From 315373e5b1fbd9ccc834675a896e86083772d9e0 Mon Sep 17 00:00:00 2001 From: Balaji Alwar Date: Thu, 5 Sep 2024 16:56:59 -0700 Subject: [PATCH] Splitting the notebook to multiple notebooks to speed up voila dashboard rendering --- .../quarto/course_visualization_fromlogs.html | 750 + .../quarto/course_visualization_fromlogs.qmd | 512 + .../nbgitpuller_processing_visualization.qmd | 957 + .../voila_logs_processing_bigquery.ipynb | 512 + .../voila_logs_visualization_modular.ipynb | 15972 ++++++++++++++++ 5 files changed, 18703 insertions(+) create mode 100644 dashboard/quarto/course_visualization_fromlogs.html create mode 100644 dashboard/quarto/course_visualization_fromlogs.qmd create mode 100644 dashboard/quarto/nbgitpuller_processing_visualization.qmd create mode 100644 dashboard/voila/voila_logs_processing_bigquery.ipynb create mode 100644 dashboard/voila/voila_logs_visualization_modular.ipynb diff --git a/dashboard/quarto/course_visualization_fromlogs.html b/dashboard/quarto/course_visualization_fromlogs.html new file mode 100644 index 0000000..7fe8714 --- /dev/null +++ b/dashboard/quarto/course_visualization_fromlogs.html @@ -0,0 +1,750 @@ + + + + + + + + + + +Analytics for courses using Datahub during fall 2023 + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ +
+
+
+
+
Number of nbgitpuller link clicks in a semester categorized by different hubs
+
+
+ +
+
+ + + + + +
+
+
+
Percentage of nbgitpuller link clicks recorded in the semester categorized by hubs
+
+
+ +
+
+ + + + + +
+
+
+
+
+
+
Count of file type across different Github repositories
+
+
+ +
+
+ + + + + +
+
+
+
Number of clicks recorded every semester
+
+
+ +
+
+ + + + + +
+
+
+
+
+
+
+ +
+
+
+ +
+
+ + + + + +
+
+
+
Hubs usage categorized by Dates
+
+
+
Text(0.5, 1.0, 'Usage by Date')
+
+
+
Text(0.5, 0, 'Date')
+
+
+
Text(0, 0.5, 'Frequency')
+
+
+ +
+
+ + + + + +
+
+
+
+
+
+
Month by month usage frequency
+
+
+ +
+
+
+
+
Text(0.5, 1.0, 'Aug Usage Frequency')
+
+
+
Text(0.5, 0, 'Date')
+
+
+
Text(0, 0.5, 'Frequency')
+
+
+
Text(0.5, 1.0, 'Sep Usage Frequency')
+
+
+
+
+
Text(0.5, 0, 'Date')
+
+
+
Text(0, 0.5, 'Frequency')
+
+
+
Text(0.5, 1.0, 'Oct Usage Frequency')
+
+
+
Text(0.5, 0, 'Date')
+
+
+
+
+
Text(0, 0.5, 'Frequency')
+
+
+
Text(0.5, 1.0, 'Nov Usage Frequency')
+
+
+
Text(0.5, 0, 'Date')
+
+
+
Text(0, 0.5, 'Frequency')
+
+
+
+
+ +
+
+
+
+ + + + + +
+
+
+
Hubs usage across different times
+
+
+ +
+
+ + + + + +
+
+
+
+
+
+
Count of clicks from non github sources across hubs
+
+
+ +
+
+ + + + + +
+
+
+
Percentage of clicks from non github sources across hubs
+
+
+ +
+
+ + + + + +
+
+
+
+
+
+
Count of file types from non-Github repositories
+
+
+ +
+
+ + + + + +
+
+
+
Clicks from non Github repositories categorized by semester
+
+
+ +
+
+ + + + + +
+
+
+
+
+
+
Count of clicks from different course repositories
+
+
+ +
+
+ + + + + +
+ +
+
+
+ + + +
