/shell/lvs_client.sh @@ -0,0 +1,54 @@ +#!/bin/bash +[ -f /etc/init.d/functions ] && . /etc/init.d/functions +vip=10.0.0.90 +vip_netmask=10.0.0.90/32 +RealServer=10.0.0.56 + +start() { # lo网卡上没有vip 则添加 + ip addr show | grep "$vip" &>/dev/null + if [ "$?" -ne 0 ]; then + ip addr add "$vip_netmask" dev lo + fi + + echo "1" >/proc/sys/net/ipv4/conf/all/arp_ignore + echo "2" >/proc/sys/net/ipv4/conf/all/arp_announce#检测lo是否添加上vip + ip addr show | grep "$vip" &>/dev/null + if [ "$?" -eq 0 ]; then + action "ip address add vip $vip_netmask ,lvs_client start" /bin/true + else + action "can't add vip $vip_netmask " /bin/false + fi +} + +stop() { #lo网卡上有 vip 则删掉 + ip addr show | grep "$vip" &>/dev/null + if [ "$?" -eq 0 ]; then + ip addr del "$vip_netmask" dev lo + fi + + echo "0" >/proc/sys/net/ipv4/conf/all/arp_ignore + echo "0" >/proc/sys/net/ipv4/conf/all/arp_announce#检测删除结果 + ip addr show | grep "$vip" &>/dev/null + if [ "$?" -ne 0 ]; then + action "ip address del vip $vip_netmask ,lvs_client stop" /bin/true + else + action "can't del vip $vip_netmask " /bin/false + fi +} + +case $1 in +start) + start + ;; +stop) + stop + ;; +restart) + stop + sleep 1 + start + ;; +*) + echo "USAGE: $0 {start|stop|restart|status}" + ;; +esac diff --git a/shell/square.sh b/shell/square.sh new file mode 100644 index 0000000..bccd848 --- /dev/null +++ b/shell/square.sh @@ -0,0 +1,7 @@ +#!/bin/bash +echo -n "please input a number:" +read -r a +b=$((a * 2)) +for ((i = 1; i <= "${a}"; i++)); do + printf "%-${b}s\n" "+" | sed 's/ /+/g' +done diff --git a/shell/square2.sh b/shell/square2.sh new file mode 100644 index 0000000..01b1538 --- /dev/null +++ b/shell/square2.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +echo "input a number" +read -r num + +num2=$(( num * 2 )) +for ((a = 1; a <= "$num"; a++)); do + echo -e "\n" + for ((b = 1; b <= "$num2"; b++)); do + echo -n + + done +done diff --git a/shell/trapezoid2.sh b/shell/trapezoid2.sh new file mode 100644 index 0000000..cd0203c --- /dev/null +++ b/shell/trapezoid2.sh @@ -0,0 +1,12 @@ +#!/bin/bash + +echo "input a number" +read -r num + +#let num2=num*2 +for ((a = 1; a <= "$num"; a++)); do + for ((b = 1; b <= a; b++)); do + echo -n "+" + done + echo -e "\n" +done diff --git a/shell/word_sort.sh b/shell/word_sort.sh new file mode 100644 index 0000000..ccba762 --- /dev/null +++ b/shell/word_sort.sh @@ -0,0 +1,42 @@ +#!/bin/bash +a="the squid project provides a number of resources toassist users design,implement and support squid installations. Please browsethe documentation and support sections for more infomation" + +word=/tmp/word.txt + +word_sort() { + for i in $a; do + echo "$i" >>$word + done + cat $word | sort | uniq -c | sort -rn +} + +letter_sort() { + echo "$a" | + while read -r line; do + for i in $(seq 1 ${#line}); do + echo "$line" | cut -c "$i" >>$word + done + done + cat $word | sort | uniq -c | sort -rn +} +main() { + + [ -f $word ] && rm -f $word + echo "word sort input 1" + echo "letter sort input 2" + read -r num + + case $num in + "1") + word_sort + ;; + "2") + letter_sort + ;; + *) + echo "please input 1 or 2" + ;; + esac +} + +main diff --git a/singleton/singleton.py b/singleton/singleton.py new file mode 100644 index 0000000..f7d926e --- /dev/null +++ b/singleton/singleton.py @@ -0,0 +1,50 @@ + +# 方法一：使用装饰器实现单例模式。 + +from calendar import c +from functools import wraps +from tkinter.messagebox import NO + + +def singleton(cls): + """单例类装饰器""" + instances = {} + + @wraps(cls) + def wrapper(*args, **kwargs): + if cls not in instances: + instances[cls] = cls(*args, **kwargs) + return instances[cls] + return wrapper + + +@singleton +class President: + pass + +# test + + +p1 = President() +p2 = President() + +print(p1 is p2) +# output: True + + +# 方法二：使用元类实现单例模式。 + +class SingletonMeta(type): + """自定义单例元类""" + def __init__(cls, *args, **kwargs): + cls.__instance = None + super().__init__(*args, **kwargs) + + def __call__(cls, *args, **kwargs): + if cls.__instance is None: + cls.__instance = super().__call__(*args, **kwargs) + return cls.__instance + + +class President(metaclass=SingletonMeta): + pass diff --git a/snippets/Date/add_days.py b/snippets/Date/add_days.py new file mode 100644 index 0000000..73d60d5 --- /dev/null +++ b/snippets/Date/add_days.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +功能实现：计算给定日期后n天的日期。 + +解读： +使用datetime.timedelta和+运算符来计算新datetime.datetime在d中添加n天后的值。 +忽略第二个参数d，使用datetime.today()的默认值 +""" +from datetime import datetime, timedelta, date + + +def add_days(n, d = datetime.now()): + return d + timedelta(n) + + +# Examples + +print(add_days(5, date(2020, 10, 25))) +print(add_days(-5, date(2020, 10, 25))) +# output: +# 2020-10-30 +# 2020-10-20 diff --git a/snippets/Date/daterange.py b/snippets/Date/daterange.py new file mode 100644 index 0000000..9526d77 --- /dev/null +++ b/snippets/Date/daterange.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：创建开始(包括)和结束(不包括)之间的日期列表。 + +解读： +使用datetime.timedelta。从开始到结束的天数。 +使用int()将结果转换为整数，使用range()迭代每一天。 +使用列表推导式和datetime.timedelta()创建一个datetime.date对象列表。 +""" +from datetime import timedelta, date + + +def daterange(start, end): + return [start + timedelta(n) for n in range(int((end - start).days))] + + +# Examples + +print(daterange(date(2020, 10, 1), date(2020, 10, 5))) +# output: +# [datetime.date(2020, 10, 1), datetime.date(2020, 10, 2), datetime.date(2020, 10, 3), datetime.date(2020, 10, 4)] diff --git a/snippets/Date/days_ago.py b/snippets/Date/days_ago.py new file mode 100644 index 0000000..eb9ef32 --- /dev/null +++ b/snippets/Date/days_ago.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：计算从今天算起n天前的日期。 + +解读： +使用datetime.date.today()获取当前日期。 +使用datetime.timedelta从今天的日期减去n天。 +""" +from datetime import timedelta, date + + +def days_ago(n): + return date.today() - timedelta(n) + + +# Examples + +print(days_ago(5)) +# output: +# 2021-03-28 diff --git a/snippets/Date/days_diff.py b/snippets/Date/days_diff.py new file mode 100644 index 0000000..5a4a095 --- /dev/null +++ b/snippets/Date/days_diff.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：计算两个日期之间的日差。 + +解读： +从end减去start，然后使用datetime.timedelta.days得到日差。 +""" +from datetime import date + + +def days_diff(start, end): + return (end - start).days + + +# Examples + +print(days_diff(date(2020, 10, 25), date(2020, 10, 28))) +# output: +# 3 \ No newline at end of file diff --git a/snippets/Date/days_from_now.py b/snippets/Date/days_from_now.py new file mode 100644 index 0000000..e930b9d --- /dev/null +++ b/snippets/Date/days_from_now.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：计算从今天算起n天的日期。 + +解读： +使用datetime.date.today()获取当前日期。 +使用datetime.timedelta从今天的日期添加n天。 +""" +from datetime import timedelta, date + + +def days_from_now(n): + return date.today() + timedelta(n) + + +# Examples + +print(days_from_now(5)) +# output: +# 2021-04-07 diff --git a/snippets/Date/from_iso_date.py b/snippets/Date/from_iso_date.py new file mode 100644 index 0000000..21777c4 --- /dev/null +++ b/snippets/Date/from_iso_date.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：从其ISO-8601表示形式转换日期。 + +解读： +使用datetime.datetime.fromisoformat()将给定的ISO-8601日期转换为datetime.datetime对象。 +""" +from datetime import datetime + + +def from_iso_date(d): + return datetime.fromisoformat(d) + + +# Examples + +print(from_iso_date('2020-10-28T12:30:59.000000')) +# output: +# 2020-10-28 12:30:59 diff --git a/snippets/Date/is_weekday.py b/snippets/Date/is_weekday.py new file mode 100644 index 0000000..a581b1d --- /dev/null +++ b/snippets/Date/is_weekday.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查给定日期是否为工作日。 + +解读： +使用datetime.datetime.weekday()以整数形式获取星期几。 +检查一周中的一天是否小于或等于4。 +忽略第二个参数d，使用datetime.today()的默认值。 +""" +from datetime import datetime + + +def is_weekday(d = datetime.now()): + return d.weekday() <= 4 + + +# Examples + +from datetime import date + +print(is_weekday(date(2020, 10, 25))) +print(is_weekday(date(2020, 10, 28))) +# output: +# False +# True diff --git a/snippets/Date/is_weekend.py b/snippets/Date/is_weekend.py new file mode 100644 index 0000000..84de59c --- /dev/null +++ b/snippets/Date/is_weekend.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查给定日期是否为周末。 + +解读： +使用datetime.datetime.weekday()以整数形式获取星期几。 +检查一周中的一天是否大于4。 +忽略第二个参数d，使用datetime.today()的默认值。 +""" +from datetime import datetime + + +def is_weekend(d = datetime.now()): + return d.weekday() > 4 + + +# Examples + +from datetime import date + +print(is_weekend(date(2020, 10, 25))) +print(is_weekend(date(2020, 10, 28))) +# output: +# True +# False \ No newline at end of file diff --git a/snippets/Date/months_diff.py b/snippets/Date/months_diff.py new file mode 100644 index 0000000..da3e9f6 --- /dev/null +++ b/snippets/Date/months_diff.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：计算两个日期之间的月差。 + +解读： +从end减去start，然后使用datetime.timedelta。得到日差。 +除以30，并使用math.ceil()得到以月为单位的差值(取整)。 +""" +from math import ceil + + +def months_diff(start, end): + return ceil((end - start).days / 30) + + +# Examples + +from datetime import date + +print(months_diff(date(2020, 10, 28), date(2020, 11, 25))) +# output: +# 1 diff --git a/snippets/Date/to_iso_date.py b/snippets/Date/to_iso_date.py new file mode 100644 index 0000000..0650a33 --- /dev/null +++ b/snippets/Date/to_iso_date.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将日期转换为其ISO-8601表示形式。 + +解读： +使用datetime.datetime.isoformat()转换给定的datetime.datetime对象设置为ISO-8601日期。 +""" +from datetime import datetime + + +def to_iso_date(d): + return d.isoformat() + + +# Examples + +print(to_iso_date(datetime(2020, 10, 25))) +# output: +# 2020-10-25T00:00:00 diff --git a/snippets/Dictionary/Combine_values.py b/snippets/Dictionary/Combine_values.py new file mode 100644 index 0000000..c380193 --- /dev/null +++ b/snippets/Dictionary/Combine_values.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：组合两个或多个字典，为每个键创建一个值列表。 + +解读： +创建一个新的collections.defaultdict，将list作为每个键的默认值，并循环遍历dict。 +使用dict.append()将字典的值映射到键。 +使用dict()将collections.defaultdict转换为普通字典。 +""" +from collections import defaultdict + + +def combine_values(*dicts): + res = defaultdict(list) + for d in dicts: + for key in d: + res[key].append(d[key]) + return dict(res) + + +# Examples + +d1 = {'a': 1, 'b': 'foo', 'c': 400} +d2 = {'a': 3, 'b': 200, 'd': 400} + +print(combine_values(d1, d2)) +# output: +# {'a': [1, 3], 'b': ['foo', 200], 'c': [400], 'd': [400]} diff --git a/snippets/Dictionary/collect_dictionary.py b/snippets/Dictionary/collect_dictionary.py new file mode 100644 index 0000000..b657149 --- /dev/null +++ b/snippets/Dictionary/collect_dictionary.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：反转具有非唯一的可哈希值的字典。 + +解读： +创建一个collections.defaultdict，其中list作为每个键的默认值。 +使用dictionary.items()与循环结合使用dict.append()将字典的值映射到键。 +使用dict()将collections.defaultdict转换为普通字典。 +""" +from collections import defaultdict + + +def collect_dictionary(obj): + inv_obj = defaultdict(list) + for key, value in obj.items(): + inv_obj[value].append(key) + return dict(inv_obj) + + +# Examples + +ages = { + 'Peter': 10, + 'Isabel': 10, + 'Anna': 9, +} +print(collect_dictionary(ages)) +# output: +# { 10: ['Peter', 'Isabel'], 9: ['Anna'] } diff --git a/snippets/Dictionary/dict_to_list.py b/snippets/Dictionary/dict_to_list.py new file mode 100644 index 0000000..a405f36 --- /dev/null +++ b/snippets/Dictionary/dict_to_list.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将字典转换为元组列表。 + +解读： +使用dict.items()和list()从给定的字典中获取一个元组列表。 +""" + + +def dict_to_list(d): + return list(d.items()) + + +# Examples + +d = {'one': 1, 'three': 3, 'five': 5, 'two': 2, 'four': 4} +print(dict_to_list(d)) +# output: +# [('one', 1), ('three', 3), ('five', 5), ('two', 2), ('four', 4)] diff --git a/snippets/Dictionary/find_key.py b/snippets/Dictionary/find_key.py new file mode 100644 index 0000000..c91be80 --- /dev/null +++ b/snippets/Dictionary/find_key.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：在提供的字典中查找具有给定值的第一个键。 + +解读： +使用dictionary.items()和next()返回值为val的第一个键。 +""" + + +def find_key(dict, val): + return next(key for key, value in dict.items() if value == val) + + +# Examples + +ages = { + 'Peter': 10, + 'Isabel': 11, + 'Anna': 9, +} +print(find_key(ages, 11)) +# output: +# Isabel diff --git a/snippets/Dictionary/find_keys.py b/snippets/Dictionary/find_keys.py new file mode 100644 index 0000000..962a5f3 --- /dev/null +++ b/snippets/Dictionary/find_keys.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：在提供的字典中查找具有给定值的所有键。 + +解读： +使用dictionary.items()，生成器和list()返回所有值等于val的键。 +""" + + +def find_keys(dict, val): + return [key for key, value in dict.items() if value == val] + + +# Examples + +ages = { + 'Peter': 10, + 'Isabel': 11, + 'Anna': 10, +} +print(find_keys(ages, 10)) +# output: +# [ 'Peter', 'Anna' ] diff --git a/snippets/Dictionary/get.py b/snippets/Dictionary/get.py new file mode 100644 index 0000000..37f7f85 --- /dev/null +++ b/snippets/Dictionary/get.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：从字典或列表中检索给定选择器列表指示的嵌套键的值。 + +解读： +使用functools.reduce()遍历选择器列表。 +对选择器中的每个键应用operator.getitem()，检索将用于下一次迭代的迭代对象的值。 +""" + +from functools import reduce +from operator import getitem + + +def get(d, selectors): + return reduce(getitem, selectors, d) + + +# Examples + +users = { + 'freddy': { + 'name': { + 'first': 'fred', + 'last': 'smith' + }, + 'postIds': [1, 2, 3] + } +} +print(get(users, ['freddy', 'name', 'last'])) +print(get(users, ['freddy', 'postIds', 1])) +# output: +# smith +# 2 diff --git a/snippets/Dictionary/group_by.py b/snippets/Dictionary/group_by.py new file mode 100644 index 0000000..312bbef --- /dev/null +++ b/snippets/Dictionary/group_by.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：根据给定的函数对列表中的元素进行分组。 + +解读： +使用collections.defaultdict初始化字典。 +结合使用fn与for循环和dict.append()来填充字典。 +使用dict()将其转换为普通字典。 +""" +from collections import defaultdict +from math import floor + + +def group_by(lst, fn): + d = defaultdict(list) + for el in lst: + d[fn(el)].append(el) + return dict(d) + + +# Examples + +print(group_by([6.1, 4.2, 6.3], floor)) +print(group_by(['one', 'two', 'three'], len)) +# output: +# {4: [4.2], 6: [6.1, 6.3]} +# {3: ['one', 'two'], 5: ['three']} diff --git a/snippets/Dictionary/invert_dictionary.py b/snippets/Dictionary/invert_dictionary.py new file mode 100644 index 0000000..d30a2ab --- /dev/null +++ b/snippets/Dictionary/invert_dictionary.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：反转具有唯一可哈希值的字典。 + +解读： +结合列表理解使用dictionary.items()创建一个值和键反向的新字典。 +""" + + +def invert_dictionary(obj): + return {value: key for key, value in obj.items()} + + +# Examples + +ages = { + 'Peter': 10, + 'Isabel': 11, + 'Anna': 9, +} +print(invert_dictionary(ages)) +# output: +# { 10: 'Peter', 11: 'Isabel', 9: 'Anna' } diff --git a/snippets/Dictionary/key_in_dict.py b/snippets/Dictionary/key_in_dict.py new file mode 100644 index 0000000..0ba20c9 --- /dev/null +++ b/snippets/Dictionary/key_in_dict.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查给定的键是否存在于字典中。 + +解读： +使用in操作符检查d是否包含key。 +""" + + +def key_in_dict(d, key): + return (key in d) + +# +# Examples + +d = {'one': 1, 'three': 3, 'five': 5, 'two': 2, 'four': 4} +print(key_in_dict(d, 'three')) +# output: +# True diff --git a/snippets/Dictionary/key_of_max.py b/snippets/Dictionary/key_of_max.py new file mode 100644 index 0000000..03eed54 --- /dev/null +++ b/snippets/Dictionary/key_of_max.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：查找字典中最大值的键。 + +解读: +使用max()并将key参数设置为dict.get()来查找并返回给定字典中最大值的键。 +""" + + +def key_of_max(d): + return max(d, key=d.get) + + +# Examples + +print(key_of_max({'a': 4, 'b': 0, 'c': 13})) +# output: +# c diff --git a/snippets/Dictionary/key_of_min.py b/snippets/Dictionary/key_of_min.py new file mode 100644 index 0000000..16505bc --- /dev/null +++ b/snippets/Dictionary/key_of_min.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：在字典中查找最小值的键。 + +解读： +使用min()并将key参数设置为dict.get()来查找并返回给定字典中最小值的键。 +""" + + +def key_of_min(d): + return min(d, key=d.get) + + +# Examples + +print(key_of_min({'a': 4, 'b': 0, 'c': 13})) +# output: +# b diff --git a/snippets/Dictionary/keys_only.py b/snippets/Dictionary/keys_only.py new file mode 100644 index 0000000..80473c1 --- /dev/null +++ b/snippets/Dictionary/keys_only.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：创建平面字典中所有键的平面列表。 + +解读： +使用dict.keys()返回给定字典中的键值。 +返回前一个结果的list()。 +""" + + +def keys_only(flat_dict): + return list(flat_dict.keys()) + + +# Examples + +ages = { + 'Peter': 10, + 'Isabel': 11, + 'Anna': 9, +} +print(keys_only(ages)) +# output: +# ['Peter', 'Isabel', 'Anna'] diff --git a/snippets/Dictionary/map_dictionary.py b/snippets/Dictionary/map_dictionary.py new file mode 100644 index 0000000..31f6abe --- /dev/null +++ b/snippets/Dictionary/map_dictionary.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用函数将列表的值映射到字典，其中键-值对由原始值作为键，函数的结果作为值组成。 + +解读： +使用map()将fn应用到列表的每个值。 +使用zip()将原始值与fn生成的值配对。 +使用dict()返回一个合适的字典。 +""" + + +def map_dictionary(itr, fn): + return dict(zip(itr, map(fn, itr))) + + +# Examples + +print(map_dictionary([1, 2, 3], lambda x: x * x)) +# output: +# { 1: 1, 2: 4, 3: 9 } diff --git a/snippets/Dictionary/map_values.py b/snippets/Dictionary/map_values.py new file mode 100644 index 0000000..6632902 --- /dev/null +++ b/snippets/Dictionary/map_values.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：创建具有与提供的字典相同的键的字典，并为每个值运行通过提供的函数生成的值。 + +解读： +使用dict.items()遍历字典，将fn生成的值赋给新字典的每个键。 +""" + + +def map_values(obj, fn): + return {k: fn(v) for k, v in obj.items()} + + +# Examples + +users = { + 'fred': {'user': 'fred', 'age': 40}, + 'pebbles': {'user': 'pebbles', 'age': 1} +} +print(map_values(users, lambda u: u['age'])) +# output: +# {'fred': 40, 'pebbles': 1} diff --git a/snippets/Dictionary/merge_dictionaries.py b/snippets/Dictionary/merge_dictionaries.py new file mode 100644 index 0000000..8e69911 --- /dev/null +++ b/snippets/Dictionary/merge_dictionaries.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：合并两个或多个字典。 + +解读： +创建一个新的字典并循环遍历字典，使用dictionary.update()将每个字典的键值对添加到结果中。 +""" + + +def merge_dictionaries(*dicts): + res = {} + for d in dicts: + res |= d + return res + + +# Examples + +ages_one = { + 'Peter': 10, + 'Isabel': 11, +} +ages_two = { + 'Anna': 9 +} +print(merge_dictionaries(ages_one, ages_two)) +# output: +# { 'Peter': 10, 'Isabel': 11, 'Anna': 9 } diff --git a/snippets/Dictionary/pluck.py b/snippets/Dictionary/pluck.py new file mode 100644 index 0000000..c6c6d41 --- /dev/null +++ b/snippets/Dictionary/pluck.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将字典列表转换为与指定键对应的值列表。 + +解读： +使用列表推导式和dict.get()来获取lst中每个字典的key值。 +""" + + +def pluck(lst, key): + return [x.get(key) for x in lst] + + +# Examples + +simpsons = [ + {'name': 'lisa', 'age': 8}, + {'name': 'homer', 'age': 36}, + {'name': 'marge', 'age': 34}, + {'name': 'bart', 'age': 10} +] +print(pluck(simpsons, 'age')) +# output: +# [8, 36, 34, 10] diff --git a/snippets/Dictionary/sort_dict_by_key.py b/snippets/Dictionary/sort_dict_by_key.py new file mode 100644 index 0000000..3d6efb8 --- /dev/null +++ b/snippets/Dictionary/sort_dict_by_key.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：按键对给定字典进行排序。 + +解读： +使用dict.items()从d获得一个元组对列表，并使用sorted()对其进行排序。 +使用dict()将排序后的列表转换回字典。 +使用sorted()中的reverse参数根据第二个参数对字典进行反向排序。 +""" + + +def sort_dict_by_key(d, reverse=False): + return dict(sorted(d.items(), reverse=reverse)) + + +# Examples + +d = {'one': 1, 'three': 3, 'five': 5, 'two': 2, 'four': 4} +print(sort_dict_by_key(d)) +print(sort_dict_by_key(d, True)) +# output: +# {'five': 5, 'four': 4, 'one': 1, 'three': 3, 'two': 2} +# {'two': 2, 'three': 3, 'one': 1, 'four': 4, 'five': 5} diff --git a/snippets/Dictionary/sort_dict_by_value.py b/snippets/Dictionary/sort_dict_by_value.py new file mode 100644 index 0000000..54ae17d --- /dev/null +++ b/snippets/Dictionary/sort_dict_by_value.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：按值对给定字典进行排序。 + +解读： +使用dict.items()从d获得一个元组对列表，并使用lambda函数和sorted()对其进行排序。 +使用dict()将排序后的列表转换回字典。 +使用sorted()中的reverse参数根据第二个参数对字典进行反向排序。 +⚠️注意:字典值必须是相同的类型。 +""" + + +def sort_dict_by_value(d, reverse=False): + return dict(sorted(d.items(), key=lambda x: x[1], reverse=reverse)) + + +# Examples + +d = {'one': 1, 'three': 3, 'five': 5, 'two': 2, 'four': 4} +print(sort_dict_by_value(d)) +print(sort_dict_by_value(d, True)) +# output: +# {'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5} +# {'five': 5, 'four': 4, 'three': 3, 'two': 2, 'one': 1} diff --git a/snippets/Dictionary/to_dictionary.py b/snippets/Dictionary/to_dictionary.py new file mode 100644 index 0000000..dd22874 --- /dev/null +++ b/snippets/Dictionary/to_dictionary.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将两个列表组合到一个字典中，其中第一个列表的元素作为键，第二个列表的元素作为值。第一个列表的值必须是唯一的，并且是可哈希的。 + +解读： +将zip()与dict()结合使用，可以将两个列表的值组合到一个字典中。 +""" + + +def to_dictionary(keys, values): + return dict(zip(keys, values)) + + +# Examples + +print(to_dictionary(['a', 'b'], [1, 2])) +# output: +# { a: 1, b: 2 } diff --git a/snippets/Dictionary/values_only.py b/snippets/Dictionary/values_only.py new file mode 100644 index 0000000..703eee8 --- /dev/null +++ b/snippets/Dictionary/values_only.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回平面字典中所有值的平面列表。 + +解读： +使用dict.values()返回给定字典中的值。 +返回前一个结果的list()。 +""" + + +def values_only(flat_dict): + return list(flat_dict.values()) + + +# Examples + +ages = { + 'Peter': 10, + 'Isabel': 11, + 'Anna': 9, +} +print(values_only(ages)) +# output: +# [10, 11, 9] diff --git a/snippets/Function/check_prop.py b/snippets/Function/check_prop.py new file mode 100644 index 0000000..0be10a6 --- /dev/null +++ b/snippets/Function/check_prop.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：创建一个函数，该函数将为给定对象上的指定属性调用谓词函数。 + +解读： +返回一个lambda函数，该函数接受一个对象并将谓词函数fn应用到指定的属性。 +""" + + +def check_prop(fn, prop): + return lambda obj: fn(obj[prop]) + + +# Examples + +check_age = check_prop(lambda x: x >= 18, 'age') +user = {'name': 'Mark', 'age': 18} +print(check_age(user)) +# output: +# True diff --git a/snippets/Function/compose.py b/snippets/Function/compose.py new file mode 100644 index 0000000..e74af14 --- /dev/null +++ b/snippets/Function/compose.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：执行从右到左的函数组合。 + +解读： +使用functools.reduce()执行从右到左的函数组合。 +最后一个(最右边的)函数可以接受一个或多个参数;其余的函数必须是一元的。 +""" +from functools import reduce + + +def compose(*fns): + return reduce(lambda f, g: lambda *args: f(g(*args)), fns) + + +# Examples + +add5 = lambda x: x + 5 +multiply = lambda x, y: x * y +multiply_and_add_5 = compose(add5, multiply) +print(multiply_and_add_5(5, 2)) +# output: +# 15 diff --git a/snippets/Function/compose_right.py b/snippets/Function/compose_right.py new file mode 100644 index 0000000..0a0a4a6 --- /dev/null +++ b/snippets/Function/compose_right.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：执行从左到右的函数组合。 + +解读： +使用functools.reduce()执行从左到右的函数组合。 +第一个(最左边)函数可以接受一个或多个参数;其余的函数必须是一元的。 +""" +from functools import reduce + + +def compose_right(*fns): + return reduce(lambda f, g: lambda *args: g(f(*args)), fns) + + +# Examples + +add = lambda x, y: x + y +square = lambda x: x * x +add_and_square = compose_right(add, square) +print(add_and_square(1, 2)) +# output: +# 9 diff --git a/snippets/Function/curry.py b/snippets/Function/curry.py new file mode 100644 index 0000000..8ba114b --- /dev/null +++ b/snippets/Function/curry.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：咖喱一个函数。 + +解读： +使用functools.partial()返回一个新的partial对象，它的行为与fn类似，只是部分应用了给定的参数args。 +""" +from functools import partial + + +def curry(fn, *args): + return partial(fn, *args) + + +# Examples + +add = lambda x, y: x + y +add10 = curry(add, 10) +print(add10(20)) +# output: +# 30 diff --git a/snippets/Function/delay.py b/snippets/Function/delay.py new file mode 100644 index 0000000..caaa507 --- /dev/null +++ b/snippets/Function/delay.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：以毫秒为单位调用所提供的函数。 + +解读： +使用time.sleep()将fn的执行延迟ms / 1000秒。 +""" +from time import sleep + + +def delay(fn, ms, *args): + sleep(ms / 1000) + return fn(*args) + + +# Examples + +delay(lambda x: print(x), 1000, 'later') +# output: +# prints 'later' after one second diff --git a/snippets/Function/difference_by.py b/snippets/Function/difference_by.py new file mode 100644 index 0000000..c26ffb9 --- /dev/null +++ b/snippets/Function/difference_by.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将提供的函数应用于两个列表的每个元素后，返回两个列表之间的差异。 + +解读： +创建一个集合，使用map()将fn应用到b中的每个元素。 +在a上与fn结合使用一个列表推导式，只保留前面创建的集合_b中不包含的值。 +""" +from math import floor + + +def difference_by(a, b, fn): + _b = set(map(fn, b)) + return [item for item in a if fn(item) not in _b] + + +# Examples + +print(difference_by([2.1, 1.2], [2.3, 3.4], floor)) +print(difference_by([{'x': 2}, {'x': 1}], [{'x': 1}], lambda v: v['x'])) +# output: +# [1.2] +# [{'x': 2}] diff --git a/snippets/Function/intersection_by.py b/snippets/Function/intersection_by.py new file mode 100644 index 0000000..a6441ae --- /dev/null +++ b/snippets/Function/intersection_by.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将提供的函数应用于两个列表的每个列表元素后，返回两个列表中存在的元素列表。 + +解读： +创建一个集合，使用map()将fn应用到b中的每个元素。 +在a上结合使用一个列表推导式和fn来只保留包含在两个列表中的值。 +""" +from math import floor + + +def intersection_by(a, b, fn): + _b = set(map(fn, b)) + return [item for item in a if fn(item) in _b] + + +# Examples + +print(intersection_by([2.1, 1.2], [2.3, 3.4], floor)) +# output: +# [2.1] diff --git a/snippets/Function/unfold.py b/snippets/Function/unfold.py new file mode 100644 index 0000000..7cc4f6d --- /dev/null +++ b/snippets/Function/unfold.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用迭代器函数和初始种子值构建列表。 + +解读： +迭代器函数接受一个参数(seed)，并且必须总是返回一个包含两个元素的列表([value, nextSeed])，或者返回False以终止。 +使用生成器函数fn_generator，它使用while循环调用迭代器函数并生成该值，直到它返回False。 +使用列表推导式，使用迭代器函数返回生成器生成的列表。 +""" + + +def unfold(fn, seed): + def fn_generator(val): + while True: + val = fn(val[1]) + if val is False: break + yield val[0] + + return list(fn_generator([None, seed])) + + +# Examples + +f = lambda n: False if n > 50 else [-n, n + 10] +print(unfold(f, 10)) +# output: +# [-10, -20, -30, -40, -50] diff --git a/snippets/Function/when.py b/snippets/Function/when.py new file mode 100644 index 0000000..ebf196f --- /dev/null +++ b/snippets/Function/when.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：针对测试函数测试一个值x，有条件地应用一个函数。 + +解读： +检查predicate(x)的值是否为True，如果为True则返回when_true(x)，否则返回x。 +""" + + +def when(predicate, when_true): + return lambda x: when_true(x) if predicate(x) else x + + +# Examples + +double_even_numbers = when(lambda x: x % 2 == 0, lambda x: x * 2) +print(double_even_numbers(2)) +print(double_even_numbers(1)) +# output: +# 4 +# 1 diff --git a/snippets/List/all_equal.py b/snippets/List/all_equal.py new file mode 100644 index 0000000..505b2a8 --- /dev/null +++ b/snippets/List/all_equal.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查列表中的所有元素是否相等。 + +解读： +使用set()消除重复的元素，然后使用len()检查length是否为1。 +""" + + +def all_equal(lst): + return len(set(lst)) == 1 + + +# Examples + +print(all_equal([1, 2, 3, 4, 5, 6])) +print(all_equal([1, 1, 1, 1])) +# output: +# False +# True diff --git a/snippets/List/all_unique.py b/snippets/List/all_unique.py new file mode 100644 index 0000000..ac9f73c --- /dev/null +++ b/snippets/List/all_unique.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查列表中的所有值是否都是唯一的。 + +解读： +在给定的列表中使用set()来保持唯一的出现。 +使用len()将唯一值的长度与原始列表进行比较。 +""" + + +def all_unique(lst): + return len(lst) == len(set(lst)) + + +# Examples + +x = [1, 2, 3, 4, 5, 6] +y = [1, 2, 2, 3, 4, 5] +print(all_unique(x)) +print(all_unique(y)) +# output: +# True +# False diff --git a/snippets/List/average.py b/snippets/List/average.py new file mode 100644 index 0000000..3eda564 --- /dev/null +++ b/snippets/List/average.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：计算两个或多个数字的平均值。 + +解读： +使用sum()将提供的所有参数相加，除以len()。 +""" + + +def average(*args): + return sum(args, 0.0) / len(args) + + +# Examples + +print(average(*[1, 2, 3])) +print(average(1, 2, 3)) +# output: +# 2.0 +# 2.0 diff --git a/snippets/List/average_by.py b/snippets/List/average_by.py new file mode 100644 index 0000000..c9681ae --- /dev/null +++ b/snippets/List/average_by.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用提供的函数将每个元素映射到一个值后，计算列表的平均值。 + +解读： +使用map()将每个元素映射到fn返回的值。 +使用sum()将所有映射值相加，除以len(lst)。 +省略最后一个实参fn，使用默认的identity函数。 +""" + + +def average_by(lst, fn=lambda x: x): + return sum(map(fn, lst), 0.0) / len(lst) + + +# Examples + +print(average_by([{'n': 4}, {'n': 2}, {'n': 8}, {'n': 6}], lambda x: x['n'])) +# output: +# 5.0 diff --git a/snippets/List/bifurcate.py b/snippets/List/bifurcate.py new file mode 100644 index 0000000..0c6b00e --- /dev/null +++ b/snippets/List/bifurcate.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：根据给定筛选器列表的结果将值分成两组。 + +解读： +根据过滤器，使用列表推导和zip()将元素添加到组中。 +如果filter对任何元素都有真值，则将其添加到第一个组，否则将其添加到第二个组。 +""" + + +def bifurcate(lst, filter): + return [ + [x for x, flag in zip(lst, filter) if flag], + [x for x, flag in zip(lst, filter) if not flag] + ] + + +# Examples + +print(bifurcate(['beep', 'boop', 'foo', 'bar'], [True, True, False, True])) +# output: +# [ ['beep', 'boop', 'bar'], ['foo'] ] diff --git a/snippets/List/bifurcate_by.py b/snippets/List/bifurcate_by.py new file mode 100644 index 0000000..8edc0a9 --- /dev/null +++ b/snippets/List/bifurcate_by.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：根据给定的过滤函数的结果将值分成两组。 + +解读： +根据fn为每个元素返回的值，使用列表推导式将元素添加到组中。 +如果fn为任何元素返回真值，则将其添加到第一组，否则将其添加到第二组。 +""" + + +def bifurcate_by(lst, fn): + return [ + [x for x in lst if fn(x)], + [x for x in lst if not fn(x)] + ] + + +# Examples + +print(bifurcate_by(['beep', 'boop', 'foo', 'bar'], lambda x: x[0] == 'b')) +# output: +# [ ['beep', 'boop', 'bar'], ['foo'] ] diff --git a/snippets/List/cast_list.py b/snippets/List/cast_list.py new file mode 100644 index 0000000..2620af7 --- /dev/null +++ b/snippets/List/cast_list.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：如果提供的值不是列表，则将其转换为列表。 + +解读： +使用isinstance()检查给定的值是否为可枚举的。 +使用list()返回或相应地封装在一个列表中。 +""" + + +def cast_list(val): + return list(val) if isinstance(val, (tuple, list, set, dict)) else [val] + + +# Examples + +print(cast_list('foo')) +print(cast_list([1])) +print(cast_list(('foo', 'bar'))) +# output: +# ['foo'] +# [1] +# ['foo', 'bar'] diff --git a/snippets/List/chunk.py b/snippets/List/chunk.py new file mode 100644 index 0000000..c4244a8 --- /dev/null +++ b/snippets/List/chunk.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将列表分割成指定大小的较小列表。 + +解读： +使用list()和range()创建所需大小的列表。 +在列表上使用map()，并使用给定列表的拼接来填充它。 +最后，返回创建的列表。 +""" +from math import ceil + + +def chunk(lst, size): + return list(map(lambda x: lst[x * size:x * size + size], list(range(ceil(len(lst) / size))))) + + +# Examples + +print(chunk([1, 2, 3, 4, 5], 2)) +# output: +# [[1, 2], [3, 4], [5]] diff --git a/snippets/List/chunk_into_n.py b/snippets/List/chunk_into_n.py new file mode 100644 index 0000000..5432ae6 --- /dev/null +++ b/snippets/List/chunk_into_n.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将一个列表分成n个较小的列表。 + +解读： +使用math.ceil()和len()获取每个块的大小。 +使用list()和range()创建一个大小为n的新列表。 +使用map()将新列表的每个元素映射到一个大小为length的块 +如果不能均匀地分割原始列表，则最后一个块将包含剩余的元素 +""" +from math import ceil + + +def chunk_into_n(lst, n): + size = ceil(len(lst) / n) + return list(map(lambda x: lst[x * size:x * size + size], list(range(n)))) + + +# Examples + +print(chunk_into_n([1, 2, 3, 4, 5, 6, 7], 4)) +# output: +# [[1, 2], [3, 4], [5, 6], [7]] diff --git a/snippets/List/compact.py b/snippets/List/compact.py new file mode 100644 index 0000000..e241295 --- /dev/null +++ b/snippets/List/compact.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：从列表中删除假值。 + +解读： +使用filter()过滤掉假值(False、None、0和"")。 +""" + + +def compact(lst): + return list(filter(None, lst)) + + +# Examples + +print(compact([0, 1, False, 2, '', 3, 'a', 's', 34])) +# output: +# [ 1, 2, 3, 'a', 's', 34 ] diff --git a/snippets/List/count_by.py b/snippets/List/count_by.py new file mode 100644 index 0000000..8c786d9 --- /dev/null +++ b/snippets/List/count_by.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：根据给定的函数对列表中的元素进行分组，并返回每个组中的元素计数。 + +解读： +使用collections.defaultdict初始化字典。 +使用map()去给定函数映射给定列表的值。 + +""" +from collections import defaultdict +from math import floor + + +def count_by(lst, fn=lambda x: x): + count = defaultdict(int) + for val in map(fn, lst): + count[val] += 1 + return dict(count) + + +# Examples + +print(count_by([6.1, 4.2, 6.3], floor)) +print(count_by(['one', 'two', 'three'], len)) +# output: +# {6: 2, 4: 1} +# {3: 2, 5: 1} diff --git a/snippets/List/count_occurrences.py b/snippets/List/count_occurrences.py new file mode 100644 index 0000000..8eeef15 --- /dev/null +++ b/snippets/List/count_occurrences.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：计算列表中出现的值。 + +解读： +使用list.count()来计算val在lst中出现的次数。 +""" + + +def count_occurrences(lst, val): + return lst.count(val) + + +# Examples + +print(count_occurrences([1, 1, 2, 1, 2, 3], 1)) +# output: +# 3 diff --git a/snippets/List/cumsum.py b/snippets/List/cumsum.py new file mode 100644 index 0000000..a4a6247 --- /dev/null +++ b/snippets/List/cumsum.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：创建部分和的列表。 + +解读： +使用itertools.accumulate()创建每个元素的累积和。 +使用list()将结果转换为列表。 +""" +from itertools import accumulate + + +def cumsum(lst): + return list(accumulate(lst)) + + +# Examples + +print(cumsum(range(0, 15, 3))) +# output: +# [0, 3, 9, 18, 30] diff --git a/snippets/List/deep_flatten.py b/snippets/List/deep_flatten.py new file mode 100644 index 0000000..6c4f3b4 --- /dev/null +++ b/snippets/List/deep_flatten.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：深层拼合列表 + +解读： +使用递归。 +对collections.abc.Iterable使用isinstance()检查元素是否可迭代 +如果是可迭代的，则递归应用deep_flatten()，否则返回[lst]。 +""" +from collections.abc import Iterable + + +def deep_flatten(lst): + return ([a for i in lst for a in deep_flatten(i)] if isinstance(lst, Iterable) else [lst]) + + +# Examples + +print(deep_flatten([1, [2], [[3], 4], 5])) +# output: +# [1, 2, 3, 4, 5] diff --git a/snippets/List/dict_to_list.py b/snippets/List/dict_to_list.py new file mode 100644 index 0000000..a405f36 --- /dev/null +++ b/snippets/List/dict_to_list.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将字典转换为元组列表。 + +解读： +使用dict.items()和list()从给定的字典中获取一个元组列表。 +""" + + +def dict_to_list(d): + return list(d.items()) + + +# Examples + +d = {'one': 1, 'three': 3, 'five': 5, 'two': 2, 'four': 4} +print(dict_to_list(d)) +# output: +# [('one', 1), ('three', 3), ('five', 5), ('two', 2), ('four', 4)] diff --git a/snippets/List/difference.py b/snippets/List/difference.py new file mode 100644 index 0000000..fe97286 --- /dev/null +++ b/snippets/List/difference.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：计算两个可迭代对象之间的差值，不过滤重复值。 + +解读： +从b创建一个集合。 +对a使用列表推导式，只保留前面创建的集合_b中不包含的值。 +""" + + +def difference(a, b): + _b = set(b) + return [item for item in a if item not in _b] + + +# Examples + +print(difference([1, 2, 3], [1, 2, 4])) +# output: +# [3] diff --git a/snippets/List/difference_by.py b/snippets/List/difference_by.py new file mode 100644 index 0000000..4ef1752 --- /dev/null +++ b/snippets/List/difference_by.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将提供的函数应用于两个列表的每个元素后，返回两个列表之间的差异。 + +解读： +创建一个集合，使用map()将fn应用到b中的每个元素。 +在a上与fn结合使用一个列表表达式，只保留前面创建的集合_b中不包含的值。 +""" +from math import floor + + +def difference_by(a, b, fn): + _b = set(map(fn, b)) + return [item for item in a if fn(item) not in _b] + + +# Examples + +print(difference_by([2.1, 1.2], [2.3, 3.4], floor)) +print(difference_by([{'x': 2}, {'x': 1}], [{'x': 1}], lambda v: v['x'])) +# output: +# [1.2] +# [{'x': 2}] diff --git a/snippets/List/digitize.py b/snippets/List/digitize.py new file mode 100644 index 0000000..f105e6a --- /dev/null +++ b/snippets/List/digitize.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将数字转换为数字列表。 + +解读： +对n的字符串表示形式使用map()和int组合，并从结果返回一个列表。 +""" + + +def digitize(n): + return list(map(int, str(n))) + + +# Examples + +print(digitize(123)) +# output: +# [1, 2, 3] diff --git a/snippets/List/drop.py b/snippets/List/drop.py new file mode 100644 index 0000000..12e9f43 --- /dev/null +++ b/snippets/List/drop.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回一个从左边删除n个元素的列表。 + +解读： +使用切片表示法从左边删除指定数量的元素。 +忽略最后一个参数n，使用默认值1。 +""" + + +def drop(a, n=1): + return a[n:] + + +# Examples + +print(drop([1, 2, 3])) +print(drop([1, 2, 3], 2)) +print(drop([1, 2, 3], 42)) +# output: +# [2, 3] +# [3] +# [] diff --git a/snippets/List/drop_right.py b/snippets/List/drop_right.py new file mode 100644 index 0000000..1b251c2 --- /dev/null +++ b/snippets/List/drop_right.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回一个从右侧删除n个元素的列表 + +解读： +使用切片表示法从右侧删除指定数量的元素。 +忽略最后一个参数n，使用默认值1。 +""" + + +def drop_right(a, n=1): + return a[:-n] + + +# Examples + +print(drop_right([1, 2, 3])) +print(drop_right([1, 2, 3], 2)) +print(drop_right([1, 2, 3], 42)) +# output: +# [1, 2] +# [1] +# [] diff --git a/snippets/List/every.py b/snippets/List/every.py new file mode 100644 index 0000000..fe8744c --- /dev/null +++ b/snippets/List/every.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查所提供的函数是否对列表中的每个元素都返回True。 + +解读： +将all()与map()和fn结合使用，检查fn是否为列表中的所有元素返回True。 +""" + + +def every(lst, fn=lambda x: x): + return all(map(fn, lst)) + + +# Examples + +print(every([4, 2, 3], lambda x: x > 1)) +print(every([1, 2, 3])) +# output: +# True +# True diff --git a/snippets/List/every_nth.py b/snippets/List/every_nth.py new file mode 100644 index 0000000..a5bb492 --- /dev/null +++ b/snippets/List/every_nth.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回列表中的每个第n个元素 + +解读： +使用切片表示法创建一个新列表，该列表包含给定列表的第n个元素。 +""" + + +def every_nth(lst, nth): + return lst[nth - 1::nth] + + +# Examples + +print(every_nth([1, 2, 3, 4, 5, 6], 2)) +# output: +# [ 2, 4, 6 ] diff --git a/snippets/List/fibonacci.py b/snippets/List/fibonacci.py new file mode 100644 index 0000000..a8e813d --- /dev/null +++ b/snippets/List/fibonacci.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：生成一个列表，包含斐波那契序列，直到第n项。 + +解读： +从0和1开始，使用list.append()将列表最后两个数字的总和加到列表的末尾，直到列表的长度达到n。 +如果n小于或等于0，返回一个包含0的列表。 +""" + + +def fibonacci(n): + if n <= 0: + return [0] + sequence = [0, 1] + while len(sequence) <= n: + next_value = sequence[-1] + sequence[len(sequence) - 2] + sequence.append(next_value) + return sequence + + +# Examples + +print(fibonacci(7)) +# output: +# [0, 1, 1, 2, 3, 5, 8, 13] diff --git a/snippets/List/filter_non_unique.py b/snippets/List/filter_non_unique.py new file mode 100644 index 0000000..48ed6cd --- /dev/null +++ b/snippets/List/filter_non_unique.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：创建一个包含过滤掉的非唯一值的列表 + +解读： +使用collections.Counter以获取列表中每个值的计数。 +使用列表推导式创建只包含唯一值的列表。 +""" +from collections import Counter + + +def filter_non_unique(lst): + return [item for item, count in Counter(lst).items() if count == 1] + + +# Examples + +print(filter_non_unique([1, 2, 2, 3, 4, 4, 5])) +# output: +# [1, 3, 5] diff --git a/snippets/List/filter_unique.py b/snippets/List/filter_unique.py new file mode 100644 index 0000000..9888a7d --- /dev/null +++ b/snippets/List/filter_unique.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：创建一个包含过滤掉的唯一值的列表。 + +解读： +使用collections.Counter获取列表中每个值的计数。 +使用列表推导式来创建仅包含非唯一值的列表。 +""" +from collections import Counter + + +def filter_unique(lst): + return [item for item, count in Counter(lst).items() if count > 1] + + +# Examples + +print(filter_unique([1, 2, 2, 3, 4, 4, 5])) +# output: +# [2, 4] diff --git a/snippets/List/find.py b/snippets/List/find.py new file mode 100644 index 0000000..e4b822c --- /dev/null +++ b/snippets/List/find.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：查找给定列表中满足所提供的测试函数的第一个元素的值。 + +解读： +使用列表推导式和next()返回lst中fn返回True的第一个元素。 +""" + + +def find(lst, fn): + return next(x for x in lst if fn(x)) + + +# Examples + +print(find([1, 2, 3, 4], lambda n: n % 2 == 1)) +# output: +# 1 diff --git a/snippets/List/find_index.py b/snippets/List/find_index.py new file mode 100644 index 0000000..ba44847 --- /dev/null +++ b/snippets/List/find_index.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：查找给定列表中满足所提供的测试函数的第一个元素的索引 + +解读： +使用列表推导式、enumerate()和next()返回lst中fn返回True的第一个元素的索引。 +""" + + +def find_index(lst, fn): + return next(i for i, x in enumerate(lst) if fn(x)) + + +# Examples + +print(find_index([1, 2, 3, 4], lambda n: n % 2 == 1)) +# output: +# 0 diff --git a/snippets/List/find_index_of_all.py b/snippets/List/find_index_of_all.py new file mode 100644 index 0000000..831f8f1 --- /dev/null +++ b/snippets/List/find_index_of_all.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：查找给定列表中满足所提供的测试函数的所有元素的索引。 + +解读： +使用enumerate()和一个列表推导式来返回fn返回True的lst中所有元素的索引。 +""" + + +def find_index_of_all(lst, fn): + return [i for i, x in enumerate(lst) if fn(x)] + + +# Examples + +print(find_index_of_all([1, 2, 3, 4], lambda n: n % 2 == 1)) +# output: +# [0, 2] diff --git a/snippets/List/find_last.py b/snippets/List/find_last.py new file mode 100644 index 0000000..dfad73a --- /dev/null +++ b/snippets/List/find_last.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：查找给定列表中满足所提供的测试函数的最后一个元素的值。 + +解读： +使用列表推导式和next()返回lst中fn返回True的最后一个元素。 +""" + + +def find_last(lst, fn): + return next(x for x in lst[::-1] if fn(x)) + + +# Examples + +print(find_last([1, 2, 3, 4], lambda n: n % 2 == 1)) +# output: +# 3 diff --git a/snippets/List/find_last_index.py b/snippets/List/find_last_index.py new file mode 100644 index 0000000..c44a5ec --- /dev/null +++ b/snippets/List/find_last_index.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：查找给定列表中满足所提供的测试函数的最后一个元素的索引。 + +解读： +使用列表推导式、enumerate()和next()返回lst中fn返回True的最后一个元素的索引。 +""" + + +def find_last_index(lst, fn): + return len(lst) - 1 - next(i for i, x in enumerate(lst[::-1]) if fn(x)) + + +# Examples + +print(find_last_index([1, 2, 3, 4], lambda n: n % 2 == 1)) +# output: +# 2 diff --git a/snippets/List/find_parity_outliers.py b/snippets/List/find_parity_outliers.py new file mode 100644 index 0000000..8fff262 --- /dev/null +++ b/snippets/List/find_parity_outliers.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：查找给定列表中奇偶校验异常值的项。 + +解读： +使用集合。使用列表推导式计算列表中的偶数和奇数值。 +使用collections.Counter.most_common()来获得最常见的奇偶校验。 +使用列表推导式查找所有不匹配最常见奇偶校验的元素。 +""" +from collections import Counter + + +def find_parity_outliers(nums): + return [ + x for x in nums + if x % 2 != Counter([n % 2 for n in nums]).most_common()[0][0] + ] + + +# Examples + +print(find_parity_outliers([1, 2, 3, 4, 6])) +# output: +# [1, 3] diff --git a/snippets/List/flatten.py b/snippets/List/flatten.py new file mode 100644 index 0000000..0903936 --- /dev/null +++ b/snippets/List/flatten.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：把列表的列表变平一次。 + +解读： +使用列表推导式按顺序从子列表中提取每个值。 +""" + + +def flatten(lst): + return [x for y in lst for x in y] + + +# Examples + +print(flatten([[1, 2, 3, 4], [5, 6, 7, 8]])) +# output: +# [1, 2, 3, 4, 5, 6, 7, 8] diff --git a/snippets/List/for_each.py b/snippets/List/for_each.py new file mode 100644 index 0000000..3ff91fc --- /dev/null +++ b/snippets/List/for_each.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：对每个列表元素执行一次提供的函数 + +解读： +使用for循环对itr中的每个元素执行fn。 +""" + + +def for_each(itr, fn): + for el in itr: + fn(el) + + +# Examples + +for_each([1, 2, 3], print) +# output: +# 1 2 3 diff --git a/snippets/List/for_each_right.py b/snippets/List/for_each_right.py new file mode 100644 index 0000000..d17df32 --- /dev/null +++ b/snippets/List/for_each_right.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：对每个列表元素执行一次提供的函数，从列表的最后一个元素开始。 + +解读： +结合使用for循环和slice符号对itr中的每个元素执行fn，从最后一个元素开始。 +""" + + +def for_each_right(itr, fn): + for el in itr[::-1]: + fn(el) + + +# Examples + +for_each_right([1, 2, 3], print) +# output: +# 3 2 1 diff --git a/snippets/List/frequencies.py b/snippets/List/frequencies.py new file mode 100644 index 0000000..39f6211 --- /dev/null +++ b/snippets/List/frequencies.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：创建一个字典，其中列表的唯一值作为键，它们的频率作为值。 + +解读： +使用collections.defaultdict()来存储每个唯一元素的频率。 +使用dict()返回一个字典，其中列表中唯一的元素作为键，它们的频率作为值。 + +该函数与collections.Counter功能一样 +""" +from collections import defaultdict + + +def frequencies(lst): + freq = defaultdict(int) + for val in lst: + freq[val] += 1 + return dict(freq) + + +# Examples + +print(frequencies(['a', 'b', 'a', 'c', 'a', 'a', 'b'])) +# output: +# { 'a': 4, 'b': 2, 'c': 1 } diff --git a/snippets/List/geometric_progression.py b/snippets/List/geometric_progression.py new file mode 100644 index 0000000..feb2304 --- /dev/null +++ b/snippets/List/geometric_progression.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：初始化一个列表，该列表包含指定范围内的数字，其中包括起始和结束，两个术语之间的比率为step。如果step = 1，则返回错误。 + +解读： +使用range()、math.log()和math.floor()以及一个列表推导式来创建一个适当长度的列表，并为每个元素应用step。 +忽略第二个参数start，使用默认值1。 +忽略第三个参数step，使用默认值2。 +""" +from math import floor, log + + +def geometric_progression(end, start=1, step=2): + return [start * step**i for i in range(floor(log(end / start) / log(step)) + 1)] + + +# Examples + +print(geometric_progression(256)) +print(geometric_progression(256, 3)) +print(geometric_progression(256, 1, 4)) +# output: +# [1, 2, 4, 8, 16, 32, 64, 128, 256] +# [3, 6, 12, 24, 48, 96, 192] +# [1, 4, 16, 64, 256] diff --git a/snippets/List/get.py b/snippets/List/get.py new file mode 100644 index 0000000..37f7f85 --- /dev/null +++ b/snippets/List/get.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：从字典或列表中检索给定选择器列表指示的嵌套键的值。 + +解读： +使用functools.reduce()遍历选择器列表。 +对选择器中的每个键应用operator.getitem()，检索将用于下一次迭代的迭代对象的值。 +""" + +from functools import reduce +from operator import getitem + + +def get(d, selectors): + return reduce(getitem, selectors, d) + + +# Examples + +users = { + 'freddy': { + 'name': { + 'first': 'fred', + 'last': 'smith' + }, + 'postIds': [1, 2, 3] + } +} +print(get(users, ['freddy', 'name', 'last'])) +print(get(users, ['freddy', 'postIds', 1])) +# output: +# smith +# 2 diff --git a/snippets/List/group_by.py b/snippets/List/group_by.py new file mode 100644 index 0000000..312bbef --- /dev/null +++ b/snippets/List/group_by.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：根据给定的函数对列表中的元素进行分组。 + +解读： +使用collections.defaultdict初始化字典。 +结合使用fn与for循环和dict.append()来填充字典。 +使用dict()将其转换为普通字典。 +""" +from collections import defaultdict +from math import floor + + +def group_by(lst, fn): + d = defaultdict(list) + for el in lst: + d[fn(el)].append(el) + return dict(d) + + +# Examples + +print(group_by([6.1, 4.2, 6.3], floor)) +print(group_by(['one', 'two', 'three'], len)) +# output: +# {4: [4.2], 6: [6.1, 6.3]} +# {3: ['one', 'two'], 5: ['three']} diff --git a/snippets/List/has_duplicates.py b/snippets/List/has_duplicates.py new file mode 100644 index 0000000..253667e --- /dev/null +++ b/snippets/List/has_duplicates.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查平面列表中是否有重复的值。 + +解读： +在给定的列表中使用set()删除重复项，并将其长度与列表长度进行比较。 +""" + + +def has_duplicates(lst): + return len(lst) != len(set(lst)) + + +# Examples + +x = [1, 2, 3, 4, 5, 5] +y = [1, 2, 3, 4, 5] +print(has_duplicates(x)) +print(has_duplicates(y)) +# output: +# True +# False diff --git a/snippets/List/have_same_contents.py b/snippets/List/have_same_contents.py new file mode 100644 index 0000000..8f4b6c9 --- /dev/null +++ b/snippets/List/have_same_contents.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查两个列表是否包含相同的元素，而不考虑顺序。 + +解读： +对两个列表的组合使用set()来查找唯一的值。 +使用for循环遍历它们，比较每个列表中每个唯一值的count()。 +如果计数不匹配任何元素，则返回False，否则返回True。 +""" + + +def have_same_contents(a, b): + return all(a.count(v) == b.count(v) for v in set(a + b)) + + +# Examples + +print(have_same_contents([1, 2, 4], [2, 4, 1])) +# output: +# True diff --git a/snippets/List/head.py b/snippets/List/head.py new file mode 100644 index 0000000..e1a7ba0 --- /dev/null +++ b/snippets/List/head.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回列表的头。 + +解读： +使用lst[0]返回所传递列表的第一个元素。 +""" + + +def head(lst): + return lst[0] + + +# Examples + +print(head([1, 2, 3])) +# output: +# 1 diff --git a/snippets/List/includes_all.py b/snippets/List/includes_all.py new file mode 100644 index 0000000..70313c3 --- /dev/null +++ b/snippets/List/includes_all.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查值中的所有元素是否都包含在lst中 + +解读： +使用for循环检查value中的每个值是否都包含在lst中。 +如果找不到任何一个值则返回False，否则返回True。 +""" + + +def includes_all(lst, values): + return all(v in lst for v in values) + + +# Examples + +print(includes_all([1, 2, 3, 4], [1, 4])) +print(includes_all([1, 2, 3, 4], [1, 5])) +# output: +# True +# False diff --git a/snippets/List/includes_any.py b/snippets/List/includes_any.py new file mode 100644 index 0000000..cd924a5 --- /dev/null +++ b/snippets/List/includes_any.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查value中的任何元素是否包含在lst中。 + +解读： +使用for循环检查values中的任何值是否包含在lst中。 +如果找到任何一个值则返回True，否则返回False。 +""" + + +def includes_any(lst, values): + return any(v in lst for v in values) + + +# Examples + +print(includes_any([1, 2, 3, 4], [2, 9])) +print(includes_any([1, 2, 3, 4], [8, 9])) +# output: +# True +# False diff --git a/snippets/List/index_of_all.py b/snippets/List/index_of_all.py new file mode 100644 index 0000000..a42329f --- /dev/null +++ b/snippets/List/index_of_all.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回列表中某个元素出现的所有索引的列表。 + +解读： +使用enumerate()和一个列表推导式来检查每个元素是否与value相等，并在结果中添加i。 +""" +def index_of_all(lst, value): + return [i for i, x in enumerate(lst) if x == value] + + +# Examples + +print(index_of_all([1, 2, 1, 4, 5, 1], 1)) +print(index_of_all([1, 2, 3, 4], 6)) +# output： +# [0, 2, 5] +# [] diff --git a/snippets/List/initial.py b/snippets/List/initial.py new file mode 100644 index 0000000..c24f0aa --- /dev/null +++ b/snippets/List/initial.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回列表中除最后一个元素外的所有元素。 + +解读： +使用lst[:-1]返回列表中除最后一个元素外的所有元素。 +""" + + +def initial(lst): + return lst[:-1] + + +# Examples + +print(initial([1, 2, 3])) +# output： +# [1, 2] diff --git a/snippets/List/initialize_2d_list.py b/snippets/List/initialize_2d_list.py new file mode 100644 index 0000000..739fe71 --- /dev/null +++ b/snippets/List/initialize_2d_list.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：初始化给定宽度、高度和值的2D列表。 + +解读： +使用列表推导式和range()生成h行，其中每一行都是长度为h的列表，用val初始化。 +忽略最后一个参数val，将默认值设置为None。 +""" + + +def initialize_2d_list(w, h, val=None): + return [[val for _ in range(w)] for _ in range(h)] + + +# Examples + +print(initialize_2d_list(2, 2, 0)) +# output: +# [[0, 0], [0, 0]] diff --git a/snippets/List/initialize_list_with_range.py b/snippets/List/initialize_list_with_range.py new file mode 100644 index 0000000..362d1fc --- /dev/null +++ b/snippets/List/initialize_list_with_range.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：初始化一个列表，该列表包含指定范围内的数字，其中start和end包含它们的共同差值步骤。 + +解读： +使用list()和range()生成一个适当长度的列表，在给定的范围内填充所需的值。 +省略start以使用默认值0 +省略步长，使用默认值1 +""" + + +def initialize_list_with_range(end, start=0, step=1): + return list(range(start, end + 1, step)) + + +# Examples + +print(initialize_list_with_range(5)) +print(initialize_list_with_range(7, 3)) +print(initialize_list_with_range(9, 0, 2)) +# output: +# [0, 1, 2, 3, 4, 5] +# [3, 4, 5, 6, 7] +# [0, 2, 4, 6, 8] diff --git a/snippets/List/initialize_list_with_values.py b/snippets/List/initialize_list_with_values.py new file mode 100644 index 0000000..f9dbcde --- /dev/null +++ b/snippets/List/initialize_list_with_values.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：初始化并使用指定的值填充列表。 + +解读： +使用列表推导式和range()生成一个长度等于n的列表，并填充所需的值。 +省略val，使用默认值0。 +""" + + +def initialize_list_with_values(n, val=0): + return [val for _ in range(n)] + + +# Examples + +print(initialize_list_with_values(5, 2)) +# output: +# [2, 2, 2, 2, 2] diff --git a/snippets/List/intersection.py b/snippets/List/intersection.py new file mode 100644 index 0000000..339e6f1 --- /dev/null +++ b/snippets/List/intersection.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回两个列表中都存在的元素列表。 + +解读： +从a和b创建一个集合 +使用内置的集合运算符＆仅保留两个集合中包含的值，然后将集合转换回列表。 +""" + + +def intersection(a, b): + _a, _b = set(a), set(b) + return list(_a & _b) + + +# Examples + +print(intersection([1, 2, 3], [4, 3, 2])) +# output: +# [2, 3] diff --git a/snippets/List/intersection_by.py b/snippets/List/intersection_by.py new file mode 100644 index 0000000..a6441ae --- /dev/null +++ b/snippets/List/intersection_by.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将提供的函数应用于两个列表的每个列表元素后，返回两个列表中存在的元素列表。 + +解读： +创建一个集合，使用map()将fn应用到b中的每个元素。 +在a上结合使用一个列表推导式和fn来只保留包含在两个列表中的值。 +""" +from math import floor + + +def intersection_by(a, b, fn): + _b = set(map(fn, b)) + return [item for item in a if fn(item) in _b] + + +# Examples + +print(intersection_by([2.1, 1.2], [2.3, 3.4], floor)) +# output: +# [2.1] diff --git a/snippets/List/is_contained_in.py b/snippets/List/is_contained_in.py new file mode 100644 index 0000000..c711c7a --- /dev/null +++ b/snippets/List/is_contained_in.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查第一个列表的元素是否包含在第二个列表中，而不考虑顺序。 + +解读： +使用count()检查某个值在a中的出现次数是否多于在b中的出现次数。 +如果找到任何这样的值则返回False，否则返回True。 +""" + + +def is_contained_in(a, b): + return all(a.count(v) <= b.count(v) for v in set(a)) + + +# Examples + +print(is_contained_in([1, 4], [2, 4, 1])) +# output: +# True diff --git a/snippets/List/keys_only.py b/snippets/List/keys_only.py new file mode 100644 index 0000000..80473c1 --- /dev/null +++ b/snippets/List/keys_only.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：创建平面字典中所有键的平面列表。 + +解读： +使用dict.keys()返回给定字典中的键值。 +返回前一个结果的list()。 +""" + + +def keys_only(flat_dict): + return list(flat_dict.keys()) + + +# Examples + +ages = { + 'Peter': 10, + 'Isabel': 11, + 'Anna': 9, +} +print(keys_only(ages)) +# output: +# ['Peter', 'Isabel', 'Anna'] diff --git a/snippets/List/last.py b/snippets/List/last.py new file mode 100644 index 0000000..fb84782 --- /dev/null +++ b/snippets/List/last.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回列表中的最后一个元素。 + +解读： +使用lst[-1]返回所传递列表的最后一个元 +""" + + +def last(lst): + return lst[-1] + + +# Examples + +print(last([1, 2, 3])) +# output: +# 3 diff --git a/snippets/List/lcm.py b/snippets/List/lcm.py new file mode 100644 index 0000000..97e5333 --- /dev/null +++ b/snippets/List/lcm.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回数字列表的最小公倍数。 + +解读： +在给定的列表上使用functools.reduce()， math.gcd()和lcm(x,y) = x * y / gcd(x,y)。 +""" +from functools import reduce +from math import gcd + + +def lcm(numbers): + return reduce((lambda x, y: int(x * y / gcd(x, y))), numbers) + + +# Examples + +print(lcm([12, 7])) +print(lcm([1, 3, 4, 5])) +# output: +# 84 +# 60 diff --git a/snippets/List/longest_item.py b/snippets/List/longest_item.py new file mode 100644 index 0000000..5afd3bc --- /dev/null +++ b/snippets/List/longest_item.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：接受任意数量的可迭代对象或带有length属性的对象，并返回最长的那个。 + +解读： +使用max()和len()作为键来返回最大长度的项。 +如果多个对象具有相同的长度，则返回第一个对象。 +""" + + +def longest_item(*args): + return max(args, key=len) + + +# Examples + +print(longest_item('this', 'is', 'a', 'testcase')) +print(longest_item([1, 2, 3], [1, 2], [1, 2, 3, 4, 5])) +print(longest_item([1, 2, 3], 'foobar')) +# output: +# testcase +# [1, 2, 3, 4, 5] +# foobar diff --git a/snippets/List/map_dictionary.py b/snippets/List/map_dictionary.py new file mode 100644 index 0000000..31f6abe --- /dev/null +++ b/snippets/List/map_dictionary.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用函数将列表的值映射到字典，其中键-值对由原始值作为键，函数的结果作为值组成。 + +解读： +使用map()将fn应用到列表的每个值。 +使用zip()将原始值与fn生成的值配对。 +使用dict()返回一个合适的字典。 +""" + + +def map_dictionary(itr, fn): + return dict(zip(itr, map(fn, itr))) + + +# Examples + +print(map_dictionary([1, 2, 3], lambda x: x * x)) +# output: +# { 1: 1, 2: 4, 3: 9 } diff --git a/snippets/List/max_by.py b/snippets/List/max_by.py new file mode 100644 index 0000000..0d07b29 --- /dev/null +++ b/snippets/List/max_by.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用提供的函数将每个元素映射到一个值后，返回列表的最大值。 + +解读： +使用map()和fn，使用提供的函数将每个元素映射到一个值。 +使用max()返回最大值。 +""" + + +def max_by(lst, fn): + return max(map(fn, lst)) + + +# Examples + +print(max_by([{'n': 4}, {'n': 2}, {'n': 8}, {'n': 6}], lambda v: v['n'])) +# output: +# 8 diff --git a/snippets/List/max_element_index.py b/snippets/List/max_element_index.py new file mode 100644 index 0000000..6c910bc --- /dev/null +++ b/snippets/List/max_element_index.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回列表中具有最大值的元素的索引。 + +解读： +使用max()和list.index()获取列表中的最大值并返回其索引。 +""" + + +def max_element_index(arr): + return arr.index(max(arr)) + + +# Examples + +print(max_element_index([5, 8, 9, 7, 10, 3, 0])) +# output: +# 4 diff --git a/snippets/List/max_n.py b/snippets/List/max_n.py new file mode 100644 index 0000000..50243b8 --- /dev/null +++ b/snippets/List/max_n.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回提供的列表中的n个最大元素。 + +解读： +使用sorted()对列表进行排序。 +使用切片表示法获得指定数量的元素 +省略第二个参数n，得到一个只有一个元素的列表。 +如果n大于或等于提供的列表的长度，则返回原始列表(按降序排序)。 +""" + + +def max_n(lst, n=1): + return sorted(lst, reverse=True)[:n] + + +# Examples + +print(max_n([1, 2, 3])) +print(max_n([1, 2, 3], 2)) +# output: +# [3] +# [3, 2] diff --git a/snippets/List/merge.py b/snippets/List/merge.py new file mode 100644 index 0000000..a461fa2 --- /dev/null +++ b/snippets/List/merge.py @@ -0,0 +1,33 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将两个或多个列表合并为列表的列表，根据每个输入列表的位置组合元素。 + +解读： +使用max()与列表推导式结合使用来获取参数中最长列表的长度。 +将range()与max_length变量结合使用，只要最长列表中有元素，就可以循环多次。 +如果列表小于max_length，剩余的项使用fill_value(默认为None)。 +zip()和itertools.zip_longest()提供了与此代码片段类似的功能。 +""" + + +def merge(*args, fill_value=None): + max_length = max(len(lst) for lst in args) + return [ + [ + args[k][i] if i < len(args[k]) else fill_value + for k in range(len(args)) + ] + for i in range(max_length) + ] + + +# Examples + +print(merge(['a', 'b'], [1, 2], [True, False])) +print(merge(['a'], [1, 2], [True, False])) +print(merge(['a'], [1, 2], [True, False], fill_value='_')) +# output: +# [['a', 1, True], ['b', 2, False]] +# [['a', 1, True], [None, 2, False]] +# [['a', 1, True], ['_', 2, False]] diff --git a/snippets/List/min_by.py b/snippets/List/min_by.py new file mode 100644 index 0000000..313a97a --- /dev/null +++ b/snippets/List/min_by.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用提供的函数将每个元素映射到一个值后，返回列表的最小值。 + +解读： +使用map()和fn，使用提供的函数将每个元素映射到一个值。 +使用min()返回最小值。 +""" + + +def min_by(lst, fn): + return min(map(fn, lst)) + + +# Examples + +print(min_by([{'n': 4}, {'n': 2}, {'n': 8}, {'n': 6}], lambda v: v['n'])) +# output: +# 2 diff --git a/snippets/List/min_element_index.py b/snippets/List/min_element_index.py new file mode 100644 index 0000000..a83c843 --- /dev/null +++ b/snippets/List/min_element_index.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回列表中具有最小值的元素的索引。 + +解读： +使用min()和list.index()获取列表中的最小值，然后返回其索引。 +""" + + +def min_element_index(arr): + return arr.index(min(arr)) + + +# Examples + +print(min_element_index([3, 5, 2, 6, 10, 7, 9])) +# output: +# 2 diff --git a/snippets/List/min_n.py b/snippets/List/min_n.py new file mode 100644 index 0000000..bee861e --- /dev/null +++ b/snippets/List/min_n.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：从提供的列表中返回n个最小元素。 + +解读： +使用sorted()对列表进行排序。 +使用切片表示法获得指定数量的元素。 +省略第二个参数n，得到一个只有一个元素的列表。 +如果n大于或等于提供的列表的长度，则返回原始列表(按升序排序)。 +""" + + +def min_n(lst, n=1): + return sorted(lst, reverse=False)[:n] + + +# Examples + +print(min_n([1, 2, 3])) +print(min_n([1, 2, 3], 2)) +# output: +# [1] +# [1, 2] diff --git a/snippets/List/most_frequent.py b/snippets/List/most_frequent.py new file mode 100644 index 0000000..3dd3cd4 --- /dev/null +++ b/snippets/List/most_frequent.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回列表中最常用的元素。 + +解读： +使用set()获取lst中唯一的值。 +使用max()查找外观最多的元素。 +""" + + +def most_frequent(lst): + return max(set(lst), key=lst.count) + + +# Examples + +print(most_frequent([1, 2, 1, 2, 3, 2, 1, 4, 2])) +# output： +# 2 diff --git a/snippets/List/none.py b/snippets/List/none.py new file mode 100644 index 0000000..13e612e --- /dev/null +++ b/snippets/List/none.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查所提供的函数是否对列表中至少一个元素返回True。 + +解读： +使用all()和fn检查fn是否为列表中的所有元素返回False。 +""" + + +def none(lst, fn=lambda x: x): + return all(not fn(x) for x in lst) + + +# Examples + +print(none([0, 1, 2, 0], lambda x: x >= 2)) +print(none([0, 0, 0])) +# output: +# False +# True diff --git a/snippets/List/offset.py b/snippets/List/offset.py new file mode 100644 index 0000000..eb16ad8 --- /dev/null +++ b/snippets/List/offset.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将指定数量的元素移动到列表的末尾 + +解读： +使用切片表示法获得列表的两个切片，并在返回之前组合它们。 +""" + + +def offset(lst, offset): + return lst[offset:] + lst[:offset] + + +# Examples + +print(offset([1, 2, 3, 4, 5], 2)) +print(offset([1, 2, 3, 4, 5], -2)) +# output: +# [3, 4, 5, 1, 2] +# [4, 5, 1, 2, 3] diff --git a/snippets/List/pluck.py b/snippets/List/pluck.py new file mode 100644 index 0000000..c6c6d41 --- /dev/null +++ b/snippets/List/pluck.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将字典列表转换为与指定键对应的值列表。 + +解读： +使用列表推导式和dict.get()来获取lst中每个字典的key值。 +""" + + +def pluck(lst, key): + return [x.get(key) for x in lst] + + +# Examples + +simpsons = [ + {'name': 'lisa', 'age': 8}, + {'name': 'homer', 'age': 36}, + {'name': 'marge', 'age': 34}, + {'name': 'bart', 'age': 10} +] +print(pluck(simpsons, 'age')) +# output: +# [8, 36, 34, 10] diff --git a/snippets/List/powerset.py b/snippets/List/powerset.py new file mode 100644 index 0000000..e982344 --- /dev/null +++ b/snippets/List/powerset.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回给定迭代器的幂集。 + +解读： +使用list()将给定值转换为list。 +使用range()和itertools.combination()创建一个返回所有子集的生成器。 +使用itertools.chain.from_iterable()和list()来使用生成器并返回一个列表。 +""" +from itertools import chain, combinations + + +def powerset(iterable): + s = list(iterable) + return list(chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))) + + +# Examples + +print(powerset([1, 2])) +# output: +# [(), (1,), (2,), (1, 2)] diff --git a/snippets/List/reverse.py b/snippets/List/reverse.py new file mode 100644 index 0000000..748c16b --- /dev/null +++ b/snippets/List/reverse.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：反转列表或字符串。 + +解读: +使用切片表示法反转列表或字符串。 +""" + + +def reverse(itr): + return itr[::-1] + + +# Examples + +print(reverse([1, 2, 3])) +print(reverse('snippet')) +# output: +# [3, 2, 1] +# teppins diff --git a/snippets/List/roll.py b/snippets/List/roll.py new file mode 100644 index 0000000..a9ab7ad --- /dev/null +++ b/snippets/List/roll.