+ + + + + + + \ No newline at end of file diff --git a/dashboard/quarto/course_visualization_fromlogs.qmd b/dashboard/quarto/course_visualization_fromlogs.qmd new file mode 100644 index 0000000..0c45aab --- /dev/null +++ b/dashboard/quarto/course_visualization_fromlogs.qmd @@ -0,0 +1,512 @@ +--- +title: Analytics for courses using Datahub during fall 2023 +author: Joanna Yang (Analysis, Viz) and Balaji Alwar (Dashboarding) with support from Ryan Lovett +jupyter: python3 +format: + dashboard: + logo: images/datahublogo.svg + nav-buttons: [github] + theme: lumen + scrolling: true +server: shiny +--- + +## Row +### Column + +```{python} +#| editable: true +#| slideshow: {slide_type: ''} +#| tags: [] +#| title: Number of nbgitpuller link clicks in a semester categorized by different hubs +# importing all packages +#!pip install pandas +#!pip install matplotlib +#!pip install shiny +import pandas as pd +import gzip +import re +from urllib.parse import urlparse, parse_qsl, unquote +import os +import matplotlib.pyplot as plt +import matplotlib.dates as mdates +import math +import numpy as np +import datetime +#%matplotlib inline +#!conda env list +nbgitpuller_filename = 'nbgitpuller-clicks-fall-2023.jsonl.gz' +nbgitpuller_df = pd.read_json(gzip.open(nbgitpuller_filename), lines = True) +#nbgitpuller_df = pd.read_json("nbgitpuller-clicks-fall-2023.jsonl") +#nbgitpuller_df.head() +# obtaining substring after GET and before the redirection +urls_all = nbgitpuller_df.textPayload.apply(lambda x: x[x.find('GET')+3:x.find('->')].strip()) + +# uses urllib.parse to parse the url into path and query +urls_parsed_all = urls_all.apply(lambda x: urlparse(x)) + +# uses the parsed urls to obtain the action from the path +nbgitpuller_df['actions'] = urls_parsed_all.apply(lambda x: os.path.basename(x.path)) +#nbgitpuller_df.actions.unique() +def path_extension_puller(row): + """ + pandas row function; uses apply + function to pull out select file extensions and urlpaths + """ + row_dict = dict(row) + if 'urlpath' in row_dict: + key = 'urlpath' + elif 'subPath' in row_dict: + key = 'subPath' + else: + return 'NaN', 'NaN' + + # files that the analysis is interested in + file_extension_list = ['ipyn[b]?', 'Rmd', 'pdf', 'txt', 'xml', 'ini', 'csv', 'py', 'R', 'md'] + if len(row_dict[key].split('.')) > 1: + file_extension_split_string = row_dict[key].split('.')[-1] + for file_extension in file_extension_list: + if (len(re.findall(file_extension, file_extension_split_string)) > 0): + return row_dict[key], re.findall(file_extension, file_extension_split_string)[-1] + else: + return row_dict[key], 'NaN' + else: + return row_dict[key], 'NaN' + +def get_repo(row): + """ + pandas row function; uses apply + returns repo url from parsed url + """ + for item in row: + key, value = item + if 'repo' in key: + return unquote(value) + return 'NaN' + +def repo_parsing(row): + """ + pandas row function; uses apply + parses the repo url so that it obtains the user and folder/content user is accessing + """ + if row: + if len(row[0].split('/')) > 2: + return row[0].split('/')[1] + else: + return row[0].split('/')[-1] + else: + return 'NaN' + +# makes a new dataframe that only contains git-pull and resets index +nbgitpuller_df_pull = nbgitpuller_df[nbgitpuller_df.actions == 'git-pull'].reset_index() + +# obtains all the log info +log_info_pull = nbgitpuller_df_pull.textPayload.apply(lambda x: ''.join(re.findall("\[.*\]", x)).replace('[', '').replace(']', '').split(' ')) + +# retreives the hubs for each textpayload +hub_source_pull = nbgitpuller_df_pull.resource.apply(lambda x: x['labels']['namespace_name']) + +# obtains substring after GET and before the redirection +urls_pull = nbgitpuller_df_pull.textPayload.apply(lambda x: x[x.find('GET')+3:x.find('->')].strip()) + +# uses urllib.parse to parse the url into path and query +urls_parsed_pull = urls_pull.apply(lambda x: urlparse(x)) + +# uses parsed urls to obtain the action as a quality check +actions_pull = urls_parsed_pull.apply(lambda x: os.path.basename(x.path)) + +# breaks apart the parsed query into repo/urlpath +urls_queries_pull = urls_parsed_pull.apply(lambda x: parse_qsl(x.query)) + +# getting the file type from urlpath +path_extension_pull = urls_queries_pull.apply(path_extension_puller) + +# gets repo urls from the parsed url +repos_pull = urls_queries_pull.apply(get_repo) + +# extract ones that have github.com in the repo url or else its a null value +repos_parsed_pull = repos_pull.apply(lambda x: re.findall("github\.com/+(.+)", x) if x else 'NaN') + +# obtains the user and git content from github.com repo urls +git_user_pull = repos_parsed_pull.apply(lambda x: x[0].split('/')[0] if x else 'NaN') +git_user_repo_pull = repos_parsed_pull.apply(repo_parsing) + +# adds it all into a dataframe +nbgitpuller_textPayload_df_pull = pd.DataFrame({'log_info_type': log_info_pull.apply(lambda x: x[0]), + 'timestamp_date': log_info_pull.apply(lambda x: x[1]), + 'timestamp_time': log_info_pull.apply(lambda x: x[2]), + 'action': actions_pull, + 'git_query': urls_queries_pull, + 'repo': repos_pull, + 'git_user_content': repos_parsed_pull, + 'git_user': git_user_pull, + 'git_content': git_user_repo_pull, + 'git_path': path_extension_pull.apply(lambda x: x[0]), + 'file_extension': path_extension_pull.apply(lambda x: x[1]), + 'hub': hub_source_pull}) +nbgitpuller_textPayload_df_pull['git_user_content_path'] = nbgitpuller_textPayload_df_pull.apply(lambda x: ''.join(x['git_user_content']) + '/' + ''.join(x['git_path']), axis = 1) +def course_assigner_regex(row): + """ + pandas row function; uses apply + determines which classes and semesters are for each github repo + """ + courses = {'(data8|ds8)': 'data8', '(ds100|data100)': 'data100', '(prob140)': 'data140', #data + '(caldataeng|data101|ds101)': 'data101', '(data6|ds6)': 'data6', '(data102|ds102)': 'data102', #data + '(data4ac|ds4ac)': 'data4ac', '(data198|ds198)': 'data198', + '(cs189|compsci189)': 'compsci189', '(cs170|compsci170)': 'compsci170', #compsci + '(ee16a|eecs16a)': 'eecs16a', '(ee16b|eecs16b)': 'eecs16b', '(eecs127)': 'eecs127',#eecs + '(ee120|eleng120)': 'eleng120', #electrical engineering + '(physics111b)': 'physics111b', '(physics88)': 'physics88', # physics + '(polsci3|ps3|polisci3)': 'polsci3', '(polsci5|ps5)': 'polsci5', '(polsci88|ps88)': 'polsci88', '(ps109|polsci109)': 'polsci109', # polisci + '(ce190|civeng90)': 'civgeng190', '(ce93|civeng93)': 'civeng93', '(ce200b|civeng200b)': 'civeng200b', '(ce110|civeng110)': 'civeng110', #civileng + '(envecon118|eep118)': 'envecon118', '(eep147|envecon147)': 'envecon147', '(eep153|envecon153)': 'envecon153', #environmental + 'ph[w]?142': 'pbhlth142', 'ph[w]?251': 'pbhlth251', 'ph[w]?290': 'pbhlth290', 'ph[w]?252': 'pbhlth252', 'ph[w]?253': 'pbhlth253', 'pbhlth250c': 'pbhlth250c', + 'ph[w]?196': 'pbhlth196', # public health + 'mcb163l': 'mcellbi163l', 'mcb280': 'mcellbi280', 'mcbc117': 'mcellbic117', 'mcb32': 'mcellbi32', 'mcb288': 