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将指定数量的元素移动到列表的开头。 + +解读： +使用切片表示法获得列表的两个切片，并在返回之前组合它们。 +""" + + +def roll(lst, offset): + return lst[-offset:] + lst[:-offset] + + +# Examples + +print(roll([1, 2, 3, 4, 5], 2)) +print(roll([1, 2, 3, 4, 5], -2)) +# output: +# [4, 5, 1, 2, 3] +# [3, 4, 5, 1, 2] diff --git a/snippets/List/sample.py b/snippets/List/sample.py new file mode 100644 index 0000000..9669969 --- /dev/null +++ b/snippets/List/sample.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：从列表中返回一个随机元素。 + +解读： +使用random.choice()从lst中获取一个随机元素 +""" +from random import choice + + +def sample(lst): + return choice(lst) + + +# Examples + +print(sample([3, 7, 9, 11])) +# output: +# 9 diff --git a/snippets/List/shuffle.py b/snippets/List/shuffle.py new file mode 100644 index 0000000..25468c0 --- /dev/null +++ b/snippets/List/shuffle.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：随机化列表中值的顺序，返回一个新的列表。 + +解读： +使用Fisher-Yates算法对列表中的元素重新排序。 +random.shuffle提供了与此代码片段类似的功能。 +""" +from copy import deepcopy +from random import randint + + +def shuffle(lst): + temp_lst = deepcopy(lst) + m = len(temp_lst) + while (m): + m -= 1 + i = randint(0, m) + temp_lst[m], temp_lst[i] = temp_lst[i], temp_lst[m] + return temp_lst + + +# Examples + +foo = [1, 2, 3] +print(shuffle(foo), f', foo = {foo}') +# output: +# [2, 3, 1], foo = [1, 2, 3] diff --git a/snippets/List/similarity.py b/snippets/List/similarity.py new file mode 100644 index 0000000..f63ca20 --- /dev/null +++ b/snippets/List/similarity.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回两个列表中都存在的元素列表。 + +解读： +对a使用列表推导式，只保留包含在两个列表中的值 +""" + + +def similarity(a, b): + return [item for item in a if item in b] + + +# Examples + +print(similarity([1, 2, 3], [1, 2, 4])) +# output: +# [1, 2] diff --git a/snippets/List/some.py b/snippets/List/some.py new file mode 100644 index 0000000..031d97f --- /dev/null +++ b/snippets/List/some.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查所提供的函数是否对列表中至少一个元素返回True。 + +解读： +结合使用any()和map()检查fn是否为列表中的任何元素返回True +""" + + +def some(lst, fn=lambda x: x): + return any(map(fn, lst)) + + +# Examples + +print(some([0, 1, 2, 0], lambda x: x >= 2)) +print(some([0, 0, 1, 0])) +# output: +# True +# True diff --git a/snippets/List/sort_by_indexes.py b/snippets/List/sort_by_indexes.py new file mode 100644 index 0000000..1799b8e --- /dev/null +++ b/snippets/List/sort_by_indexes.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：根据包含所需索引的另一个列表对一个列表进行排序。 + +解读： +使用zip()和sorted()根据索引的值组合并排序这两个列表。 +使用列表推导式从结果中获取每对元素的第一个元素。 +使用sorted()中的reverse参数根据第三个参数对字典进行反向排序。 +""" + + +def sort_by_indexes(lst, indexes, reverse=False): + return [val for (_, val) in sorted(zip(indexes, lst), key=lambda x: x[0], reverse=reverse)] + + +# Examples + +a = ['eggs', 'bread', 'oranges', 'jam', 'apples', 'milk'] +b = [3, 2, 6, 4, 1, 5] +print(sort_by_indexes(a, b)) +print(sort_by_indexes(a, b, True)) +# output: +# ['apples', 'bread', 'eggs', 'jam', 'milk', 'oranges'] +# ['oranges', 'milk', 'jam', 'eggs', 'bread', 'apples'] diff --git a/snippets/List/spread.py b/snippets/List/spread.py new file mode 100644 index 0000000..cdcadad --- /dev/null +++ b/snippets/List/spread.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：通过将列表中的元素扩展到一个新的列表中，使列表变平。 + +解读： +循环遍历元素，如果元素是列表则使用list.extend()，否则使用list.append()。 +""" + + +def spread(arg): + ret = [] + for i in arg: + ret.extend(i) if isinstance(i, list) else ret.append(i) + return ret + + +# Examples + +print(spread([1, 2, 3, [4, 5, 6], [7], 8, 9])) +# output: +# [1, 2, 3, 4, 5, 6, 7, 8, 9] diff --git a/snippets/List/sum_by.py b/snippets/List/sum_by.py new file mode 100644 index 0000000..7ddae67 --- /dev/null +++ b/snippets/List/sum_by.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用提供的函数将每个元素映射到一个值之后，计算列表的总和。 + +解读： +使用map()和fn，使用提供的函数将每个元素映射到一个值。 +使用sum()返回值的和。 +""" + + +def sum_by(lst, fn): + return sum(map(fn, lst)) + + +# Examples + +print(sum_by([{'n': 4}, {'n': 2}, {'n': 8}, {'n': 6}], lambda v: v['n'])) +# output: +# 20 diff --git a/snippets/List/symmetric_difference.py b/snippets/List/symmetric_difference.py new file mode 100644 index 0000000..8dbbe1e --- /dev/null +++ b/snippets/List/symmetric_difference.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回两个可迭代对象之间的对称差，而不会过滤出重复的值。 + +解读： +从每个列表创建一个集合。 +对它们中的每一个都使用列表推导式，以只保留先前创建的另一个集合中不包含的值。 +""" + + +def symmetric_difference(a, b): + (_a, _b) = (set(a), set(b)) + return [item for item in a if item not in _b] + [item for item in b if item not in _a] + + +# Examples + +print(symmetric_difference([1, 2, 3], [1, 2, 4])) +# output: +# [3, 4] diff --git a/snippets/List/symmetric_difference_by.py b/snippets/List/symmetric_difference_by.py new file mode 100644 index 0000000..5576f95 --- /dev/null +++ b/snippets/List/symmetric_difference_by.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将提供的函数应用于两个列表的每个列表元素后，返回两个列表之间的对称差异。 + +解读： +通过对每个列表中的每个元素应用fn来创建一个集合。 +将列表推导与fn一起使用时，仅保留先前创建的另一个列表中未包含的值。 +""" +from math import floor + + +def symmetric_difference_by(a, b, fn): + (_a, _b) = (set(map(fn, a)), set(map(fn, b))) + return [item for item in a if fn(item) not in _b] + [item for item in b if fn(item) not in _a] + + +# Examples + +print(symmetric_difference_by([2.1, 1.2], [2.3, 3.4], floor)) +# output: +# [1.2, 3.4] diff --git a/snippets/List/tail.py b/snippets/List/tail.py new file mode 100644 index 0000000..6b74fcb --- /dev/null +++ b/snippets/List/tail.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回列表中除第一个元素外的所有元素。 + +解读： +如果列表的长度大于1，则使用切片表示法返回最后一个元素。 +否则，返回整个列表。 +""" + + +def tail(lst): + return lst[1:] if len(lst) > 1 else lst + + +# Examples + +print(tail([1, 2, 3])) +print(tail([1])) +# output: +# [2, 3] +# [1] diff --git a/snippets/List/take.py b/snippets/List/take.py new file mode 100644 index 0000000..7c90eb0 --- /dev/null +++ b/snippets/List/take.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回一个从开头移除n个元素的列表 + +解读： +使用切片表示法创建一个包含n个从开始取的元素的列表切片。 +""" + + +def take(itr, n=1): + return itr[:n] + + +# Examples + +print(take([1, 2, 3], 5)) +print(take([1, 2, 3], 0)) +# output: +# [1, 2, 3] +# [] diff --git a/snippets/List/take_right.py b/snippets/List/take_right.py new file mode 100644 index 0000000..af3ec05 --- /dev/null +++ b/snippets/List/take_right.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回一个从末尾移除n个元素的列表。 + +解读： +使用切片表示法创建一个包含从末尾取的n个元素的列表切片。 +""" + + +def take_right(itr, n=1): + return itr[-n:] + + +# Examples + +print(take_right([1, 2, 3], 2)) +print(take_right([1, 2, 3])) +# output: +# [2, 3] +# [3] diff --git a/snippets/List/to_dictionary.py b/snippets/List/to_dictionary.py new file mode 100644 index 0000000..dd22874 --- /dev/null +++ b/snippets/List/to_dictionary.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将两个列表组合到一个字典中，其中第一个列表的元素作为键，第二个列表的元素作为值。第一个列表的值必须是唯一的，并且是可哈希的。 + +解读： +将zip()与dict()结合使用，可以将两个列表的值组合到一个字典中。 +""" + + +def to_dictionary(keys, values): + return dict(zip(keys, values)) + + +# Examples + +print(to_dictionary(['a', 'b'], [1, 2])) +# output: +# { a: 1, b: 2 } diff --git a/snippets/List/transpose.py b/snippets/List/transpose.py new file mode 100644 index 0000000..7a7f2ef --- /dev/null +++ b/snippets/List/transpose.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：对二维列表进行转置。 + +解读： +使用*lst以元组的形式获取提供的列表。 +结合使用zip()和list()来创建给定二维列表的转置。 +""" + + +def transpose(lst): + return list(zip(*lst)) + + +# Examples + +print(transpose([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])) +# output: +# [(1, 4, 7, 10), (2, 5, 8, 11), (3, 6, 9, 12)] diff --git a/snippets/List/unfold.py b/snippets/List/unfold.py new file mode 100644 index 0000000..7cc4f6d --- /dev/null +++ b/snippets/List/unfold.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用迭代器函数和初始种子值构建列表。 + +解读： +迭代器函数接受一个参数(seed)，并且必须总是返回一个包含两个元素的列表([value, nextSeed])，或者返回False以终止。 +使用生成器函数fn_generator，它使用while循环调用迭代器函数并生成该值，直到它返回False。 +使用列表推导式，使用迭代器函数返回生成器生成的列表。 +""" + + +def unfold(fn, seed): + def fn_generator(val): + while True: + val = fn(val[1]) + if val is False: break + yield val[0] + + return list(fn_generator([None, seed])) + + +# Examples + +f = lambda n: False if n > 50 else [-n, n + 10] +print(unfold(f, 10)) +# output: +# [-10, -20, -30, -40, -50] diff --git a/snippets/List/union.py b/snippets/List/union.py new file mode 100644 index 0000000..1f06d83 --- /dev/null +++ b/snippets/List/union.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回两个列表中存在的每个元素一次 + +解读： +创建一个包含a和b的所有值的集合，并将其转换为一个列表。 +""" + + +def union(a, b): + return list(set(a + b)) + + +# Examples + +print(union([1, 2, 3], [4, 3, 2])) +# output: +# [1, 2, 3, 4] diff --git a/snippets/List/union_by.py b/snippets/List/union_by.py new file mode 100644 index 0000000..3d7b424 --- /dev/null +++ b/snippets/List/union_by.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将提供的函数应用到两个列表中的每个元素之后，一次返回两个列表中任何一个列表中存在的每个元素。 + +解读： +通过对a中的每个元素应用fn创建一个集合。 +在b上结合使用一个列表推导式和fn来只保留先前创建的集合_a中不包含的值。 +最后，从前面的结果和a创建一个集合，并将其转换为一个列表 +""" +from math import floor + + +def union_by(a, b, fn): + _a = set(map(fn, a)) + return list(set(a + [item for item in b if fn(item) not in _a])) + + +# Examples + +print(union_by([2.1], [1.2, 2.3], floor)) +# output: +# [2.1, 1.2] diff --git a/snippets/List/unique_elements.py b/snippets/List/unique_elements.py new file mode 100644 index 0000000..69e3b43 --- /dev/null +++ b/snippets/List/unique_elements.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回给定列表中的唯一元素 + +解读： +从列表中创建一个集合来丢弃重复的值，然后从中返回一个列表。 +""" + + +def unique_elements(li): + return list(set(li)) + + +# Examples + +print(unique_elements([1, 2, 2, 3, 4, 3])) +# output: +# [1, 2, 3, 4] diff --git a/snippets/List/values_only.py b/snippets/List/values_only.py new file mode 100644 index 0000000..703eee8 --- /dev/null +++ b/snippets/List/values_only.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回平面字典中所有值的平面列表。 + +解读： +使用dict.values()返回给定字典中的值。 +返回前一个结果的list()。 +""" + + +def values_only(flat_dict): + return list(flat_dict.values()) + + +# Examples + +ages = { + 'Peter': 10, + 'Isabel': 11, + 'Anna': 9, +} +print(values_only(ages)) +# output: +# [10, 11, 9] diff --git a/snippets/List/weighted_average.py b/snippets/List/weighted_average.py new file mode 100644 index 0000000..8a29b7c --- /dev/null +++ b/snippets/List/weighted_average.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回两个或多个数字的加权平均值。 + +解读： +使用sum()可以按权重对数字乘积求和，然后对权重求和。 +使用zip()和列表推导式来迭代值和权重对 +""" + + +def weighted_average(nums, weights): + return sum(x * y for x, y in zip(nums, weights)) / sum(weights) + + +# Examples + +print(weighted_average([1, 2, 3], [0.6, 0.2, 0.3])) +# output: +# 1.727272727272727 diff --git a/snippets/Math/arithmetic_progression.py b/snippets/Math/arithmetic_progression.py new file mode 100644 index 0000000..ef60417 --- /dev/null +++ b/snippets/Math/arithmetic_progression.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：生成一个等差数列的数字列表，该数列从给定的正整数开始，直到指定的极限。 + +解读： +使用range()和list()与适当的start、step和end值。 +""" + + +def arithmetic_progression(n, lim): + return list(range(n, lim + 1, n)) + + +# Examples + +print(arithmetic_progression(5, 25)) +# output: +# [5, 10, 15, 20, 25] diff --git a/snippets/Math/average.py b/snippets/Math/average.py new file mode 100644 index 0000000..3eda564 --- /dev/null +++ b/snippets/Math/average.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：计算两个或多个数字的平均值。 + +解读： +使用sum()将提供的所有参数相加，除以len()。 +""" + + +def average(*args): + return sum(args, 0.0) / len(args) + + +# Examples + +print(average(*[1, 2, 3])) +print(average(1, 2, 3)) +# output: +# 2.0 +# 2.0 diff --git a/snippets/Math/average_by.py b/snippets/Math/average_by.py new file mode 100644 index 0000000..c9681ae --- /dev/null +++ b/snippets/Math/average_by.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用提供的函数将每个元素映射到一个值后，计算列表的平均值。 + +解读： +使用map()将每个元素映射到fn返回的值。 +使用sum()将所有映射值相加，除以len(lst)。 +省略最后一个实参fn，使用默认的identity函数。 +""" + + +def average_by(lst, fn=lambda x: x): + return sum(map(fn, lst), 0.0) / len(lst) + + +# Examples + +print(average_by([{'n': 4}, {'n': 2}, {'n': 8}, {'n': 6}], lambda x: x['n'])) +# output: +# 5.0 diff --git a/snippets/Math/binomial_coefficient.py b/snippets/Math/binomial_coefficient.py new file mode 100644 index 0000000..7dab89a --- /dev/null +++ b/snippets/Math/binomial_coefficient.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回不重复且无顺序地从 n 项中选择 k 项的方式总数 + +解读： +使用math.comb()计算二项式系数 +注意： +comb是python3.8新版功能. +""" +from math import comb + + +def binomial_coefficient(n, k): + return comb(n, k) + + +# Examples + +print(binomial_coefficient(8, 2)) +# output: +# 28 diff --git a/snippets/Math/clamp_number.py b/snippets/Math/clamp_number.py new file mode 100644 index 0000000..45d8219 --- /dev/null +++ b/snippets/Math/clamp_number.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将num限制在边界值指定的范围之内。 + +解读： +如果num在(a, b)范围内，返回num。 +否则，返回范围内最近的数字。 +""" + + +def clamp_number(num, a, b): + return max(min(num, max(a, b)), min(a, b)) + + +# Examples + +print(clamp_number(2, 3, 5)) +print(clamp_number(1, -1, -5)) +# output: +# 3 +# -1 diff --git a/snippets/Math/degrees_to_rads.py b/snippets/Math/degrees_to_rads.py new file mode 100644 index 0000000..2533670 --- /dev/null +++ b/snippets/Math/degrees_to_rads.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将角度从角度转换为弧度。 + +解读： +使用math.pi和角度到弧度的公式来把角度转换成弧度。 +""" +from math import pi + + +def degrees_to_rads(deg): + return (deg * pi) / 180.0 + + +# Examples + +print(degrees_to_rads(180)) +# output: +# ~3.1416（约等于） diff --git a/snippets/Math/digitize.py b/snippets/Math/digitize.py new file mode 100644 index 0000000..f105e6a --- /dev/null +++ b/snippets/Math/digitize.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将数字转换为数字列表。 + +解读： +对n的字符串表示形式使用map()和int组合，并从结果返回一个列表。 +""" + + +def digitize(n): + return list(map(int, str(n))) + + +# Examples + +print(digitize(123)) +# output: +# [1, 2, 3] diff --git a/snippets/Math/factorial.py b/snippets/Math/factorial.py new file mode 100644 index 0000000..2ae807e --- /dev/null +++ b/snippets/Math/factorial.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：计算数字的阶乘。 + +解读： +使用递归。 +如果num小于等于1，则返回1。 +否则，返回num和num - 1的阶乘的乘积 +如果num是负数或浮点数，则抛出异常。 +""" + + +def factorial(num): + if num < 0 or num % 1 != 0: + raise Exception("Number can't be floating point or negative.") + return 1 if num == 0 else num * factorial(num - 1) + + +# Examples + +print(factorial(6)) +# output: +# 720 diff --git a/snippets/Math/fibonacci.py b/snippets/Math/fibonacci.py new file mode 100644 index 0000000..a8e813d --- /dev/null +++ b/snippets/Math/fibonacci.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：生成一个列表，包含斐波那契序列，直到第n项。 + +解读： +从0和1开始，使用list.append()将列表最后两个数字的总和加到列表的末尾，直到列表的长度达到n。 +如果n小于或等于0，返回一个包含0的列表。 +""" + + +def fibonacci(n): + if n <= 0: + return [0] + sequence = [0, 1] + while len(sequence) <= n: + next_value = sequence[-1] + sequence[len(sequence) - 2] + sequence.append(next_value) + return sequence + + +# Examples + +print(fibonacci(7)) +# output: +# [0, 1, 1, 2, 3, 5, 8, 13] diff --git a/snippets/Math/find_parity_outliers.py b/snippets/Math/find_parity_outliers.py new file mode 100644 index 0000000..8fff262 --- /dev/null +++ b/snippets/Math/find_parity_outliers.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：查找给定列表中奇偶校验异常值的项。 + +解读： +使用集合。使用列表推导式计算列表中的偶数和奇数值。 +使用collections.Counter.most_common()来获得最常见的奇偶校验。 +使用列表推导式查找所有不匹配最常见奇偶校验的元素。 +""" +from collections import Counter + + +def find_parity_outliers(nums): + return [ + x for x in nums + if x % 2 != Counter([n % 2 for n in nums]).most_common()[0][0] + ] + + +# Examples + +print(find_parity_outliers([1, 2, 3, 4, 6])) +# output: +# [1, 3] diff --git a/snippets/Math/gcd.py b/snippets/Math/gcd.py new file mode 100644 index 0000000..38c3159 --- /dev/null +++ b/snippets/Math/gcd.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：计算数字列表的最大公约数。 + +解读： +在给定列表上使用functools.reduce()和math.gcd()。 +""" +from functools import reduce +from math import gcd as _gcd + + +def gcd(numbers): + return reduce(_gcd, numbers) + + +# Examples + +print(gcd([8, 36, 28])) +# output: +# 4 diff --git a/snippets/Math/geometric_progression.py b/snippets/Math/geometric_progression.py new file mode 100644 index 0000000..feb2304 --- /dev/null +++ b/snippets/Math/geometric_progression.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：初始化一个列表，该列表包含指定范围内的数字，其中包括起始和结束，两个术语之间的比率为step。如果step = 1，则返回错误。 + +解读： +使用range()、math.log()和math.floor()以及一个列表推导式来创建一个适当长度的列表，并为每个元素应用step。 +忽略第二个参数start，使用默认值1。 +忽略第三个参数step，使用默认值2。 +""" +from math import floor, log + + +def geometric_progression(end, start=1, step=2): + return [start * step**i for i in range(floor(log(end / start) / log(step)) + 1)] + + +# Examples + +print(geometric_progression(256)) +print(geometric_progression(256, 3)) +print(geometric_progression(256, 1, 4)) +# output: +# [1, 2, 4, 8, 16, 32, 64, 128, 256] +# [3, 6, 12, 24, 48, 96, 192] +# [1, 4, 16, 64, 256] diff --git a/snippets/Math/hamming_distance.py b/snippets/Math/hamming_distance.py new file mode 100644 index 0000000..5849725 --- /dev/null +++ b/snippets/Math/hamming_distance.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：计算两个值之间的汉明距离。 + +解读： +使用异或运算符(^)找出两个数之间的位差。 +使用bin()将结果转换为二进制字符串。 +将字符串转换为列表，并使用str类的count()计数并返回其中的1的数量。 +""" + + +def hamming_distance(a, b): + return bin(a ^ b).count('1') + + +# Examples + +print(hamming_distance(2, 3)) +# output: +# 1 diff --git a/snippets/Math/hex_to_rgb.py b/snippets/Math/hex_to_rgb.py new file mode 100644 index 0000000..f6badbb --- /dev/null +++ b/snippets/Math/hex_to_rgb.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将一个十六进制颜色码转换为一个与RGB分量对应的整数元组。 + +解读： +结合int()和列表切片表示法使用列表推导式从十六进制字符串中获取RGB分量。 +使用tuple()将结果列表转换为元组。 +""" + + +def hex_to_rgb(hex): + return tuple(int(hex[i:i + 2], 16) for i in (0, 2, 4)) + + +# Examples + +print(hex_to_rgb('FFA501')) +# output: +# (255, 165, 1) diff --git a/snippets/Math/in_range.py b/snippets/Math/in_range.py new file mode 100644 index 0000000..bd75940 --- /dev/null +++ b/snippets/Math/in_range.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查给定数字是否在给定范围内。 + +解读： +使用算术比较检查给定的数字是否在指定的范围内。 +如果没有指定第二个参数end，则该范围被认为是从0到start。 +""" + + +def in_range(n, start, end=0): + return start <= n <= end if end >= start else end <= n <= start + + +# Examples + +print(in_range(3, 2, 5)) +print(in_range(3, 4)) +print(in_range(2, 3, 5)) +print(in_range(3, 2)) +# output: +# True +# True +# False +# False diff --git a/snippets/Math/is_prime.py b/snippets/Math/is_prime.py new file mode 100644 index 0000000..fa1278f --- /dev/null +++ b/snippets/Math/is_prime.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查提供的整数是否为质数。 + +解读： +如果数字是0、1、负数或2的倍数则返回False。 +使用all()和range()检查从3到给定数字的平方根的数字。 +如果没有除数则返回True，否则返回False。 +""" +from math import sqrt + + +def is_prime(n): + if n <= 1 or (n % 2 == 0 and n > 2): + return False + return all(n % i for i in range(3, int(sqrt(n)) + 1, 2)) + + +# Examples + +print(is_prime(11)) +# output: +# True diff --git a/snippets/Math/lcm.py b/snippets/Math/lcm.py new file mode 100644 index 0000000..97e5333 --- /dev/null +++ b/snippets/Math/lcm.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回数字列表的最小公倍数。 + +解读： +在给定的列表上使用functools.reduce()， math.gcd()和lcm(x,y) = x * y / gcd(x,y)。 +""" +from functools import reduce +from math import gcd + + +def lcm(numbers): + return reduce((lambda x, y: int(x * y / gcd(x, y))), numbers) + + +# Examples + +print(lcm([12, 7])) +print(lcm([1, 3, 4, 5])) +# output: +# 84 +# 60 diff --git a/snippets/Math/max_by.py b/snippets/Math/max_by.py new file mode 100644 index 0000000..0d07b29 --- /dev/null +++ b/snippets/Math/max_by.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用提供的函数将每个元素映射到一个值后，返回列表的最大值。 + +解读： +使用map()和fn，使用提供的函数将每个元素映射到一个值。 +使用max()返回最大值。 +""" + + +def max_by(lst, fn): + return max(map(fn, lst)) + + +# Examples + +print(max_by([{'n': 4}, {'n': 2}, {'n': 8}, {'n': 6}], lambda v: v['n'])) +# output: +# 8 diff --git a/snippets/Math/max_element_index.py b/snippets/Math/max_element_index.py new file mode 100644 index 0000000..6c910bc --- /dev/null +++ b/snippets/Math/max_element_index.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回列表中具有最大值的元素的索引。 + +解读： +使用max()和list.index()获取列表中的最大值并返回其索引。 +""" + + +def max_element_index(arr): + return arr.index(max(arr)) + + +# Examples + +print(max_element_index([5, 8, 9, 7, 10, 3, 0])) +# output: +# 4 diff --git a/snippets/Math/max_n.py b/snippets/Math/max_n.py new file mode 100644 index 0000000..50243b8 --- /dev/null +++ b/snippets/Math/max_n.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回提供的列表中的n个最大元素。 + +解读： +使用sorted()对列表进行排序。 +使用切片表示法获得指定数量的元素 +省略第二个参数n，得到一个只有一个元素的列表。 +如果n大于或等于提供的列表的长度，则返回原始列表(按降序排序)。 +""" + + +def max_n(lst, n=1): + return sorted(lst, reverse=True)[:n] + + +# Examples + +print(max_n([1, 2, 3])) +print(max_n([1, 2, 3], 2)) +# output: +# [3] +# [3, 2] diff --git a/snippets/Math/median.py b/snippets/Math/median.py new file mode 100644 index 0000000..bc383b8 --- /dev/null +++ b/snippets/Math/median.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：找到一组数字的中位数。 + +解读： +使用list.Sort()对列表中的数字进行排序。 +找到中值，如果列表长度为奇数，它是列表的中间元素;如果列表长度为偶数，它是列表中间两个元素的平均值。 +statistics.median()提供了与此代码片段类似的功能。 +""" + + +def median(list): + list.sort() + list_length = len(list) + if list_length % 2 == 0: + return (list[list_length // 2 - 1] + list[list_length // 2]) / 2 + return float(list[list_length // 2]) + + +# Examples + +print(median([1, 2, 3])) +print(median([1, 2, 3, 4])) +# output: +# 2.0 +# 2.5 diff --git a/snippets/Math/min_by.py b/snippets/Math/min_by.py new file mode 100644 index 0000000..313a97a --- /dev/null +++ b/snippets/Math/min_by.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用提供的函数将每个元素映射到一个值后，返回列表的最小值。 + +解读： +使用map()和fn，使用提供的函数将每个元素映射到一个值。 +使用min()返回最小值。 +""" + + +def min_by(lst, fn): + return min(map(fn, lst)) + + +# Examples + +print(min_by([{'n': 4}, {'n': 2}, {'n': 8}, {'n': 6}], lambda v: v['n'])) +# output: +# 2 diff --git a/snippets/Math/min_element_index.py b/snippets/Math/min_element_index.py new file mode 100644 index 0000000..a83c843 --- /dev/null +++ b/snippets/Math/min_element_index.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回列表中具有最小值的元素的索引。 + +解读： +使用min()和list.index()获取列表中的最小值，然后返回其索引。 +""" + + +def min_element_index(arr): + return arr.index(min(arr)) + + +# Examples + +print(min_element_index([3, 5, 2, 6, 10, 7, 9])) +# output: +# 2 diff --git a/snippets/Math/min_n.py b/snippets/Math/min_n.py new file mode 100644 index 0000000..bee861e --- /dev/null +++ b/snippets/Math/min_n.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：从提供的列表中返回n个最小元素。 + +解读： +使用sorted()对列表进行排序。 +使用切片表示法获得指定数量的元素。 +省略第二个参数n，得到一个只有一个元素的列表。 +如果n大于或等于提供的列表的长度，则返回原始列表(按升序排序)。 +""" + + +def min_n(lst, n=1): + return sorted(lst, reverse=False)[:n] + + +# Examples + +print(min_n([1, 2, 3])) +print(min_n([1, 2, 3], 2)) +# output: +# [1] +# [1, 2] diff --git a/snippets/Math/num_to_range.py b/snippets/Math/num_to_range.py new file mode 100644 index 0000000..026cb2c --- /dev/null +++ b/snippets/Math/num_to_range.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将数字从一个范围映射到另一范围。 + +解读： +从inMin-inMax返回outMin-outMax之间的映射num。 +""" + + +def num_to_range(num, inMin, inMax, outMin, outMax): + return outMin + (float(num - inMin) / float(inMax - inMin) * (outMax - outMin)) + + +# Examples + +print(num_to_range(5, 0, 10, 0, 100)) +# output: +# 50.0 diff --git a/snippets/Math/pad_number.py b/snippets/Math/pad_number.py new file mode 100644 index 0000000..a25ce57 --- /dev/null +++ b/snippets/Math/pad_number.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将给定数字填充到指定长度。 + +解读: +在将数字转换为字符串之后，使用str.zfill()将数字填充为指定的长度。 +""" + + +def pad_number(n, l) -> str: + return str(n).zfill(l) + + +# Examples + +print(pad_number(1234, 6)) +# output: +# 001234 diff --git a/snippets/Math/powerset.py b/snippets/Math/powerset.py new file mode 100644 index 0000000..e982344 --- /dev/null +++ b/snippets/Math/powerset.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回给定迭代器的幂集。 + +解读： +使用list()将给定值转换为list。 +使用range()和itertools.combination()创建一个返回所有子集的生成器。 +使用itertools.chain.from_iterable()和list()来使用生成器并返回一个列表。 +""" +from itertools import chain, combinations + + +def powerset(iterable): + s = list(iterable) + return list(chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))) + + +# Examples + +print(powerset([1, 2])) +# output: +# [(), (1,), (2,), (1, 2)] diff --git a/snippets/Math/rads_to_degrees.py b/snippets/Math/rads_to_degrees.py new file mode 100644 index 0000000..35de7e8 --- /dev/null +++ b/snippets/Math/rads_to_degrees.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将角度从弧度转换为角度。 + +解读： +使用math.pi和弧度到度数的公式将角度从弧度转换成角度。 +""" +from math import pi + + +def rads_to_degrees(rad): + return (rad * 180.0) / pi + + +# Examples + +print(rads_to_degrees(pi / 2)) +# output: +# 90.0 diff --git a/snippets/Math/reverse_number.py b/snippets/Math/reverse_number.py new file mode 100644 index 0000000..f690778 --- /dev/null +++ b/snippets/Math/reverse_number.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：反转数字。 + +解读： +使用str()将数字转换为字符串，使用slice表示法将其颠倒，使用str.replace()删除符号。 +使用float()将结果转换为浮点数字，使用math.copysign()复制原始符号。 +""" +from math import copysign + + +def reverse_number(n) -> float: + return copysign(float(str(n)[::-1].replace('-', '')), n) + + +# Examples + +print(reverse_number(981)) +print(reverse_number(-500)) +print(reverse_number(73.6)) +print(reverse_number(-5.23)) +# output: +# 189.0 +# -5.0 +# 6.37 +# -32.5 diff --git a/snippets/Math/rgb_to_hex.py b/snippets/Math/rgb_to_hex.py new file mode 100644 index 0000000..d33b52e --- /dev/null +++ b/snippets/Math/rgb_to_hex.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将RGB分量的值转换为十六进制颜色码。 + +解读： +使用'{:02X}'为零填充的十六进制值创建一个占位符，并将其复制三次。 +对结果字符串使用str.format()将占位符替换为给定的值。 +""" + + +def rgb_to_hex(r, g, b): + return ('{:02X}' * 3).format(r, g, b) + + +# Examples + +print(rgb_to_hex(255, 165, 1)) +# output: +# FFA501 diff --git a/snippets/Math/sum_by.py b/snippets/Math/sum_by.py new file mode 100644 index 0000000..7ddae67 --- /dev/null +++ b/snippets/Math/sum_by.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：使用提供的函数将每个元素映射到一个值之后，计算列表的总和。 + +解读： +使用map()和fn，使用提供的函数将每个元素映射到一个值。 +使用sum()返回值的和。 +""" + + +def sum_by(lst, fn): + return sum(map(fn, lst)) + + +# Examples + +print(sum_by([{'n': 4}, {'n': 2}, {'n': 8}, {'n': 6}], lambda v: v['n'])) +# output: +# 20 diff --git a/snippets/Math/sum_of_powers.py b/snippets/Math/sum_of_powers.py new file mode 100644 index 0000000..14463e1 --- /dev/null +++ b/snippets/Math/sum_of_powers.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回从开始到结束（包括两端）的所有数字的幂的和。 + +解读： +将range()与列表推导式结合使用，创建一个指定范围内的元素列表 +使用sum()将这些值相加。 +忽略第二个参数power，使用默认的2的幂。 +忽略第三个参数start，使用默认的起始值1。 +""" + + +def sum_of_powers(end, power=2, start=1) -> int: + return sum((i) ** power for i in range(start, end + 1)) + + +# Examples + +print(sum_of_powers(10)) +print(sum_of_powers(10, 3)) +print(sum_of_powers(10, 3, 5)) +# output: +# 385 +# 3025 +# 2925 diff --git a/snippets/Math/to_binary.py b/snippets/Math/to_binary.py new file mode 100644 index 0000000..167e5e4 --- /dev/null +++ b/snippets/Math/to_binary.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回给定数字的二进制表示形式。 + +解读： +使用bin()将给定的十进制数转换为等效的二进制数。 +""" + + +def to_binary(n): + return bin(n) + + +# Examples + +print(to_binary(100)) +# output: +# 0b1100100 diff --git a/snippets/Math/to_hex.py b/snippets/Math/to_hex.py new file mode 100644 index 0000000..36dd7c9 --- /dev/null +++ b/snippets/Math/to_hex.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回给定数字的十六进制表示形式 + +解读： +使用hex()将给定的十进制数转换为其等效的十六进制数 +""" + + +def to_hex(dec): + return hex(dec) + + +# Examples + +print(to_hex(41)) +print(to_hex(332)) +# output: +# 0x29 +# 0x14c diff --git a/snippets/Math/to_roman_numeral.py b/snippets/Math/to_roman_numeral.py new file mode 100644 index 0000000..703befe --- /dev/null +++ b/snippets/Math/to_roman_numeral.py @@ -0,0 +1,44 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将一个整数转换为它的罗马数字表示形式。接受1到3999之间的值(包括两个值)。 + +解读： +创建一个以(罗马值，整数)的形式包含元组的查找列表。 +使用for循环在查找时遍历值。 +使用divmod()用余数更新num，将罗马数字表示形式添加到结果中。 +""" + + +def to_roman_numeral(num): + lookup = [ + (1000, 'M'), + (900, 'CM'), + (500, 'D'), + (400, 'CD'), + (100, 'C'), + (90, 'XC'), + (50, 'L'), + (40, 'XL'), + (10, 'X'), + (9, 'IX'), + (5, 'V'), + (4, 'IV'), + (1, 'I'), + ] + res = '' + for (n, roman) in lookup: + (d, num) = divmod(num, n) + res += roman * d + return res + + +# Examples + +print(to_roman_numeral(3)) +print(to_roman_numeral(11)) +print(to_roman_numeral(1998)) +# output: +# III +# XI +# MCMXCVIII diff --git a/snippets/Math/weighted_average.py b/snippets/Math/weighted_average.py new file mode 100644 index 0000000..8a29b7c --- /dev/null +++ b/snippets/Math/weighted_average.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回两个或多个数字的加权平均值。 + +解读： +使用sum()可以按权重对数字乘积求和，然后对权重求和。 +使用zip()和列表推导式来迭代值和权重对 +""" + + +def weighted_average(nums, weights): + return sum(x * y for x, y in zip(nums, weights)) / sum(weights) + + +# Examples + +print(weighted_average([1, 2, 3], [0.6, 0.2, 0.3])) +# output: +# 1.727272727272727 diff --git a/snippets/String/byte_size.py b/snippets/String/byte_size.py new file mode 100644 index 0000000..fab9044 --- /dev/null +++ b/snippets/String/byte_size.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：返回字符串的长度（以字节为单位）。 + +解读: +使用str.encode('utf-8')对给定的字符串进行编码并返回其长度。 +""" + + +def byte_size(s): + return len(s.encode('utf-8')) + + +# Examples + +print(byte_size('😀')) +print(byte_size('Hello World')) +# output: +# 4 +# 11 diff --git a/snippets/String/camel.py b/snippets/String/camel.py new file mode 100644 index 0000000..1aa16fb --- /dev/null +++ b/snippets/String/camel.py @@ -0,0 +1,29 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将一个字符串转换为驼峰大小写。 + +解读： +使用re.sub()将任何-或_替换为一个空格，使用regexp r"(_|-)+"。 +使用str.title()将每个单词的第一个字母大写，并将其余的转换为小写。 +最后，使用str.replace()删除单词之间的空格。 +""" +from re import sub + + +def camel(s): + s = sub(r"(_|-)+", " ", s).title().replace(" ", "") + return ''.join([s[0].lower(), s[1:]]) + + +# Examples + +print(camel('some_database_field_name')) +print(camel('Some label that needs to be camelized')) +print(camel('some-javascript-property')) +print(camel('some-mixed_string with spaces_underscores-and-hyphens')) +# output: +# someDatabaseFieldName +# someLabelThatNeedsToBeCamelized +# someJavascriptProperty +# someMixedStringWithSpacesUnderscoresAndHyphens diff --git a/snippets/String/capitalize.py b/snippets/String/capitalize.py new file mode 100644 index 0000000..8933253 --- /dev/null +++ b/snippets/String/capitalize.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将字符串的第一个字母大写。 + +解读： +使用列表切片和str.upper()将字符串的第一个字母大写。 +使用str.join()将大写的第一个字母与其他字符组合起来。 +省略lower_rest参数以保持字符串的其余部分完整，或将其设置为True以转换为小写。 +""" + + +def capitalize(s, lower_rest=False): + return ''.join([s[:1].upper(), (s[1:].lower() if lower_rest else s[1:])]) + + +# Examples + +print(capitalize('fooBar')) +print(capitalize('fooBar', True)) +# output: +# FooBar +# Foobar diff --git a/snippets/String/capitalize_every_word.py b/snippets/String/capitalize_every_word.py new file mode 100644 index 0000000..e21d97a --- /dev/null +++ b/snippets/String/capitalize_every_word.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将字符串中每个单词的首字母大写。 + +解读: +使用str.title()将字符串中每个单词的第一个字母大写。 +""" + + +def capitalize_every_word(s) -> str: + return s.title() + + +# Examples + +print(capitalize_every_word('hello world!')) +# output: +# 'Hello World!' diff --git a/snippets/String/decapitalize.py b/snippets/String/decapitalize.py new file mode 100644 index 0000000..0759192 --- /dev/null +++ b/snippets/String/decapitalize.py @@ -0,0 +1,23 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将字符串的首字母大写。 + +解读： +使用列表切片和str.lower()对字符串的第一个字母大写。 +使用str.join()将小写的第一个字母与其余字符组合在一起。 +省略upper_rest参数以保持字符串的其余部分不变，或将其设置为True以转换为大写字母。 +""" + + +def decapitalize(s, upper_rest=False): + return ''.join([s[:1].lower(), (s[1:].upper() if upper_rest else s[1:])]) + + +# Examples + +print(decapitalize('FooBar')) +print(decapitalize('FooBar', True)) +# output: +# fooBar +# fOOBAR diff --git a/snippets/String/hex_to_rgb.py b/snippets/String/hex_to_rgb.py new file mode 100644 index 0000000..f6badbb --- /dev/null +++ b/snippets/String/hex_to_rgb.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将一个十六进制颜色码转换为一个与RGB分量对应的整数元组。 + +解读： +结合int()和列表切片表示法使用列表推导式从十六进制字符串中获取RGB分量。 +使用tuple()将结果列表转换为元组。 +""" + + +def hex_to_rgb(hex): + return tuple(int(hex[i:i + 2], 16) for i in (0, 2, 4)) + + +# Examples + +print(hex_to_rgb('FFA501')) +# output: +# (255, 165, 1) diff --git a/snippets/String/is_anagram.py b/snippets/String/is_anagram.py new file mode 100644 index 0000000..354105b --- /dev/null +++ b/snippets/String/is_anagram.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查一个字符串是否是另一个字符串的变形词(不区分大小写，忽略空格、标点和特殊字符)。 + +解读: +使用str.isalnum()过滤出非字母数字字符，使用str.lower()将每个字符转换为小写字母。 +使用集合。计数器计算每个字符串的结果字符并比较结果。 +""" +from collections import Counter + + +def is_anagram(s1, s2): + return Counter( + c.lower() for c in s1 if c.isalnum() + ) == Counter( + c.lower() for c in s2 if c.isalnum() + ) + + +# Examples + +print(is_anagram('#anagram', 'Nag a ram!')) +# output: +# True diff --git a/snippets/String/kebab.py b/snippets/String/kebab.py new file mode 100644 index 0000000..3218b66 --- /dev/null +++ b/snippets/String/kebab.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将一个字符串转换为烤肉串大小写。 + +解读： +使用re.sub()将任何-或_替换为一个空格，使用regexp r"(_|-)+"。 +使用re.sub()匹配字符串中的所有单词，str.lower()将它们小写。 +最后，使用str.join()以-作为分隔符组合所有单词。 +""" +from re import sub + + +def kebab(s): + return '-'.join( + sub( + r"(\s|_|-)+", + " ", + sub( + r"[A-Z]{2,}(?=[A-Z][a-z]+[0-9]*|\b)|[A-Z]?[a-z]+[0-9]*|[A-Z]|[0-9]+", + lambda mo: f' {mo.group(0).lower()}', + s, + ), + ).split() + ) + + +# Examples + +print(kebab('camelCase')) +print(kebab('some text')) +print(kebab('some-mixed_string With spaces_underscores-and-hyphens')) +print(kebab('AllThe-small Things')) +# output: +# camel-case +# some-text +# some-mixed-string-with-spaces-underscores-and-hyphens +# all-the-small-things diff --git a/snippets/String/longest_item.py b/snippets/String/longest_item.py new file mode 100644 index 0000000..5afd3bc --- /dev/null +++ b/snippets/String/longest_item.py @@ -0,0 +1,24 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：接受任意数量的可迭代对象或带有length属性的对象，并返回最长的那个。 + +解读： +使用max()和len()作为键来返回最大长度的项。 +如果多个对象具有相同的长度，则返回第一个对象。 +""" + + +def longest_item(*args): + return max(args, key=len) + + +# Examples + +print(longest_item('this', 'is', 'a', 'testcase')) +print(longest_item([1, 2, 3], [1, 2], [1, 2, 3, 4, 5])) +print(longest_item([1, 2, 3], 'foobar')) +# output: +# testcase +# [1, 2, 3, 4, 5] +# foobar diff --git a/snippets/String/n_times_string.py b/snippets/String/n_times_string.py new file mode 100644 index 0000000..eb17146 --- /dev/null +++ b/snippets/String/n_times_string.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：生成一个字符串，该字符串重复n次给定的字符串值。 + +解读: +使用*操作符重复字符串n次。 +""" + + +def n_times_string(s, n) -> str: + return (s * n) + + +# Examples + +print(n_times_string('py', 4)) +# output: +# 'pypypypy' diff --git a/snippets/String/pad.py b/snippets/String/pad.py new file mode 100644 index 0000000..03b7a53 --- /dev/null +++ b/snippets/String/pad.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：如果指定的字符比指定的长度短，则在字符串的两边填充指定的字符。 + +解读: +使用str.ljust()和str.rjust()来填充给定字符串的两边。 +省略第三个参数char，以使用空白字符作为默认填充字符。 +""" +from math import floor + + +def pad(s, length, char=' ')->str: + return s.rjust(floor((len(s) + length) / 2), char).ljust(length, char) + + +# Examples + +print(repr(pad('cat', 8))) +print(repr(pad('42', 6, '0'))) +print(repr(pad('foobar', 3))) +# output: +# ' cat ' +# '004200' +# 'foobar' diff --git a/snippets/String/pad_number.py b/snippets/String/pad_number.py new file mode 100644 index 0000000..a25ce57 --- /dev/null +++ b/snippets/String/pad_number.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将给定数字填充到指定长度。 + +解读: +在将数字转换为字符串之后，使用str.zfill()将数字填充为指定的长度。 +""" + + +def pad_number(n, l) -> str: + return str(n).zfill(l) + + +# Examples + +print(pad_number(1234, 6)) +# output: +# 001234 diff --git a/snippets/String/palindrome.py b/snippets/String/palindrome.py new file mode 100644 index 0000000..a97a849 --- /dev/null +++ b/snippets/String/palindrome.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：检查给定的字符串是否是回文。 + +解读: +使用str.lower()和re.sub()将其转换为小写，并从给定的字符串中删除非字母数字字符。 +然后，使用切片表示法将新字符串与其相反的字符串进行比较。 +""" +from re import sub + + +def palindrome(s) -> bool: + s = sub('[\W_]', '', s.lower()) + return s == s[::-1] + + +# Examples + +print(palindrome('taco cat')) +# output: +# True diff --git a/snippets/String/reverse.py b/snippets/String/reverse.py new file mode 100644 index 0000000..748c16b --- /dev/null +++ b/snippets/String/reverse.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：反转列表或字符串。 + +解读: +使用切片表示法反转列表或字符串。 +""" + + +def reverse(itr): + return itr[::-1] + + +# Examples + +print(reverse([1, 2, 3])) +print(reverse('snippet')) +# output: +# [3, 2, 1] +# teppins diff --git a/snippets/String/rgb_to_hex.py b/snippets/String/rgb_to_hex.py new file mode 100644 index 0000000..d33b52e --- /dev/null +++ b/snippets/String/rgb_to_hex.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将RGB分量的值转换为十六进制颜色码。 + +解读： +使用'{:02X}'为零填充的十六进制值创建一个占位符，并将其复制三次。 +对结果字符串使用str.format()将占位符替换为给定的值。 +""" + + +def rgb_to_hex(r, g, b): + return ('{:02X}' * 3).format(r, g, b) + + +# Examples + +print(rgb_to_hex(255, 165, 1)) +# output: +# FFA501 diff --git a/snippets/String/slugify.py b/snippets/String/slugify.py new file mode 100644 index 0000000..313591b --- /dev/null +++ b/snippets/String/slugify.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将字符串转换为url友好的字符串。 + +解读: +使用str.lower()和str.strip()来规范化输入字符串。 +使用re.sub()来替换空格、破折号和下划线，并删除特殊字符。 +""" +import re + + +def slugify(s): + s = s.lower().strip() + s = re.sub(r'[^\w\s-]', '', s) + s = re.sub(r'[\s_-]+', '-', s) + s = re.sub(r'^-+|-+$', '', s) + return s + + +# Examples + +print(slugify('Hello World!')) +# output: +# hello-world diff --git a/snippets/String/snake.py b/snippets/String/snake.py new file mode 100644 index 0000000..a670baf --- /dev/null +++ b/snippets/String/snake.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将字符串转换为snake case。 + +解读： +使用re.sub()匹配字符串中的所有单词，str.lower()将它们小写。 +使用re.sub()将任意-字符替换为空格。 +最后，使用str.join()以-作为分隔符组合所有单词。 +""" +from re import sub + + +def snake(s): + return '_'.join( + sub('([A-Z][a-z]+)', r' \1', + sub('([A-Z]+)', r' \1', + s.replace('-', ' '))).split()).lower() + + +# Examples + +print(snake('camelCase')) +print(snake('some text')) +print(snake('some-mixed_string With spaces_underscores-and-hyphens')) +print(snake('AllThe-small Things')) +# output: +# camel_case +# some_text +# some_mixed_string_with_spaces_underscores_and_hyphens +# all_the_small_things diff --git a/snippets/String/split_lines.py b/snippets/String/split_lines.py new file mode 100644 index 0000000..31a296a --- /dev/null +++ b/snippets/String/split_lines.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将多行字符串拆分为行列表。 + +解读: +使用str.split()和'\n'来匹配换行符并创建列表。 +str.splitlines()提供了与此代码片段类似的功能。 +""" + + +def split_lines(s): + return s.split('\n') + + +# Examples + +print(split_lines('This\nis a\nmultiline\nstring.\n')) +# output: +# ['This', 'is a', 'multiline', 'string.' , ''] diff --git a/snippets/String/to_roman_numeral.py b/snippets/String/to_roman_numeral.py new file mode 100644 index 0000000..703befe --- /dev/null +++ b/snippets/String/to_roman_numeral.py @@ -0,0 +1,44 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将一个整数转换为它的罗马数字表示形式。接受1到3999之间的值(包括两个值)。 + +解读： +创建一个以(罗马值，整数)的形式包含元组的查找列表。 +使用for循环在查找时遍历值。 +使用divmod()用余数更新num，将罗马数字表示形式添加到结果中。 +""" + + +def to_roman_numeral(num): + lookup = [ + (1000, 'M'), + (900, 'CM'), + (500, 'D'), + (400, 'CD'), + (100, 'C'), + (90, 'XC'), + (50, 'L'), + (40, 'XL'), + (10, 'X'), + (9, 'IX'), + (5, 'V'), + (4, 'IV'), + (1, 'I'), + ] + res = '' + for (n, roman) in lookup: + (d, num) = divmod(num, n) + res += roman * d + return res + + +# Examples + +print(to_roman_numeral(3)) +print(to_roman_numeral(11)) +print(to_roman_numeral(1998)) +# output: +# III +# XI +# MCMXCVIII diff --git a/snippets/String/words.py b/snippets/String/words.py new file mode 100644 index 0000000..ddf7e06 --- /dev/null +++ b/snippets/String/words.py @@ -0,0 +1,25 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +功能实现：将给定的字符串转换为单词列表。 + +解读： +使用re.findall()与提供的模式一起查找所有匹配的子字符串。 +省略第二个参数以使用默认regexp，它匹配字母数字和连字符。 +""" +import re + + +def words(s, pattern='[a-zA-Z-]+'): + return re.findall(pattern, s) + + +# Examples + +print(words('I love Python!!')) +print(words('python, javaScript & coffee')) +print(words('build -q --out one-item', r'\b[a-zA-Z-]+\b')) +# output: +# ['I', 'love', 'Python'] +# ['python', 'javaScript', 'coffee'] +# ['build', 'q', 'out', 'one-item'] diff --git a/socket_programming/event_loop_select.py b/socket_programming/event_loop_select.py index 022521e..58f1154 100644 --- a/socket_programming/event_loop_select.py +++ b/socket_programming/event_loop_select.py @@ -6,4 +6,4 @@ s = socket.socket() s.connect(('localhost', 8888)) while True: - msg = + msg =s.recv(1024) diff --git a/socket_programming/get_host_ip.py b/socket_programming/get_host_ip.py new file mode 100644 index 0000000..d05d8c8 --- /dev/null +++ b/socket_programming/get_host_ip.