'mcellbi288', #molecular cell bio + '(bio1b|biology1b)': 'biology1b', # biology + 'stat88': 'stat88', 'stat157': 'stat157', 'stat159': 'stat159', 'stat131': 'stat131', 'stat135': 'stat135', 'stat20': 'stat20', + 'stat150': 'stat150', #stat + 'math124': 'math124', #math + '(demog180)': 'demog180', 'demog[c]?175': 'demog175', #demography + '(eps130)': 'eps130', '(eps88)': 'eps88', 'eps256': 'eps256', 'eps24': 'eps24', + '(econ140)': 'econ140', '(econ148)': 'econ148', 'econ141': 'econ141', 'econ172': 'econ172', 'econ151': 'econ151', #econ + 'econ157': 'econ157', 'econ130': 'econ130', 'econ143': 'econ143', 'econ135': 'econ135', + '(rbridge)': 'datasci_rbridge', '(midsw241)': 'datasci241', '(midsw203)': 'datasci203', #datasci + '(legal123|legalst123)': 'legalst123', '(legalst190|legal190)': 'legalst190', # legal + '(es22ac|ethstd22ac)': 'ethstd22ac', '(esc164a|ethstdc164a)': 'ethstdc164a', '(es21ac|ethstd21ac)': 'ethstd21ac', # ethnic studies + 'cp201b': 'cyplan201b', '(cityplanning88|cp88)': 'cyplan88', + 'ib120': 'integbi120', 'ibc32': 'integbi32', 'ib134l': 'integbi134l', + 'mse104l': 'matsci104l', + 'are212': 'aresec212', + 'educw142': 'educw142', + '(cogscic131|psych123)': 'cogscic131', 'psych198': 'psych198', + 'anth[ro]?115': 'anthro115', + 'espmc167': 'espmc167', '(ibespm105)': 'espmc105', + 'ls88': 'ls88', + 'dighum101': 'dighum101', 'dighum160': 'dighum160', + 'plantbi135': 'plantbi135', + 'hist160': 'history160', + 'soc88': 'sociol88', 'sw282': 'socwel282', + 'music30': 'music30', 'artw23ac': 'artw23ac'} + # hard coded + git_content_user = {'danielabrahamgit120': 'eleng120', 'evalencialopezw142': 'educw142', 'charismasacey[A-Za-z0-9]+cp201': 'cp201a'} + + #strips anything thats not a letter or number + git_string_cleaned = re.sub(r'[^a-zA-Z0-9]', '', ''.join(row)).lower() + for key in courses: + if re.findall(key, git_string_cleaned): + return courses[key] + for key in git_content_user: + if re.findall(key, git_string_cleaned): + return git_content_user[key] + else: + return 'unknown' + +# assigns classes/courses to each log +nbgitpuller_textPayload_df_pull['course'] = nbgitpuller_textPayload_df_pull.git_user_content_path.apply(course_assigner_regex) + +def semester_assigner_regex(row): + """ + pandas row function; uses apply + returns the semester of the course material if known + """ + semester = [r'fa[ll]*\d{1,4}', r'su[mmer]*\d{1,4}', r'sp[ring]*\d{1,4}', r'\d{1,4}fa[ll]', r'\d{1,4}su[mmer]*', r'\d{1,4}sp[ring]*'] + sem_match_dict = {'sp': 'spring', 'fa': 'fall', 'su':'summer'} + + git_string_cleaned = re.sub(r'[^a-zA-Z0-9]', '', ''.join(row)).lower() + + year_range = [2018, datetime.datetime.now().year] + + for sem in semester: + try: + if re.findall(sem, git_string_cleaned): + sem_match = re.findall(sem, git_string_cleaned)[-1] + sem_match_split = re.split('(\d+)', sem_match) + sem_char = re.findall('[a-z]+', sem_match)[-1] + sem_year = re.findall('[0-9]+', sem_match)[-1] + for key, value in sem_match_dict.items(): + if key in sem_char and sem_match_split[-1] == '': + if len(sem_year) < 4: + if year_range[0] <= int(f'20{sem_year[-2:]}') <= year_range[1]: + return f'{value}20{sem_year[-2:]}' + else: + return + elif len(sem_year) == 4: + if year_range[0] <= int(sem_year) <= year_range[1]: + return