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import socket + + +def get_host_ip(): + """ + 查询本机ip地址 + :return: ip + """ + s = None + try: + s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) + s.connect(('8.8.8.8', 80)) + ip = s.getsockname()[0] + finally: + s.close() + return ip + + +if __name__ == '__main__': + print(get_host_ip()) diff --git a/ssh/ssh_connection.py b/ssh/ssh_connection.py new file mode 100644 index 0000000..4c01945 --- /dev/null +++ b/ssh/ssh_connection.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import paramiko + + +class SSHConnection: + """ + 对paramiko进行封装，实现远程命令执行和文件上传下载 + """ + + def __init__(self, host='192.168.12.68', port=22, username='root', pwd='123456'): + self.host = host + self.port = port + self.username = username + self.pwd = pwd + self.__k = None + self.__transport = self.connect() + + def connect(self): + """ + 连接Linux服务器 + :return: transport对象 + """ + transport = paramiko.Transport((self.host, self.port)) + transport.connect(username=self.username, password=self.pwd) + return transport + + def upload(self, local_path, target_path): + """ + 上传本地文件到服务器上 + :param local_path:本地计算机上的文件路径 + :param target_path:远程服务器上的文件路径 + :return:无 + """ + sftp = paramiko.SFTPClient.from_transport(self.__transport) + sftp.put(local_path, target_path) + + def download(self, remote_path, local_path): + """ + 将服务器上的文件下载到本地 + :param remote_path:远程服务器上的文件路径 + :param local_path:本地计算机上的文件路径 + :return: 无 + """ + sftp = paramiko.SFTPClient.from_transport(self.__transport) + sftp.get(remote_path, local_path) + + def cmd(self, command): + """ + 在服务器上执行shell命令 + :param command:要执行的命令 + :return:执行命令后的返回结果 + """ + ssh = paramiko.SSHClient() + ssh._transport = self.__transport + # 执行命令 + stdin, stdout, stderr = ssh.exec_command(command) + # 获取命令结果 + result = stdout.read().decode("utf-8") + print(result) + return result + + def close(self): + """ + 关闭服务器连接 + :return: 无 + """ + self.__transport.close() + + +def main(): + ssh = SSHConnection(host="192.168.56.136", port=22, username="root", pwd="123456") + ssh.cmd('ls -lah;cd /home/python/Desktop/prj/run.sh') # 执行ls -lah命令,并执行run.sh脚本 + ssh.upload(r'C:\Users\liming\Desktop\python_projects\program\test\test.py', '/home/python/Desktop/1.py') # 将本地的test.py文件上传到远端服务器的/home/python/Desktop目录下并改名为1.py + ssh.download('/home/python/Desktop/1.py', 'testdownload.py') # 将远端服务器的/home/python/Desktop目录下的1.p下载到本地的test目录下并改名为test.py + ssh.close() # 关闭连接 + + +if __name__ == '__main__': + main() diff --git a/subprocess/exe_command.py b/subprocess/exe_command.py new file mode 100644 index 0000000..d904b60 --- /dev/null +++ b/subprocess/exe_command.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import subprocess +from threading import Timer + + +def exe_command(cmd, timeout=300): + # https://stackoverflow.com/questions/1191374/using-module-subprocess-with-timeout + process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) + timer = Timer(timeout, process.kill) + try: + timer.start() + stdout, stderr = process.communicate() + shellresult = stdout.decode('utf-8', errors='ignore') + retcode = process.poll() + return (retcode, shellresult) + finally: + timer.cancel() + + +if __name__ == '__main__': + print(exe_command('ifconfig')) diff --git a/tar/make_tar_file.py b/tar/make_tar_file.py new file mode 100644 index 0000000..ecdd820 --- /dev/null +++ b/tar/make_tar_file.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# @Time : 2022/2/27 17:59 +# @Author : 一叶知秋 +# @File : make_tar_file.py +# @Software: PyCharm +from __future__ import division, print_function +import os +import sys +import tarfile +import logging + +if sys.version_info[0] == 2: + input = raw_input +else: + input = input +formatter = '%(asctime)s %(levelname)s %(filename)s[%(lineno)d]: %(message)s' +logging.basicConfig(level=logging.DEBUG, format=formatter, + datefmt='%Y:%m:%d %H:%M:%S', encoding='utf-8') + + +def make_tar_file(filename, filepath): + """ + make tar file + :param filename: tar file name + :param filepath: source file directory + :return: + """ + with tarfile.open(filename, mode='w:gz') as tar: + for root, dirs, files in os.walk(filepath): + for file in files: + fullpath = os.path.join(root, file) + logging.info('file name: %s', fullpath) + arcname = fullpath.replace(filepath, '') + tar.add(fullpath, arcname) + + +def main(): + """do main""" + sourceFolder = input('请输入源文件路径目录：\n') + filename = input('请输入压缩文件名字： \n') + logging.info('source file dir: %s', sourceFolder) + logging.info('file name: %s', filename) + make_tar_file(filename, sourceFolder) + + +if __name__ == '__main__': + main() diff --git a/temp_file/tempfile_demo.py b/temp_file/tempfile_demo.py new file mode 100644 index 0000000..4f9b9d9 --- /dev/null +++ b/temp_file/tempfile_demo.py @@ -0,0 +1,44 @@ +import linecache +import os +import tempfile + +lorem = '''Lorem ipsum dolor sit amet, consectetuer +adipiscing elit. Vivamus eget elit. In posuere mi non +risus. Mauris id quam posuere lectus sollicitudin +varius. Praesent at mi. Nunc eu velit. Sed augue massa, +fermentum id, nonummy a, nonummy sit amet, ligula. Curabitur +eros pede, egestas at, ultricies ac, apellentesque eu, +tellus. + +Sed sed odio sed mi luctus mollis. Integer et nulla ac augue +convallis accumsan. Ut felis. Donec lectus sapien, elementum +nec, condimentum ac, interdum non, tellus. Aenean viverra, +mauris vehicula semper porttitor, ipsum odio consectetuer +lorem, ac imperdiet eros odio a sapien. Nulla mauris tellus, +aliquam non, egestas a, nonummy et, erat. Vivamus sagittis +porttitor eros.''' + + +def make_tempfile(): + fd, temp_file_name = tempfile.mkstemp() + os.close(fd) + with open(temp_file_name,'wt') as file: + file.write(lorem) + return temp_file_name + + +def cleanup(filename): + os.unlink(filename) + + +filename = make_tempfile() +print(filename) +# 从源文件和缓存中挑出相同行 +# (注意，linecache 从1开始，数组从0开始) +print('SOURCE:') +print('{!r}'.format(lorem.split('\n')[4])) +print() +print('CACHE:') +print('{!r}'.format(linecache.getline(filename, 5))) + +cleanup(filename) diff --git a/text_html/dos2unix.py b/text_html/dos2unix.py index 7486f3d..3558514 100644 --- a/text_html/dos2unix.py +++ b/text_html/dos2unix.py @@ -10,8 +10,8 @@ # # - Check that it works (as I had the impression it didn't work all the time). -from string import join -from string import split +# from string import join +# from string import split import getopt import os import re @@ -21,25 +21,25 @@ def dos2unix(filename): import sys - text = open(filename, 'rb').read().replace('\r\n', '\n') + text = open(filename, 'r').read().replace('\r\n', '\n') open(filename, 'wb').write(text) def dos2unix(data): - return join(split(data, '\r\n'), '\n') + return '\n'.join(data.split('\r\n') ) def unix2dos(data): - return join(split(dos2unix(data), '\n'), '\r\n') + return '\r\n'.join(dos2unix(data).split( '\n')) def confirm(file_): - s = raw_input('%s? ' % file_) + s = input('%s? ' % file_) return s and s[0] == 'y' def usage(): - print """\ + print ("""\ USAGE dos2unix.py [-iuvnfcd] [-b extension] file {file} DESCRIPTION @@ -55,7 +55,7 @@ def usage(): -b ext use 'ext' as backup extension (default .bak) -c don't make a backup -d keep modification date and mode -""" +""") sys.exit() @@ -102,7 +102,7 @@ def main(): newdata = convert(data) if newdata != data: if verbose and not interactive: - print file_ + print (file_) if not interactive or confirm(file_): if not noaction: newfile = file_+'.@' diff --git a/text_html/encoding_decoding_tool.py b/text_html/encoding_decoding_tool.py index 919ddd1..a651443 100644 --- a/text_html/encoding_decoding_tool.py +++ b/text_html/encoding_decoding_tool.py @@ -17,7 +17,7 @@ def convert_encoding(data, new_coding='UTF-8'): """未知编码转成utf8""" encoding = chardet.detect(data)['encoding'] if new_coding.upper() != encoding.upper(): - data = data.decode(encoding, data).encode(new_coding) + data = data.decode(encoding).encode(new_coding) return data @@ -32,7 +32,7 @@ def detect_html_encoding(url): if __name__ == '__main__': - print detect_html_encoding('http://www.baidu.com') - convert_encoding('hehe', new_coding='UTF-8') - to_unicode('hehe') - print get_encoding('hehe') + print(detect_html_encoding('http://www.baidu.com')) + convert_encoding('hehe'.encode('utf-8'), new_coding='UTF-8') + print(to_unicode('hehe'.encode('utf-8'))) + print(get_encoding('hehe'.encode('utf-8'))) diff --git a/text_html/hash_tools.py b/text_html/hash_tools.py index 53b7621..5e0cb41 100755 --- a/text_html/hash_tools.py +++ b/text_html/hash_tools.py @@ -173,7 +173,8 @@ def append(self, buffer): 0xffffffffffffffff) def fini(self): - return self.crc ^0L + # https://stackoverflow.com/questions/9549226/small-python-syntax-error + return self.crc ^ 0 def crc64(buffer): @@ -185,4 +186,4 @@ def crc64(buffer): if __name__ == "__main__": # print(file_md5('./common.txt')) - print(crc64(open('t.py').read())) + print(crc64(open('t.py',encoding='utf-8').read())) diff --git a/text_html/html2text_tool.py b/text_html/html2text_tool.py index e8e279d..47efbec 100644 --- a/text_html/html2text_tool.py +++ b/text_html/html2text_tool.py @@ -5,9 +5,9 @@ from bs4 import BeautifulSoup def html2txt(html=u''): - print html + print(html) soup = BeautifulSoup(html) - print soup.get_text() + print(soup.get_text()) import html2text # to markdown not plain text @@ -31,7 +31,7 @@ def test(): html = requests.get('http://codingpy.com/article/top-10-mistakes-that-python-programmers-make/').text soup = BeautifulSoup(html) content = soup.find(class_='article-content') - print(html2makrdown(unicode(content))) + print(html2makrdown(content)) if __name__ == '__main__': diff --git a/text_html/t.py b/text_html/t.py index 1a78cb4..1d51797 100644 --- a/text_html/t.py +++ b/text_html/t.py @@ -18,7 +18,7 @@ def convert_encoding(data, new_coding='UTF-8'): """未知编码转成utf8""" encoding = cchardet.detect(data)['encoding'] if new_coding.upper() != encoding.upper(): - data = data.decode(encoding, data).encode(new_coding) + data = data.decode(encoding).encode(new_coding) return data @@ -28,7 +28,7 @@ def to_unicode(unknown_bytes): def detect_html_encoding(url): - r = requests.get(url).content + data = requests.get(url).content return cchardet.detect(data)['encoding'] diff --git a/text_html/to_utf8.py b/text_html/to_utf8.py index a86bf68..e4b86a2 100644 --- a/text_html/to_utf8.py +++ b/text_html/to_utf8.py @@ -32,7 +32,7 @@ def to_utf8(filename): lines[i] = line.replace('charset=gbk', 'charset=utf-8') txt = '\n'.join(lines) - content = txt.decode('gb18030').encode('utf-8') + content = txt.encode('gb18030').decode('utf-8') filename += 'l' with open(filename, 'w', encoding='utf-8') as f: f.write(content) diff --git a/thread/multi_process.py b/threads/multi_process.py similarity index 59% rename from thread/multi_process.py rename to threads/multi_process.py index bd163e9..db1aa2a 100644 --- a/thread/multi_process.py +++ b/threads/multi_process.py @@ -5,21 +5,23 @@ import os, time, random def long_time_task(name): - print 'Run task %s (%s)...' % (name, os.getpid()) + print('Run task %s (%s)...' % (name, os.getpid())) start = time.time() time.sleep(random.random() * 3) end = time.time() - print 'Task %s runs %0.2f seconds.' % (name, (end - start)) + print('Task %s runs %0.2f seconds.' % (name, (end - start))) + def main(): - print 'Parent process %s.' % os.getpid() + print('Parent process %s.' % os.getpid()) p = Pool() for i in range(5): p.apply_async(long_time_task, args=(i,)) - print 'Waiting for all subprocesses done...' + print('Waiting for all subprocesses done...') p.close() p.join() - print 'All subprocesses done.' + print('All subprocesses done.') + if __name__ == '__main__': main() diff --git a/thread/t.py b/threads/t.py similarity index 100% rename from thread/t.py rename to threads/t.py diff --git a/thread/thread_pool.py b/threads/thread_pool.py similarity index 100% rename from thread/thread_pool.py rename to threads/thread_pool.py diff --git a/thread/xpath_utils.py b/threads/xpath_utils.py similarity index 100% rename from thread/xpath_utils.py rename to threads/xpath_utils.py diff --git a/tkinter_demo/Canvas.py b/tkinter_demo/Canvas.py new file mode 100644 index 0000000..c15c23f --- /dev/null +++ b/tkinter_demo/Canvas.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk # 使用Tkinter前需要先导入 + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('My Window') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('500x300') # 这里的乘是小x + +# 第4步，在图形界面上创建 500 * 200 大小的画布并放置各种元素 +canvas = tk.Canvas(window, bg='green', height=200, width=500) +# 说明图片位置，并导入图片到画布上 +image_file = tk.PhotoImage(file='pic.gif') # 图片位置（相对路径，与.py文件同一文件夹下，也可以用绝对路径，需要给定图片具体绝对路径） +image = canvas.create_image(250, 0, anchor='n', image=image_file) # 图片锚定点（n图片顶端的中间点位置）放在画布（250,0）坐标处 +# 定义多边形参数，然后在画布上画出指定图形 +x0, y0, x1, y1 = 100, 100, 150, 150 +line = canvas.create_line(x0 - 50, y0 - 50, x1 - 50, y1 - 50) # 画直线 +oval = canvas.create_oval(x0 + 120, y0 + 50, x1 + 120, y1 + 50, fill='yellow') # 画圆 用黄色填充 +arc = canvas.create_arc(x0, y0 + 50, x1, y1 + 50, start=0, extent=180) # 画扇形 从0度打开收到180度结束 +rect = canvas.create_rectangle(330, 30, 330 + 20, 30 + 20) # 画矩形正方形 +canvas.pack() + + +# 第6步，触发函数，用来一定指定图形 +def moveit(): + canvas.move(rect, 2, 2) # 移动正方形rect（也可以改成其他图形名字用以移动一起图形、元素），按每次（x=2, y=2）步长进行移动 + + +# 第5步，定义一个按钮用来移动指定图形的在画布上的位置 +b = tk.Button(window, text='move item', command=moveit).pack() + +# 第7步，主窗口循环显示 +window.mainloop() diff --git a/tkinter_demo/Frame.py b/tkinter_demo/Frame.py new file mode 100644 index 0000000..d0f8bcf --- /dev/null +++ b/tkinter_demo/Frame.py @@ -0,0 +1,36 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk # 使用Tkinter前需要先导入 + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('My Window') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('500x300') # 这里的乘是小x + +# 第4步，在图形界面上创建一个标签用以显示内容并放置 +tk.Label(window, text='on the window', bg='red', font=('Arial', 16)).pack() # 和前面部件分开创建和放置不同，其实可以创建和放置一步完成 + +# 第5步，创建一个主frame，长在主window窗口上 +frame = tk.Frame(window) +frame.pack() + +# 第6步，创建第二层框架frame，长在主框架frame上面 +frame_l = tk.Frame(frame) # 第二层frame，左frame，长在主frame上 +frame_r = tk.Frame(frame) # 第二层frame，右frame，长在主frame上 +frame_l.pack(side='left') +frame_r.pack(side='right') + +# 第7步，创建三组标签，为第二层frame上面的内容，分为左区域和右区域，用不同颜色标识 +tk.Label(frame_l, text='on the frame_l1', bg='green').pack() +tk.Label(frame_l, text='on the frame_l2', bg='green').pack() +tk.Label(frame_l, text='on the frame_l3', bg='green').pack() +tk.Label(frame_r, text='on the frame_r1', bg='yellow').pack() +tk.Label(frame_r, text='on the frame_r2', bg='yellow').pack() +tk.Label(frame_r, text='on the frame_r3', bg='yellow').pack() + +# 第8步，主窗口循环显示 +window.mainloop() diff --git a/tkinter_demo/Menu.py b/tkinter_demo/Menu.py new file mode 100644 index 0000000..6fe229e --- /dev/null +++ b/tkinter_demo/Menu.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk # 使用Tkinter前需要先导入 + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('My Window') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('500x300') # 这里的乘是小x + +# 第4步，在图形界面上创建一个标签用以显示内容并放置 +l = tk.Label(window, text=' ', bg='green') +l.pack() + +# 第10步，定义一个函数功能，用来代表菜单选项的功能，这里为了操作简单，定义的功能比较简单 +counter = 0 + + +def do_job(): + global counter + l.config(text='do ' + str(counter)) + counter += 1 + + +# 第5步，创建一个菜单栏，这里我们可以把他理解成一个容器，在窗口的上方 +menubar = tk.Menu(window) + +# 第6步，创建一个File菜单项（默认不下拉，下拉内容包括New，Open，Save，Exit功能项） +filemenu = tk.Menu(menubar, tearoff=0) +# 将上面定义的空菜单命名为File，放在菜单栏中，就是装入那个容器中 +menubar.add_cascade(label='File', menu=filemenu) + +# 在File中加入New、Open、Save等小菜单，即我们平时看到的下拉菜单，每一个小菜单对应命令操作。 +filemenu.add_command(label='New', command=do_job) +filemenu.add_command(label='Open', command=do_job) +filemenu.add_command(label='Save', command=do_job) +filemenu.add_separator() # 添加一条分隔线 +filemenu.add_command(label='Exit', command=window.quit) # 用tkinter里面自带的quit()函数 + +# 第7步，创建一个Edit菜单项（默认不下拉，下拉内容包括Cut，Copy，Paste功能项） +editmenu = tk.Menu(menubar, tearoff=0) +# 将上面定义的空菜单命名为 Edit，放在菜单栏中，就是装入那个容器中 +menubar.add_cascade(label='Edit', menu=editmenu) + +# 同样的在 Edit 中加入Cut、Copy、Paste等小命令功能单元，如果点击这些单元, 就会触发do_job的功能 +editmenu.add_command(label='Cut', command=do_job) +editmenu.add_command(label='Copy', command=do_job) +editmenu.add_command(label='Paste', command=do_job) + +# 第8步，创建第二级菜单，即菜单项里面的菜单 +submenu = tk.Menu(filemenu) # 和上面定义菜单一样，不过此处实在File上创建一个空的菜单 +filemenu.add_cascade(label='Import', menu=submenu, underline=0) # 给放入的菜单submenu命名为Import + +# 第9步，创建第三级菜单命令，即菜单项里面的菜单项里面的菜单命令（有点拗口，笑~~~） +submenu.add_command(label='Submenu_1', command=do_job) # 这里和上面创建原理也一样，在Import菜单项中加入一个小菜单命令Submenu_1 + +# 第11步，创建菜单栏完成后，配置让菜单栏menubar显示出来 +window.config(menu=menubar) + +# 第12步，主窗口循环显示 +window.mainloop() diff --git a/tkinter_demo/Scale.py b/tkinter_demo/Scale.py new file mode 100644 index 0000000..e2df041 --- /dev/null +++ b/tkinter_demo/Scale.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk # 使用Tkinter前需要先导入 + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('My Window') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('500x300') # 这里的乘是小x + +# 第4步，在图形界面上创建一个标签label用以显示并放置 +l = tk.Label(window, bg='green', fg='white', width=20, text='empty') +l.pack() + + +# 第6步，定义一个触发函数功能 +def print_selection(v): + l.config(text='you have selected ' + v) + + +# 第5步，创建一个尺度滑条，长度200字符，从0开始10结束，以2为刻度，精度为0.01，触发调用print_selection函数 +s = tk.Scale(window, label='try me', from_=0, to=10, orient=tk.HORIZONTAL, length=200, showvalue=0, tickinterval=2, + resolution=0.01, command=print_selection) +s.pack() + +# 第7步，主窗口循环显示 +window.mainloop() diff --git a/tkinter_demo/boom.py b/tkinter_demo/boom.py new file mode 100644 index 0000000..bb140e8 --- /dev/null +++ b/tkinter_demo/boom.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk +import random +import threading +import time + + +def boom(): + # 第1步，实例化object，建立窗口window + window = tk.Tk() + width = window.winfo_screenwidth() + height = window.winfo_screenheight() + a = random.randrange(0, width) + b = random.randrange(0, height) + # 第2步，给窗口的可视化起名字 + window.title("你是一个傻狍子") + # 第3步，设定窗口的大小(长 * 宽) + window.geometry('200x50') + # 第4步，在图形界面上设定标签 + label = tk.Label(window, + text="你是一个傻狍子", + bg='green', + font=('宋体', 17), + width=a, + height=b) + # 第5步，放置标签 + label.pack() + # 第6步，主窗口循环显示 + window.mainloop() + + +if __name__ == '__main__': + threads = [] + for i in range(5): + t = threading.Thread(target=boom) + threads.append(t) + t.start() + time.sleep(0.3) + for t in threads: + t.join() diff --git a/tkinter_demo/button.py b/tkinter_demo/button.py new file mode 100644 index 0000000..5cf75cf --- /dev/null +++ b/tkinter_demo/button.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +""" +　简单说明： + +　　Button（按钮）部件是一个标准的Tkinter窗口部件，用来实现各种按钮。按钮能够包含文本或图象，并且你能够将按钮与一个Python函数或方法相关联。当这个按钮被按下时，Tkinter自动调用相关联的函数或方法。 + +按钮仅能显示一种字体，但是这个文本可以跨行。另外，这个文本中的一个字母可以有下划线，例如标明一个快捷键。默认情况，Tab键用于将焦点移动到一个按钮部件。 + +　　什么时候用按钮部件 + +　　简言之，按钮部件用来让用户说“马上给我执行这个任务”，通常我们用显示在按钮上的文本或图象来提示。按钮通常用在工具条中或应用程序窗口中，并且用来接收或忽略输入在对话框中的数据。关于按钮和输入的数据的配合，可以参看Checkbutton和Radiobutton部件。 + +　　如何创建： + +普通的按钮很容易被创建，仅仅指定按钮的内容（文本、位图、图象）和一个当按钮被按下时的回调函数即可： + +b = tk.Button(window, text="hit me", command=hit_me) + +没有回调函数的按钮是没有用的，当你按下这个按钮时它什么也不做。你可能在开发一个应用程序的时候想实现这种按钮，比如为了不干扰你的beta版的测试者： + +b = tk.Button(window, text="Help", command=DISABLED) +""" +import tkinter as tk # 使用Tkinter前需要先导入 + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('My Window') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('500x300') # 这里的乘是小x + +# 第4步，在图形界面上设定标签 +var = tk.StringVar() # 将label标签的内容设置为字符类型，用var来接收hit_me函数的传出内容用以显示在标签上 +l = tk.Label(window, textvariable=var, bg='green', fg='white', + font=('Arial', 12), width=30, height=2) +# 说明： bg为背景，fg为字体颜色，font为字体，width为长，height为高，这里的长和高是字符的长和高，比如height=2,就是标签有2个字符这么高 +l.pack() + +# 定义一个函数功能（内容自己自由编写），供点击Button按键时调用，调用命令参数command=函数名 +on_hit = False + + +def hit_me(): + global on_hit + if on_hit == False: + on_hit = True + var.set('you hit me') + else: + on_hit = False + var.set('') + + +# 第5步，在窗口界面设置放置Button按键 +b = tk.Button(window, text='hit me', font=('Arial', 12), + width=10, height=1, command=hit_me) +b.pack() + +# 第6步，主窗口循环显示 +window.mainloop() diff --git a/tkinter_demo/checkbutton.py b/tkinter_demo/checkbutton.py new file mode 100644 index 0000000..50db53e --- /dev/null +++ b/tkinter_demo/checkbutton.py @@ -0,0 +1,41 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk # 使用Tkinter前需要先导入 + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('My Window') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('500x300') # 这里的乘是小x + +# 第4步，在图形界面上创建一个标签label用以显示并放置 +l = tk.Label(window, bg='yellow', width=20, text='empty') +l.pack() + + +# 第6步，定义触发函数功能 +def print_selection(): + if (var1.get() == 1) & (var2.get() == 0): # 如果选中第一个选项，未选中第二个选项 + l.config(text='I love only Python ') + elif (var1.get() == 0) & (var2.get() == 1): # 如果选中第二个选项，未选中第一个选项 + l.config(text='I love only C++') + elif (var1.get() == 0) & (var2.get() == 0): # 如果两个选项都未选中 + l.config(text='I do not love either') + else: + l.config(text='I love both') # 如果两个选项都选中 + + +# 第5步，定义两个Checkbutton选项并放置 +var1 = tk.IntVar() # 定义var1和var2整型变量用来存放选择行为返回值 +var2 = tk.IntVar() +c1 = tk.Checkbutton(window, text='Python', variable=var1, onvalue=1, offvalue=0, + command=print_selection) # 传值原理类似于radiobutton部件 +c1.pack() +c2 = tk.Checkbutton(window, text='C++', variable=var2, onvalue=1, offvalue=0, command=print_selection) +c2.pack() + +# 第7步，主窗口循环显示 +window.mainloop() diff --git a/tkinter_demo/entry.py b/tkinter_demo/entry.py new file mode 100644 index 0000000..a59a5d3 --- /dev/null +++ b/tkinter_demo/entry.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk # 使用Tkinter前需要先导入 + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('My Window') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('500x300') # 这里的乘是小x + +# 第4步，在图形界面上设定输入框控件entry并放置控件 +e1 = tk.Entry(window, show='*', font=('Arial', 14)) # 显示成密文形式 +e2 = tk.Entry(window, show=None, font=('Arial', 14)) # 显示成明文形式 +e1.pack() +e2.pack() + +# 第5步，主窗口循环显示 +window.mainloop() diff --git a/tkinter_demo/listbox.py b/tkinter_demo/listbox.py new file mode 100644 index 0000000..2da65d8 --- /dev/null +++ b/tkinter_demo/listbox.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk # 使用Tkinter前需要先导入 + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('My Window') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('500x300') # 这里的乘是小x + +# 第4步，在图形界面上创建一个标签label用以显示并放置 +var1 = tk.StringVar() # 创建变量，用var1用来接收鼠标点击具体选项的内容 +l = tk.Label(window, bg='green', fg='yellow', font=('Arial', 12), width=10, textvariable=var1) +l.pack() + + +# 第6步，创建一个方法用于按钮的点击事件 +def print_selection(): + value = lb.get(lb.curselection()) # 获取当前选中的文本 + var1.set(value) # 为label设置值 + + +# 第5步，创建一个按钮并放置，点击按钮调用print_selection函数 +b1 = tk.Button(window, text='print selection', width=15, height=2, command=print_selection) +b1.pack() + +# 第7步，创建Listbox并为其添加内容 +var2 = tk.StringVar() +var2.set((1, 2, 3, 4)) # 为变量var2设置值 +# 创建Listbox +lb = tk.Listbox(window, listvariable=var2) # 将var2的值赋给Listbox +# 创建一个list并将值循环添加到Listbox控件中 +list_items = [11, 22, 33, 44] +for item in list_items: + lb.insert('end', item) # 从最后一个位置开始加入值 +lb.insert(1, 'first') # 在第一个位置加入'first'字符 +lb.insert(2, 'second') # 在第二个位置加入'second'字符 +lb.delete(2) # 删除第二个位置的字符 +lb.pack() + +# 第8步，主窗口循环显示 +window.mainloop() diff --git a/tkinter_demo/messageBox.py b/tkinter_demo/messageBox.py new file mode 100644 index 0000000..f9a3ca6 --- /dev/null +++ b/tkinter_demo/messageBox.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk # 使用Tkinter前需要先导入 +import tkinter.messagebox # 要使用messagebox先要导入模块 + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('My Window') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('500x300') # 这里的乘是小x + + +# 第5步，定义触发函数功能 +def hit_me(): + tkinter.messagebox.showinfo(title='Hi', message='你好！') # 提示信息对话窗 + # tkinter.messagebox.showwarning(title='Hi', message='有警告！') # 提出警告对话窗 + # tkinter.messagebox.showerror(title='Hi', message='出错了！') # 提出错误对话窗 + # print(tkinter.messagebox.askquestion(title='Hi', message='你好！')) # 询问选择对话窗return 'yes', 'no' + # print(tkinter.messagebox.askyesno(title='Hi', message='你好！')) # return 'True', 'False' + # print(tkinter.messagebox.askokcancel(title='Hi', message='你好！')) # return 'True', 'False' + + +# 第4步，在图形界面上创建一个标签用以显示内容并放置 +tk.Button(window, text='hit me', bg='green', font=('Arial', 14), command=hit_me).pack() + +# 第6步，主窗口循环显示 +window.mainloop() diff --git a/tkinter_demo/pic.gif b/tkinter_demo/pic.gif new file mode 100644 index 0000000..1ad06f2 Binary files /dev/null and b/tkinter_demo/pic.gif differ diff --git a/tkinter_demo/radiobutton.py b/tkinter_demo/radiobutton.py new file mode 100644 index 0000000..3378908 --- /dev/null +++ b/tkinter_demo/radiobutton.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk # 使用Tkinter前需要先导入 + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('My Window') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('500x300') # 这里的乘是小x + +# 第4步，在图形界面上创建一个标签label用以显示并放置 +var = tk.StringVar() # 定义一个var用来将radiobutton的值和Label的值联系在一起. +l = tk.Label(window, bg='yellow', width=20, text='empty') +l.pack() + + +# 第6步，定义选项触发函数功能 +def print_selection(): + l.config(text='you have selected ' + var.get()) + + +# 第5步，创建三个radiobutton选项，其中variable=var, value='A'的意思就是，当我们鼠标选中了其中一个选项，把value的值A放到变量var中，然后赋值给variable +r1 = tk.Radiobutton(window, text='Option A', variable=var, value='A', command=print_selection) +r1.pack() +r2 = tk.Radiobutton(window, text='Option B', variable=var, value='B', command=print_selection) +r2.pack() +r3 = tk.Radiobutton(window, text='Option C', variable=var, value='C', command=print_selection) +r3.pack() + +# 第7步，主窗口循环显示 +window.mainloop() diff --git a/tkinter_demo/text.py b/tkinter_demo/text.py new file mode 100644 index 0000000..023d890 --- /dev/null +++ b/tkinter_demo/text.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk # 使用Tkinter前需要先导入 + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('My Window') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('500x300') # 这里的乘是小x + +# 第4步，在图形界面上设定输入框控件entry框并放置 +e = tk.Entry(window, show=None) # 显示成明文形式 +e.pack() + + +# 第5步，定义两个触发事件时的函数insert_point和insert_end（注意：因为Python的执行顺序是从上往下，所以函数一定要放在按钮的上面） +def insert_point(): # 在鼠标焦点处插入输入内容 + var = e.get() + t.insert('insert', var) + + +def insert_end(): # 在文本框内容最后接着插入输入内容 + var = e.get() + t.insert('end', var) + + +# 第6步，创建并放置两个按钮分别触发两种情况 +b1 = tk.Button(window, + text='insert point', + width=10, + height=2, + command=insert_point) +b1.pack() +b2 = tk.Button(window, + text='insert end', + width=10, + height=2, + command=insert_end) +b2.pack() + +# 第7步，创建并放置一个多行文本框text用以显示，指定height=3为文本框是三个字符高度 +t = tk.Text(window, height=3) +t.pack() + +# 第8步，主窗口循环显示 +window.mainloop() diff --git a/tkinter_demo/user_login.py b/tkinter_demo/user_login.py new file mode 100644 index 0000000..004396b --- /dev/null +++ b/tkinter_demo/user_login.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import tkinter as tk # 使用Tkinter前需要先导入 +import tkinter.messagebox +import pickle + +# 第1步，实例化object，建立窗口window +window = tk.Tk() + +# 第2步，给窗口的可视化起名字 +window.title('Wellcome to Hongwei Website') + +# 第3步，设定窗口的大小(长 * 宽) +window.geometry('400x300') # 这里的乘是小x + +# 第4步，加载 wellcome image +canvas = tk.Canvas(window, width=400, height=135, bg='green') +image_file = tk.PhotoImage(file='pic.gif') +image = canvas.create_image(200, 0, anchor='n', image=image_file) +canvas.pack(side='top') +tk.Label(window, text='Wellcome', font=('Arial', 16)).pack() + +# 第5步，用户信息 +tk.Label(window, text='User name:', font=('Arial', 14)).place(x=10, y=170) +tk.Label(window, text='Password:', font=('Arial', 14)).place(x=10, y=210) + +# 第6步，用户登录输入框entry +# 用户名 +var_usr_name = tk.StringVar() +var_usr_name.set('example@python.com') +entry_usr_name = tk.Entry(window, textvariable=var_usr_name, font=('Arial', 14)) +entry_usr_name.place(x=120, y=175) +# 用户密码 +var_usr_pwd = tk.StringVar() +entry_usr_pwd = tk.Entry(window, textvariable=var_usr_pwd, font=('Arial', 14), show='*') +entry_usr_pwd.place(x=120, y=215) + + +# 第8步，定义用户登录功能 +def usr_login(): + # 这两行代码就是获取用户输入的usr_name和usr_pwd + usr_name = var_usr_name.get() + usr_pwd = var_usr_pwd.get() + + # 这里设置异常捕获，当我们第一次访问用户信息文件时是不存在的，所以这里设置异常捕获。 + # 中间的两行就是我们的匹配，即程序将输入的信息和文件中的信息匹配。 + try: + with open('usrs_info.pickle', 'rb') as usr_file: + usrs_info = pickle.load(usr_file) + except FileNotFoundError: + # 这里就是我们在没有读取到`usr_file`的时候，程序会创建一个`usr_file`这个文件，并将管理员 + # 的用户和密码写入，即用户名为`admin`密码为`admin`。 + with open('usrs_info.pickle', 'wb') as usr_file: + usrs_info = {'admin': 'admin'} + pickle.dump(usrs_info, usr_file) + usr_file.close() # 必须先关闭，否则pickle.load()会出现EOFError: Ran out of input + + # 如果用户名和密码与文件中的匹配成功，则会登录成功，并跳出弹窗how are you? 加上你的用户名。 + if usr_name in usrs_info: + if usr_pwd == usrs_info[usr_name]: + tkinter.messagebox.showinfo(title='Welcome', message='How are you? ' + usr_name) + # 如果用户名匹配成功，而密码输入错误，则会弹出'Error, your password is wrong, try again.' + else: + tkinter.messagebox.showerror(message='Error, your password is wrong, try again.') + else: # 如果发现用户名不存在 + is_sign_up = tkinter.messagebox.askyesno('Welcome！ ', 'You have not sign up yet. Sign up now?') + # 提示需不需要注册新用户 + if is_sign_up: + usr_sign_up() + + +# 第9步，定义用户注册功能 +def usr_sign_up(): + def sign_to_Hongwei_Website(): + # 以下三行就是获取我们注册时所输入的信息 + np = new_pwd.get() + npf = new_pwd_confirm.get() + nn = new_name.get() + + # 这里是打开我们记录数据的文件，将注册信息读出 + with open('usrs_info.pickle', 'rb') as usr_file: + exist_usr_info = pickle.load(usr_file) + # 这里就是判断，如果两次密码输入不一致，则提示Error, Password and confirm password must be the same! + if np != npf: + tkinter.messagebox.showerror('Error', 'Password and confirm password must be the same!') + + # 如果用户名已经在我们的数据文件中，则提示Error, The user has already signed up! + elif nn in exist_usr_info: + tkinter.messagebox.showerror('Error', 'The user has already signed up!') + + # 最后如果输入无以上错误，则将注册输入的信息记录到文件当中，并提示注册成功Welcome！,You have successfully signed up!，然后销毁窗口。 + else: + exist_usr_info[nn] = np + with open('usrs_info.pickle', 'wb') as usr_file: + pickle.dump(exist_usr_info, usr_file) + tkinter.messagebox.showinfo('Welcome', 'You have successfully signed up!') + # 然后销毁窗口。 + window_sign_up.destroy() + + # 定义长在窗口上的窗口 + window_sign_up = tk.Toplevel(window) + window_sign_up.geometry('300x200') + window_sign_up.title('Sign up window') + + new_name = tk.StringVar() # 将输入的注册名赋值给变量 + new_name.set('example@python.com') # 将最初显示定为'example@python.com' + tk.Label(window_sign_up, text='User name: ').place(x=10, y=10) # 将`User name:`放置在坐标（10,10）。 + entry_new_name = tk.Entry(window_sign_up, textvariable=new_name) # 创建一个注册名的`entry`，变量为`new_name` + entry_new_name.place(x=130, y=10) # `entry`放置在坐标（150,10）. + + new_pwd = tk.StringVar() + tk.Label(window_sign_up, text='Password: ').place(x=10, y=50) + entry_usr_pwd = tk.Entry(window_sign_up, textvariable=new_pwd, show='*') + entry_usr_pwd.place(x=130, y=50) + + new_pwd_confirm = tk.StringVar() + tk.Label(window_sign_up, text='Confirm password: ').place(x=10, y=90) + entry_usr_pwd_confirm = tk.Entry(window_sign_up, textvariable=new_pwd_confirm, show='*') + entry_usr_pwd_confirm.place(x=130, y=90) + + # 下面的 sign_to_Hongwei_Website + btn_comfirm_sign_up = tk.Button(window_sign_up, text='Sign up', command=sign_to_Hongwei_Website) + btn_comfirm_sign_up.place(x=180, y=120) + + +# 第7步，login and sign up 按钮 +btn_login = tk.Button(window, text='Login', command=usr_login) +btn_login.place(x=120, y=240) +btn_sign_up = tk.Button(window, text='Sign up', command=usr_sign_up) +btn_sign_up.place(x=200, y=240) + +# 第10步，主窗口循环显示 +window.mainloop() diff --git a/tools/bin2dump.py b/tools/bin2dump.py new file mode 100644 index 0000000..2781843 --- /dev/null +++ b/tools/bin2dump.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +from __future__ import with_statement, print_function +import sys +import binascii + +READ_BLOCKSIZE = 16 + +is_py3 = sys.version_info[0] == 3 + + +def main(argv): + if len(argv) < 3: + print('Usage: {0} input_file output_file'.format(argv[0])) + sys.exit(1) + with open(argv[1], 'rb') as file_inp, open(argv[2], 'w') as file_out: + while True: + byte_s = file_inp.read(READ_BLOCKSIZE) + if not byte_s: break + hex_char_repr = binascii.hexlify(byte_s) + hex_char_repr = str(hex_char_repr, encoding='utf-8') if is_py3 else hex_char_repr + file_out.write(hex_char_repr) + file_out.write("\n") + + +if __name__ == '__main__': + main(sys.argv) diff --git a/tools/combinations.py b/tools/combinations.py new file mode 100644 index 0000000..0e039e1 --- /dev/null +++ b/tools/combinations.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +# Sebastian Raschka 2014 +# Functions to calculate factorial, combinations, and permutations +# bundled in an simple command line interface. + +def factorial(n): + if n == 0: + return 1 + else: + return n * factorial(n - 1) + + +def combinations(n, r): + numerator = factorial(n) + denominator = factorial(r) * factorial(n - r) + return int(numerator / denominator) + + +def permutations(n, r): + numerator = factorial(n) + denominator = factorial(n - r) + return int(numerator / denominator) + + +assert (factorial(3) == 6) +assert (combinations(20, 8) == 125970) +assert (permutations(30, 3) == 24360) + +if __name__ == '__main__': + + import argparse + + parser = argparse.ArgumentParser( + description='Script to calculate the number of combinations or permutations ("n choose r")', + formatter_class=argparse.RawTextHelpFormatter, + + prog='Combinations', + epilog='Example: ./combinations.py -c 20 3' + ) + + parser.add_argument('-c', '--combinations', type=int, metavar='NUMBER', nargs=2, + help='Combinations: Number of ways to combine n items with sequence length r where the item order does not matter.') + + parser.add_argument('-p', '--permutations', type=int, metavar='NUMBER', nargs=2, + help='Permutations: Number of ways to combine n items with sequence length r where the item order does not matter.') + + parser.add_argument('-f', '--factorial', type=int, metavar='NUMBER', help='n! e.g., 5! = 5*4*3*2*1 = 120.') + + parser.add_argument('--version', action='version', version='%(prog)s 1.0') + + args = parser.parse_args() + print(args) + if not any((args.combinations, args.permutations, args.factorial)): + parser.print_help() + quit() + + if args.factorial: + print(factorial(args.factorial)) + + if args.combinations: + print(combinations(args.combinations[0], args.combinations[1])) + + if args.permutations: + print(permutations(args.permutations[0], args.permutations[1])) + + if args.factorial: + print(factorial(args.factorial)) + + diff --git a/tools/compute_duration.py b/tools/compute_duration.py new file mode 100644 index 0000000..97b7187 --- /dev/null +++ b/tools/compute_duration.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +""" +递归统计一个目录下所有视频文件的总时长。比如笔者用来统计课程的内容总时长。 + +pip install moviepy + +如果安装报错，尝试升级 + +pip install --upgrade setuptools + +使用方法： +python compute_duration.py --path ~/Movies/ --type .mp4 + +参考：https://blog.csdn.net/qq_22210253/article/details/86684658 +""" + +import os +import datetime +import argparse +from moviepy.editor import VideoFileClip + + +def main(): + parser = argparse.ArgumentParser( + description='Compute Total Time of a Series of Videos') + parser.add_argument("--path", metavar="PATH", default=".", + help="the root path of the videos(default: .)") + parser.add_argument("--type", metavar="TYPE", default=".mkv", + help="the type of the videos(default: .mkv)") + args = parser.parse_args() + filelist = [] + for root, dirs, files in os.walk(args.path): + for name in files: + fname = os.path.join(root, name) + if fname.endswith(args.type): + filelist.append(fname) + ftime = 0.0 + for file in sorted(filelist): + clip = VideoFileClip(file) + print("{}: {}秒".format(file, clip.duration)) + ftime += clip.duration + print("%d seconds: " % ftime, str(datetime.timedelta(seconds=ftime))) + + +if __name__ == "__main__": + main() diff --git a/tools/concat_video.py b/tools/concat_video.py new file mode 100644 index 0000000..b2ea94a --- /dev/null +++ b/tools/concat_video.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +""" +使用 ffmpeg 截取视频片段并且重新拼接 + +使用方式: +提供文件格式如下：比如 input.txt + +./input.mp4 +00:01:00 00:02:00 +00:04:00 00:08:00 +""" + +import os +import sys + +CONCAT_FILE = '_concat.txt' + + +def read_input(filepath): + with open(filepath) as f: + lines = f.readlines() + input_mp4 = lines[0].strip() + suffix = input_mp4.split('.')[-1] + for idx, start_end_time in enumerate(lines[1:]): + pair = start_end_time.split() + start, end = pair[0], pair[1] + part_name = 'part_' + str(idx) + '.' + suffix + cmd = "ffmpeg -i {} -ss {} -to {} -c copy {}".format( + input_mp4, start, end, part_name + ) + print(cmd) + os.system(cmd) + yield part_name + + +def write_part_to_file(part_list): + dir_path = os.path.dirname(os.path.realpath(__file__)) + filepath_list = [] + for part_name in part_list: + print(part_name) + path = os.path.join(dir_path, part_name) + filepath_list.append(path) + + with open(CONCAT_FILE, 'w') as f: + for path in filepath_list: + f.write("file '{}'\n".format(path)) + return filepath_list + + +def concat_video(): + cmd = "ffmpeg -f concat -safe 0 -i {} -c copy output.mp4".format(CONCAT_FILE) + os.system(cmd) + + +def remove(filepath_list): + """移除中间文件""" + for path in filepath_list + [CONCAT_FILE]: + if os.path.exists(path): + os.remove(path) + + +def main(): + try: + inputfile = sys.argv[1] + except KeyError: + print('must need input.txt') + partnames = list(read_input(inputfile)) + filepath_list = write_part_to_file(partnames) + concat_video() + remove(filepath_list) + + +if __name__ == '__main__': + main() diff --git a/tools/dataframe_draw.py b/tools/dataframe_draw.py index 2879594..7f2c436 100755 --- a/tools/dataframe_draw.py +++ b/tools/dataframe_draw.py @@ -1,5 +1,10 @@ #!/usr/bin/env python # -*- coding:utf-8 -*- +import base64 +import datetime +from io import StringIO + +import pandas as pd def draw(): @@ -31,7 +36,7 @@ def draw(): fig = plot.get_figure() io = StringIO() fig.savefig(io, format='png') - img_data = base64.encodestring(io.getvalue()) + img_data = base64.encodebytes(bytes(io.getvalue())) return u''.format(img_data) if __name__ == '__main__': diff --git a/tools/deltmp.py b/tools/deltmp.py index 12c822b..5f84abe 100755 --- a/tools/deltmp.py +++ b/tools/deltmp.py @@ -47,7 +47,8 @@ def del_dir(dir): def if_match(func): if i.match(j): func(join(root, j)) - print join(root, j) + print(join(root, j)) + for j in dirs: if_match(del_dir) diff --git a/tools/find_file.py b/tools/find_file.py new file mode 100644 index 0000000..9d50643 --- /dev/null +++ b/tools/find_file.py @@ -0,0 +1,19 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +# A Python function to find files in a directory based on a substring search. +import os + + +def find_files(substring, path): + results = [] + for f in os.listdir(path): + if substring in f: + results.append(os.path.join(path, f)) + return results + + +if __name__ == '__main__': + results = find_files('Untitled', '/Users/sebastian/Desktop/') + print(results) + # returns + # ['/Users/sebastian/Desktop/Untitled0.ipynb'] diff --git a/tools/flask_server.py b/tools/flask_server.py new file mode 100644 index 0000000..993c5a5 --- /dev/null +++ b/tools/flask_server.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +# pip install flask Flask-RESTful +from flask import Flask +from flask_restful import reqparse, abort, Api, Resource +from flask import request + +app = Flask(__name__) +api = Api(app) + +TODOS = { + 'todo1': {'task': 'build an API'}, + 'todo2': {'task': '?????'}, + 'todo3': {'task': 'profit!'}, +} + + +def abort_if_todo_doesnt_exist(todo_id): + if todo_id not in TODOS: + abort(404, message="Todo {} doesn't exist".format(todo_id)) + + +parser = reqparse.RequestParser() +parser.add_argument('task') + + +# Todo +# shows a single todo item and lets you delete a todo item +class Todo(Resource): + def get(self, todo_id): + abort_if_todo_doesnt_exist(todo_id) + return TODOS[todo_id] + + def delete(self, todo_id): + abort_if_todo_doesnt_exist(todo_id) + del TODOS[todo_id] + return '', 204 + + def put(self, todo_id): + args = parser.parse_args() + task = {'task': args['task']} + TODOS[todo_id] = task + return task, 201 + + +# TodoList +# shows a list of all todos, and lets you POST to add new tasks +class TodoList(Resource): + def get(self): + return TODOS + + def post(self): + print(request.data) + args = parser.parse_args() + todo_id = int(max(TODOS.keys()).lstrip('todo')) + 1 + todo_id = 'todo%i' % todo_id + TODOS[todo_id] = {'task': args['task']} + return TODOS[todo_id], 201 + + +class TaskList(Resource): + def post(self): + print(request.data) + return {"id": 1} + + +class Ping(Resource): + def get(self): + return 'pong' + +## +# Actually setup the Api resource routing here +## +api.add_resource(Ping, '/') +api.add_resource(TodoList, '/todos') +api.add_resource(Todo, '/todos/') +api.add_resource(TaskList, '/tasks') + + +if __name__ == '__main__': + app.run(debug=True) # port=5000 diff --git a/tools/monitor_and_reload_chrome.py b/tools/monitor_and_reload_chrome.py index dda54c4..8feaf9e 100755 --- a/tools/monitor_and_reload_chrome.py +++ b/tools/monitor_and_reload_chrome.py @@ -82,7 +82,7 @@ def refresh_browser(): def start_browser(): - # Browser is launched via a daemon thread. + # Browser is launched via a daemon threads. # It will terminate the browser when you close the python script. thread = threading.Thread(target=open_browser, kwargs={'url': 'http://localhost:8000'}) thread.daemon = True diff --git a/tools/monitor_disk_space.py b/tools/monitor_disk_space.py new file mode 100644 index 0000000..b86a619 --- /dev/null +++ b/tools/monitor_disk_space.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import time +import shutil +import os +import random +import psutil +import glob +from psutil._common import bytes2human +from loguru import logger + + +def get_disk_space(path='C:'): + """ + get disk space + """ + usage = psutil.disk_usage(path) + space_total = bytes2human(usage.total) + space_used = bytes2human(usage.used) + space_free = bytes2human(usage.free) + space_used_percent = float(bytes2human(usage.percent)[:-1]) / 100 + logger.info( + '{0:.2%} : {1}/{2}, remaining capacity {3}', + space_used_percent, + space_used, + space_total, + space_free, + ) + return space_used_percent + + +def copy_file(old_path, new_path): + """copy file""" + try: + logger.info(f'start copy file {old_path} to {new_path}') + shutil.move(old_path, new_path) + logger.info(f'copy file {old_path} to {new_path} finished') + except Exception as e: + logger.error(e) + + +def main(): + """Main function""" + to_path = r'E:\新建文件夹' + file_list = glob.glob(r'C:\Users\82718\Downloads\Video\*.ts') + if file_list: + filepath = random.choice(file_list) + copy_file(filepath, to_path) + else: + logger.warning('Could not find file') + + +if __name__ == '__main__': + while True: + time.sleep(10) + if get_disk_space() > 0.2: + main() diff --git a/tools/print_ascii.py b/tools/print_ascii.py new file mode 100644 index 0000000..f68bbb8 --- /dev/null +++ b/tools/print_ascii.py @@ -0,0 +1,8 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- + +"""打印ASCII字母表、数字、标点符号""" +import string + +for item in [string.ascii_letters,string.digits,string.punctuation]: + print(f"{len(item)}\t{item}") diff --git a/tools/remote_control_computer.py b/tools/remote_control_computer.py new file mode 100644 index 0000000..3019671 --- /dev/null +++ b/tools/remote_control_computer.py @@ -0,0 +1,62 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- + +import os +import time +import yagmail +from imbox import Imbox +from PIL import ImageGrab +""" +需要安装的包 +pip install imbox +pip install yagmail +pip install pillow +pip install keyring + +""" + +def send_mail(sender, to, contents): + smtp = yagmail.SMTP(user=sender, host='smtp.qq.com') + smtp.send(to, subject='Remote Control', contents=contents) + + +def read_mail(username, password): + with Imbox('imap.qq.com', username, password, ssl=True) as box: + all_msg = box.messages(unread=True) + for uid, message in all_msg: + # 如果是手机端发来的远程控制邮件 + if message.subject == 'Remote Control': + # 标记为已读 + box.mark_seen(uid) + return message.body['plain'][0] + + +def shutdown(): + os.system('shutdown -s -t 0') + + +def grab(sender, to): + surface = ImageGrab.grab() + surface.save('surface.jpg') + send_mail(sender, to, ['surface.jpg']) + + +def main(): + username = '982698078@qq.com' + password = '你的邮箱授权码' + receiver = 'xxxxx@qq.com' + time_space = 5 + yagmail.register(username, password) + while True: + # 读取未读邮件 + msg = read_mail(username, password) + if msg: + if msg == 'shutdown': + shutdown() + elif msg == 'grab': + grab(username, receiver) + time.sleep(time_space) + + +if __name__ == '__main__': + main() diff --git a/tools/youdao.py b/tools/youdao.py index 7d88738..4378f21 100755 --- a/tools/youdao.py +++ b/tools/youdao.py @@ -31,6 +31,7 @@ def fetch(query_str=''): def parse(html): d = json.loads(html) + print(d) try: if d.get('errorCode') == 0: if d.get('translation'): diff --git a/useful_script/palindrome.py b/useful_script/palindrome.py new file mode 100644 index 0000000..ea9905b --- /dev/null +++ b/useful_script/palindrome.py @@ -0,0 +1,14 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +def palindrome(my_str): + """ + Returns True if an input string is a palindrome. Else returns False. + """ + stripped_str = "".join(l.lower() for l in my_str if l.isalpha()) + return stripped_str == stripped_str[::-1] + +if __name__ == '__main__': + test1 = 'Hello World!' + test2 = "Go hang a salami. I'm a lasagna hog." + print('test1', palindrome(test1)) + print('test2', palindrome(test2)) diff --git a/useful_script/random_string_generator.py b/useful_script/random_string_generator.py new file mode 100644 index 0000000..6ede5f5 --- /dev/null +++ b/useful_script/random_string_generator.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +import string +import random + +def rand_string(length): + """ Generates a random string of numbers, lower- and uppercase chars. """ + return ''.join(random.choice( + string.ascii_lowercase + string.ascii_uppercase + string.digits) + for i in range(length) + ) + +if __name__ == '__main__': + print("Example1:", rand_string(length=4)) + print("Example2:", rand_string(length=8)) + print("Example2:", rand_string(length=16)) + + + # Example1: 5bVL + # Example2: oIIg37xl + # Example2: 7IqDbrf506TatFO9 diff --git a/video/video2gif.py b/video/video2gif.py new file mode 100644 index 0000000..6122670 --- /dev/null +++ b/video/video2gif.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- +# 1. 安装必备库moviepy +# pip install moviepy -i https://pypi.tuna.tsinghua.edu.cn/simple + +# 2. 写入代码 + +from moviepy.editor import * + +clip = (VideoFileClip("movie.mp4")) # 需要转为GIF的视频文件路径 +clip.write_gif("movie.gif") + +# 4. GIF很大的解决方案 +# 我们除了设置缩放分辨率resize外，还可以通过设置fps参数抽帧来减少大小 + +from moviepy.editor import * + +clip = (VideoFileClip("movie.mp4").resize((488,225))) +clip.write_gif("movie.gif",fps=15) #设置为每秒15帧 + +# 设置为每秒15帧后，文件大小只有2m多，一下缩小了4倍之多！ + +# 5. 截取视频长度转换 +# 我们还可以通过设置subclip参数来指定转换的视频范围: +# +# subclip：截取原视频中的自t_start至t_end间的视频片段 +# +# 将视频1-2秒片段转化为Gif + + +from moviepy.editor import * + +clip = (VideoFileClip("movie.mp4").subclip(t_start=1, t_end=2).resize((488, 225))) +clip.write_gif("movie.gif", fps=15) + +# 6. 指定转换后的图片大小（分辨率） +# resize参数可指定转换后的图片大小 +# +# 接受的参数为： +# +# 以像素或浮点表示的(width,height) +# +# 缩放百分比，如 0.5 +# +# 示例 +# +# 1.设置转换后的图片为600*400 +# +clip = (VideoFileClip("movie.mp4").resize((600, 400))) +clip.write_gif("movie.gif", fps=15) + +# 2.原视频缩放50% +# +clip = (VideoFileClip("movie.mp4").resize(0.5)) +clip.write_gif("movie.gif", fps=15) + diff --git a/web/baidu_google_ping.py b/web/baidu_google_ping.py index 0e7e29a..d644c92 100644 --- a/web/baidu_google_ping.py +++ b/web/baidu_google_ping.py @@ -15,7 +15,7 @@ def ping(ping_url, site_name, site_host, post_url, rss_url): result = rpc_server.weblogUpdates.extendedPing( site_name, site_host, "http://"+post_url, "http://"+rss_url ) - print result + print(result) def ping_all(*args, **kwds): @@ -24,7 +24,7 @@ def ping_all(*args, **kwds): 'http://rpc.pingomatic.com/', 'http://blogsearch.google.com/ping/RPC2', ] - print args + print(args) for url in ping_url_list: ping(url, *args, **kwds) @@ -34,9 +34,9 @@ def main(): client = redis.pubsub() client.subscribe(['ping']) while True: - print "." + print(".") for item in client.listen(): - print item + print(item) if item['type'] == 'message': msg = item['data'] if msg: diff --git a/web/parse_form_data.py b/web/parse_form_data.py index fc04bc8..b27f1ec 100644 --- a/web/parse_form_data.py +++ b/web/parse_form_data.py @@ -44,4 +44,4 @@ def form_data_to_dict(s): if __name__ == '__main__': import pprint - pprint.pprint(forms_data_to_dict(FORMS_DATA)) + pprint.pprint(form_data_to_dict(FORMS_DATA)) diff --git a/web/url_encode_unquote.py b/web/url_encode_unquote.py index 112f6b5..96ff60f 100644 --- a/web/url_encode_unquote.py +++ b/web/url_encode_unquote.py @@ -12,12 +12,12 @@ 'b': 2 } -print urlencode(data) # 对dict encode +print(urlencode(data)) # 对dict encode s = '呵呵' -print quote(s) # 对字符串encode +print(quote(s)) # 对字符串encode -print unquote(urlencode(data)) -print unquote(s) +print(unquote(urlencode(data))) +print(unquote(s)) # tornado send post post_data = { 'data': 'test data' } #A dictionary of your post data diff --git a/web/validator_email_phone.py b/web/validator_email_phone.py index cefaa20..9a2c0ed 100644 --- a/web/validator_email_phone.py +++ b/web/validator_email_phone.py @@ -75,16 +75,16 @@ def validate(data_list, data_type): if __name__ == '__main__': number = "+49 176 1234 5678" - print validate_phone(number) + print(validate_phone(number)) number = "+86 18810564550" - print validate_phone(number) + print(validate_phone(number)) number = "18810564550" - print validate_phone(number) + print(validate_phone(number)) number = "49 176 1234 5678" - print validate_phone(number) + print(validate_phone(number)) number = "86 18810564550" - print validate_phone(number) + print(validate_phone(number)) number = "+86-18810564550" - print validate_phone(number) + print(validate_phone(number)) number = "+86-188 1056 4550" - print validate_phone(number) + print(validate_phone(number)) diff --git a/z42/_lua_debug.py b/z42/_lua_debug.py index 7876248..ac77db5 100644 --- a/z42/_lua_debug.py +++ b/z42/_lua_debug.py @@ -1,7 +1,7 @@ #!/usr/bin/env python #coding:utf-8 - +from __future__ import print_function #import sys #if sys.getdefaultencoding() == 'ascii': # reload(sys) @@ -16,4 +16,4 @@ from zapp.BTC.model._redis import redis while True: - print redis.brpop('DEBUG')[1] + print(redis.brpop('DEBUG')[1]) diff --git a/z42/hg_close_branch b/z42/hg_close_branch index 145cc32..5734f7f 100755 --- a/z42/hg_close_branch +++ b/z42/hg_close_branch @@ -1,7 +1,11 @@ #!/usr/bin/env python -#coding:utf-8 +# coding:utf-8 +from __future__ import print_function + + class _Getch: """Gets a single character from standard input. Does not echo to the screen.""" + def __init__(self): try: self.impl = _GetchWindows() @@ -11,11 +15,14 @@ class _Getch: except ImportError: self.impl = _GetchUnix() - def __call__(self): return self.impl() + def __call__(self): + return self.impl() + class _GetchUnix: def __init__(self): - import tty, sys, termios # import termios now or else you'll get the Unix version on the Mac + import tty, sys, termios # import termios now or else you'll get the Unix version on the Mac + def __call__(self): import sys, tty, termios fd = sys.stdin.fileno() @@ -27,6 +34,7 @@ class _GetchUnix: termios.tcsetattr(fd, termios.TCSADRAIN, old_settings) return ch + class _GetchWindows: def __init__(self): import msvcrt @@ -35,6 +43,7 @@ class _GetchWindows: import msvcrt return msvcrt.getch() + class _GetchMacCarbon: """ A function which returns the current ASCII key that is down; @@ -42,13 +51,14 @@ class _GetchMacCarbon: page http://www.mactech.com/macintosh-c/chap02-1.html was very helpful in figuring out how to do this. """ + def __init__(self): from Carbon import Evt - Evt #see if it has this (in Unix, it doesn't) + Evt # see if it has this (in Unix, it doesn't) def __call__(self): from Carbon import Evt - if Evt.EventAvail(0x0008)[0] == 0: # 0x0008 is the keyDownMask + if Evt.EventAvail(0x0008)[0] == 0: # 0x0008 is the keyDownMask return '' else: # @@ -62,9 +72,10 @@ class _GetchMacCarbon: # (what, msg, when, where, mod) = Evt.GetNextEvent(0x0008)[1] return chr(msg & 0x000000FF) -getch = _Getch() +getch = _Getch() + from os.path import join from os import getenv from optparse import OptionParser @@ -76,7 +87,7 @@ def main(): from os.path import dirname, exists, join path = os.getcwd() - #os.chroot(path) + # os.chroot(path) sys.argv = sys.argv[:1] while True: @@ -88,11 +99,11 @@ def main(): name = i.split(' ', 1) if name[0] != 'default': t.append(map(str.strip, name)) - print '\n' + print('\n') for pos, i in enumerate(t, 1): - print '%s\t%s\t%s'%(str(pos).ljust(3), i[0].ljust(35), i[1]) + print('%s\t%s\t%s' % (str(pos).ljust(3), i[0].ljust(35), i[1])) - num_list = raw_input('\nCLOSE BRANCH : ').strip().split() + num_list = input('\nCLOSE BRANCH : ').strip().split() branch_list = [] @@ -105,7 +116,7 @@ def main(): branch = t[num][0] branch_list.append(branch) - print '\n关闭分支\n%s\n(Y确认,否则取消)'%'\n'.join(branch_list) + print('\n关闭分支\n%s\n(Y确认,否则取消)' % '\n'.join(branch_list)) if not getch() in ['y', 'Y']: continue @@ -115,22 +126,18 @@ def main(): hg update %s hg commit --close-branch -m close hg update default -"""%branch - line_break = '-'*64 +""" % branch + line_break = '-' * 64 cmd = [i.strip() for i in cmd.split('\n') if i] - print line_break + print(line_break) import os success = True for i in cmd: - print '>>>%s'%i + print('>>>%s' % i) if os.system(i) != 0: success = False break - - - if __name__ == '__main__': main() - diff --git a/z42/hg_update_branch b/z42/hg_update_branch index d9a03d8..9b355a8 100755 --- a/z42/hg_update_branch +++ b/z42/hg_update_branch @@ -1,7 +1,11 @@ #!/usr/bin/env python -#coding:utf-8 +# coding:utf-8 +from __future__ import print_function + + class _Getch: """Gets a single character from standard input. Does not echo to the screen.""" + def __init__(self): try: self.impl = _GetchWindows() @@ -11,11 +15,14 @@ class _Getch: except ImportError: self.impl = _GetchUnix() - def __call__(self): return self.impl() + def __call__(self): + return self.impl() + class _GetchUnix: def __init__(self): - import tty, sys, termios # import termios now or else you'll get the Unix version on the Mac + import tty, sys, termios # import termios now or else you'll get the Unix version on the Mac + def __call__(self): import sys, tty, termios fd = sys.stdin.fileno() @@ -27,6 +34,7 @@ class _GetchUnix: termios.tcsetattr(fd, termios.TCSADRAIN, old_settings) return ch + class _GetchWindows: def __init__(self): import msvcrt @@ -35,6 +43,7 @@ class _GetchWindows: import msvcrt return msvcrt.getch() + class _GetchMacCarbon: """ A function which returns the current ASCII key that is down; @@ -42,13 +51,14 @@ class _GetchMacCarbon: page http://www.mactech.com/macintosh-c/chap02-1.html was very helpful in figuring out how to do this. """ + def __init__(self): from Carbon import Evt - Evt #see if it has this (in Unix, it doesn't) + Evt # see if it has this (in Unix, it doesn't) def __call__(self): from Carbon import Evt - if Evt.EventAvail(0x0008)[0] == 0: # 0x0008 is the keyDownMask + if Evt.EventAvail(0x0008)[0] == 0: # 0x0008 is the keyDownMask return '' else: # @@ -62,9 +72,10 @@ class _GetchMacCarbon: # (what, msg, when, where, mod) = Evt.GetNextEvent(0x0008)[1] return chr(msg & 0x000000FF) -getch = _Getch() +getch = _Getch() + from os.path import join from os import getenv from optparse import OptionParser @@ -76,7 +87,7 @@ def main(): from os.path import dirname, exists, join path = os.getcwd() - #os.chroot(path) + # os.chroot(path) sys.argv = sys.argv[:1] hg_branch = os.popen("hg branches").readlines() @@ -87,10 +98,10 @@ def main(): if i and i.find(":") > 0: name = i.split(" ", 1) t.append(map(str.strip, name)) - print "\n" + print("\n") for pos, i in enumerate(t, 1): - print "%s\t%s\t%s"%(str(pos).ljust(3), i[0].ljust(35), i[1]) - num = raw_input("\nUPDATE TO : ").strip() + print("%s\t%s\t%s" % (str(pos).ljust(3), i[0].ljust(35), i[1])) + num = input("\nUPDATE TO : ").strip() if num.isdigit(): num = int(num) - 1 if num < 0: @@ -99,22 +110,18 @@ def main(): branch = t[num][0] cmd = """ hg update %s - """%branch - line_break = "-"*64 + """ % branch + line_break = "-" * 64 cmd = [i.strip() for i in cmd.split("\n") if i.strip()] - print line_break + print(line_break) import os success = True for i in cmd: - print ">>>%s"%i + print(">>>%s" % i) if os.system(i) != 0: - success = False + success = False return - - - if __name__ == "__main__": main() - diff --git a/z42/print_ac_route b/z42/print_ac_route index 2c34795..bf1e1c5 100755 --- a/z42/print_ac_route +++ b/z42/print_ac_route @@ -1,18 +1,18 @@ #!/usr/bin/env python -#coding:utf-8 - +# coding:utf-8 +from __future__ import print_function import zapp.ANGELCRUNCH.view._url from zapp.ANGELCRUNCH.view._route import ROUTE_LIST from zapp.ANGELCRUNCH.model.const import CID -CID2NAME = dict((k,v) for v,k in CID.__dict__.iteritems() if v[0]!="_") +CID2NAME = dict((k, v) for v, k in CID.__dict__.iteritems() if v[0] != "_") for route in reversed(ROUTE_LIST): - if isinstance(route.host, basestring): - print route.host.replace("\\.",".") + if isinstance(route.host, (basestring, str)): + print(route.host.replace("\\.", ".")) else: - print CID2NAME[route.host.cid] + print(CID2NAME[route.host.cid]) for i in route.handlers: - path , cls, params = i - print "\t %s %s.%s"%(path.ljust(35), cls.__module__, cls.__name__) - print "" + path, cls, params = i + print("\t %s %s.%s" % (path.ljust(35), cls.__module__, cls.__name__)) + print("") diff --git a/z42/print_route b/z42/print_route index 5b220db..bf2bcec 100755 --- a/z42/print_route +++ b/z42/print_route @@ -1,19 +1,20 @@ #!/usr/bin/env python -#coding:utf-8 +# coding:utf-8 +from __future__ import print_function -import zapp.ANGELCRUNCH.view._url from zapp.ANGELCRUNCH.view._route import ROUTE_LIST + # from zapp.TECH2IPO.model.const import CID # CID2NAME = dict((k,v) for v,k in CID.__dict__.iteritems() if v[0]!="_") for route in reversed(ROUTE_LIST): - if isinstance(route.host, basestring): - print route.host.replace("\\.",".") + if isinstance(route.host, (basestring, str)): + print(route.host.replace("\\.", ".")) else: pass # print CID2NAME[route.host.cid] for i in route.handlers: - path , cls, params = i - print "\t %s %s.%s"%(path.ljust(35), cls.__module__, cls.__name__) - print "" + path, cls, params = i + print("\t %s %s.%s" % (path.ljust(35), cls.__module__, cls.__name__)) + print("") diff --git a/z42/replace_line.py b/z42/replace_line.py index 52c2fca..548c41d 100644 --- a/z42/replace_line.py +++ b/z42/replace_line.py @@ -1,5 +1,6 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +from __future__ import print_function from os.path import abspath, dirname, basename, join, exists from os import walk @@ -19,7 +20,8 @@ def replace(from_string, to_string, suffix): _replace(from_s.strip(), to_s.strip(), suffix) -def _replace(from_string, to_string, suffix=('py', 'htm', 'txt', 'conf', 'css', 'h', 'template', 'js', 'html', 'rst', 'coffee', 'yaml', 'mako', 'sh', 'wsgi')): +def _replace(from_string, to_string, suffix=( +'py', 'htm', 'txt', 'conf', 'css', 'h', 'template', 'js', 'html', 'rst', 'coffee', 'yaml', 'mako', 'sh', 'wsgi')): from_string = from_string.strip() to_string = to_string.strip() @@ -50,7 +52,6 @@ def _replace(from_string, to_string, suffix=('py', 'htm', 'txt', 'conf', 'css', f.write(t) - replace( FROM_STRING, TO_STRING, diff --git a/zip/Crack_the_compressed_package.py b/zip/Crack_the_compressed_package.py new file mode 100644 index 0000000..253c803 --- /dev/null +++ b/zip/Crack_the_compressed_package.py @@ -0,0 +1,46 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +import queue +import zipfile +import itertools +from concurrent.futures import ThreadPoolExecutor + + +class BoundedThreadPoolExecutor(ThreadPoolExecutor): + def __init__(self, max_workers=None, thread_name_prefix=''): + super().__init__(max_workers, thread_name_prefix) + self._work_queue = queue.Queue(self._max_workers * 2) # 设置队列大小 + + +# https://mp.weixin.qq.com/s/2db4JBWamaH2EtxRVn_6iA +def extract(file, password): + if not flag: return + file.extractall(path='.', pwd=''.join(password).encode('utf-8')) + + +def result(f): + exception = f.exception() + if not exception: + # 如果获取不到异常说明破解成功 + print('密码为：', f.pwd) + global flag + flag = False + + +if __name__ == '__main__': + # 创建一个标志用于判断密码是否破解成功 + flag = True + # 创建一个线程池 + pool = BoundedThreadPoolExecutor(100) + nums = [str(i) for i in range(10)] + chrs = [chr(i) for i in range(65, 91)] + # 生成数字+字母的6位数密码 + password_lst = itertools.permutations(nums + chrs, 6) + # 创建文件句柄 + zfile = zipfile.ZipFile("加密文件.zip", 'r') + for pwd in password_lst: + if not flag: break + f = pool.submit(extract, zfile, pwd) + f.pwd = pwd + f.pool = pool + f.add_done_callback(result) diff --git a/zip/pack_zipfile.py b/zip/pack_zipfile.py new file mode 100644 index 0000000..6550687 --- /dev/null +++ b/zip/pack_zipfile.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# @Time : 2022/2/26 16:55 +# @Author : 一叶知秋 +# @File : pack_zipfile.py +# @Software: PyCharm +from __future__ import print_function +import os +import sys +import zipfile +import logging + +if sys.version_info[0] == 2: + input = raw_input +else: + input = input +formatter = '%(asctime)s %(levelname)s %(filename)s[%(lineno)d]: %(message)s' +logging.basicConfig(level=logging.DEBUG, format=formatter, + datefmt='%Y:%m:%d %H:%M:%S', encoding='utf-8') + + +def make_zip_file(filename, sourcefolder): + """ + make zip file + :param filename: zip file name + :param sourceFolder: source file folder + :return: + """ + with zipfile.ZipFile(filename, mode='w', compression=zipfile.ZIP_DEFLATED) as zip_file: + for root, dirs, files in os.walk(sourcefolder): + for file in files: + fullpath = os.path.join(root, file) + logging.info('filename is %s', fullpath) + arcname = fullpath.replace(sourcefolder, '') + logging.info('arcname is %s', arcname) + zip_file.write(fullpath, arcname) + + +def main(): + sourceFolder = input('请输入源文件路径目录：\n') + filename = input('请输入压缩文件名字： \n') + logging.info('source file dir: %s', sourceFolder) + logging.info('zip file name: %s', filename) + make_zip_file(filename, sourceFolder) + + +if __name__ == '__main__': + main()