f'{value}{sem_year}' + else: + return 'unknown' + elif key in sem_char and sem_match_split[-1] != '': + if year_range[0] <= int(sem_year) <= year_range[1]: + return f'{value}{sem_year}' + else: + return 'unknown' + except Exception as e: + print(f"Failed findall: {e=} {sem=} {git_string_cleaned=}") + continue + else: + return 'unknown' +#| scrolled: true +# assigns a semester to each log +nbgitpuller_textPayload_df_pull['semester'] = nbgitpuller_textPayload_df_pull.git_user_content_path.apply(semester_assigner_regex) +# transforms timestamp into one and converts from UTC to PST +nbgitpuller_textPayload_df_pull['timestamp_date_time_pst'] = pd.to_datetime(nbgitpuller_textPayload_df_pull.timestamp_date + ' ' + nbgitpuller_textPayload_df_pull.timestamp_time) - pd.Timedelta(8, unit = 'h') +# for ones that have NaN as their filetype, check if git_path contains r_studio +nbgitpuller_textPayload_df_pull['file_extension'] = nbgitpuller_textPayload_df_pull.apply(lambda x: 'rstudio' if 'rstudio' in x['git_path'] else x['file_extension'], axis = 1) +# determines if the links are github or non-github +nbgitpuller_textPayload_df_pull['abnormal'] = nbgitpuller_textPayload_df_pull.repo.apply(lambda x: 'N' if 'github.com' in x else 'Y') +#nbgitpuller_textPayload_df_pull.head() +# separates abnormal repos +nbgitpuller_textPayload_df_pull_abnormal = nbgitpuller_textPayload_df_pull[nbgitpuller_textPayload_df_pull.abnormal == 'Y'] +nbgitpuller_textPayload_df_pull_normal = nbgitpuller_textPayload_df_pull[nbgitpuller_textPayload_df_pull.abnormal == 'N'] +# clicks by hub for normal +clicks_by_hub = nbgitpuller_textPayload_df_pull_normal.hub.value_counts() +fig = plt.figure(figsize = (8,4)) +plt.bar(clicks_by_hub.index, clicks_by_hub) +plt.xlabel('Hub') +plt.ylabel('Count') +plt.xticks(rotation = 90) +plt.title('Clicks by Hub (Github Repos)'); +``` + +### Column +```{python} +#| title: Percentage of nbgitpuller link clicks recorded in the semester categorized by hubs +# clicks by hub pie chart +# only take the top 8 and then add the rest +clicks_by_hub_top8 = dict(clicks_by_hub[:8]) +fig = plt.figure() +cm = plt.get_cmap('tab20') +num_colors = 9 +clicks_by_hub_top8['others'] = clicks_by_hub[8:].sum() +clicks_by_hub_top8_df = pd.DataFrame(clicks_by_hub_top8.items(), columns = ['hub', 'counts']) +#plt.title('Clicks By Hub (Percentage) - Github Repos', fontsize = 14, color = 'k') +plt.pie(clicks_by_hub_top8_df.counts, labels = clicks_by_hub_top8_df.hub, autopct='%1.0f%%', textprops={'fontsize': 12, 'color': 'k'}, labeldistance= 1.05, colors= [cm(i) for i in range(num_colors)]); +#fig.savefig("images/hub_pie.png", transparent = True) +``` + +## Row +### Column +```{python} +#| title: Count of file type across different Github repositories +# filetypes across hubs +hubs = nbgitpuller_textPayload_df_pull_normal.hub.unique() +total_plots = len(hubs) +total_columns = 4 +total_rows = math.ceil(total_plots/total_columns) + +for k in range(total_plots): + hub_file_filter_count = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.hub == hubs[k]].file_extension.value_counts().tolist() + hub_file_filter_proportion = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.hub == hubs[k]].file_extension.value_counts(normalize = True).mul(100).round(2).tolist() + hub_file_filter_key = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.hub == hubs[k]].file_extension.value_counts().keys().tolist() + hub_file_filter_df = pd.DataFrame(data = {'file_type': hub_file_filter_key, 'counts': hub_file_filter_count, 'proportions': hub_file_filter_proportion}) + hub_file_filter_df['count_proportion'] = hub_file_filter_df['counts'].astype(str) + ' (' + hub_file_filter_df['proportions'].astype(str) + ')' + hub_file_filter_df.drop(columns = ['counts', 'proportions'], inplace = True) +# print(hubs[k]) +# print(hub_file_filter_df) + +# for ax in axes.flatten(): +# if not ax.get_visible(): +# ax.set_axis_off() +# filetypes; removed NaN counts; github repos +file_types_count = nbgitpuller_textPayload_df_pull_normal.file_extension[nbgitpuller_textPayload_df_pull_normal.file_extension != 'NaN'].value_counts() +fig = plt.figure(figsize=(8,4)) +plt.bar(file_types_count.index, file_types_count) +plt.xticks(rotation=90) +plt.xlabel('File Type') +plt.ylabel('Counts') +plt.title('File Type By Counts (Github Repos)'); + +``` + +### Column +```{python} +#| title: "Number of clicks recorded every semester" +# looking at the semester usage +sem_count = nbgitpuller_textPayload_df_pull_normal.semester.value_counts() +figure = plt.figure(figsize = (8,4)) +plt.bar(sem_count.index, sem_count) +plt.xticks(rotation = 90, fontsize = 6) +plt.xlabel('Semester') +plt.ylabel('Count') +plt.title('Semester Content Count'); +``` + +## Row +### Column +```{python} +#| title: "% of clicks recorded every semester" +# looking at what times people access the hubs +fig = plt.figure() +num_colors = 4 +cm = plt.get_cmap('tab10') +sem_top7 = dict(sem_count[:3]) +sem_top7['others'] = sem_count[3:].sum() +sem_top7_df = pd.DataFrame(sem_top7.items(), columns = ['semester', 'counts']) +#plt.title('Semester Content - Github Repos', fontsize = 14); +plt.pie(sem_top7_df.counts, labels = sem_top7_df.semester, autopct='%1.0f%%', textprops={'fontsize': 12}, labeldistance= 1.05, + colors= [cm(i) for i in range(num_colors)]); +``` + +### Column +```{python} +#| title: "Hubs usage categorized by Dates" +date_usage = pd.DataFrame(nbgitpuller_textPayload_df_pull_normal.groupby(by = 'timestamp_date').timestamp_date.count()) +date_usage.rename(columns = {'timestamp_date': 'count'}, inplace = True) +date_usage = date_usage.reset_index() +fig,ax = plt.subplots() +ax.xaxis.set_major_locator(mdates.MonthLocator()) +ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) +ax.spines['bottom'].set_color('w') +ax.spines['top'].set_color('w') +ax.spines['left'].set_color('w') +ax.spines['right'].set_color('w') +ax.tick_params(axis ='x', colors = 'w') +ax.tick_params(axis ='y', colors = 'w') +plt.title('Usage by Date', color = 'w', fontsize = 12) +plt.xlabel('Date', color = 'w', fontsize = 12) +plt.ylabel('Frequency', color = 'w', fontsize = 12) +plt.bar(date_usage.timestamp_date, date_usage['count'], color = 'w') +#fig.savefig("images/usage_bar.png",bbox_inches='tight', transparent = True) +``` + +## Row +### Column +```{python} +#| title: "Month by month usage frequency" +#| layout-ncol: 4 +date_usage['timestamp_date'] = date_usage.timestamp_date.astype('datetime64[ns]') +date_usage['month'] = date_usage['timestamp_date'].apply(lambda x: x.month) +date_usage['day'] = date_usage['timestamp_date'].apply(lambda x: x.day) +unique_months = date_usage['month'].unique() +month_list = {1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun', 7:'Jul', 8:'Aug', 9:'Sep', 10:'Oct', 11:'Nov', 12:'Dec'} + +total_plots = len(unique_months) +total_columns = 2 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(10, 10)) + +for k in range(total_plots): + month = date_usage[date_usage['month'] == unique_months[k]].reset_index() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.set_title(f'{month_list[unique_months[k]]} Usage Frequency') + ax.set_xlabel('Date') + ax.set_ylabel('Frequency') + ax.bar(month['day'], month['count']); +#axes[2,1].set_axis_off() +#plt.tight_layout() +#nbgitpuller_textPayload_df_pull_normal.head() +``` +### Column +```{python} +#| title: "Hubs usage across different times" +# looks at date of usage +nbgitpuller_textPayload_df_pull_normal['timestamp_time'] = pd.to_datetime(nbgitpuller_textPayload_df_pull_normal['timestamp_time'], format = '%H:%M:%S.%f') +nbgitpuller_textPayload_df_pull_normal['timestamp_time_hour'] = nbgitpuller_textPayload_df_pull_normal['timestamp_time'].apply(lambda x: x.hour) +usage_time = nbgitpuller_textPayload_df_pull_normal.groupby(by = 'timestamp_time_hour').timestamp_time_hour.count() +usage_time = pd.DataFrame(usage_time) +usage_time.rename(columns = {'timestamp_time_hour': 'count'}, inplace = True) +usage_time = usage_time.reset_index() +fig,ax = plt.subplots() +plt.bar(usage_time.timestamp_time_hour, usage_time['count'], color = '#003262') +ax.spines['bottom'].set_color('k') +ax.spines['top'].set_color('k') +ax.spines['left'].set_color('k') +ax.spines['right'].set_color('k') +ax.tick_params(axis ='x', colors = 'k') +ax.tick_params(axis ='y', colors = 'k') +plt.title('Usage by Time (UTC)', color = 'k') +plt.xlabel('Hour', color = 'k') +plt.ylabel('Frequency', color = 'k'); +#fig.savefig("images/time_usage_bar.png",bbox_inches='tight', transparent = True) +``` +## Row +### Column + +```{python} +#| editable: true +#| slideshow: {slide_type: ''} +#| tags: [] +#| title: "Count of clicks from non github sources across hubs" +# clicks by hub for abnormal +clicks_by_hub = nbgitpuller_textPayload_df_pull_abnormal.hub.value_counts() +fig = plt.figure(figsize = (8,4)) +plt.bar(clicks_by_hub.index, clicks_by_hub) +plt.xlabel('Hub') +plt.ylabel('Count') +plt.xticks(rotation = 90) +plt.title('Count of clicks from non github sources across hubs'); +#len(nbgitpuller_textPayload_df_pull.hub.unique()) +``` + +### Column + +```{python} +#| title: "Percentage of clicks from non github sources across hubs" +clicks_by_hub_top6 = dict(clicks_by_hub[:6]) +clicks_by_hub_top6['others'] = clicks_by_hub[6:].sum() +clicks_by_hub_top6_df = pd.DataFrame(clicks_by_hub_top6.items(), columns = ['hub', 'counts']) +#plt.title('Clicks By Hub (Percentage) - Non-Github Repos'); +plt.pie(clicks_by_hub_top6_df.counts, labels = clicks_by_hub_top6_df.hub, autopct='%1.0f%%', textprops={'fontsize': 9}, labeldistance= 1.05); +``` +## Row +### Column + +```{python} +#| title: "Count of file types from non-Github repositories" +file_types_count = nbgitpuller_textPayload_df_pull_abnormal.file_extension[nbgitpuller_textPayload_df_pull_abnormal.file_extension != 'NaN'].value_counts() +fig = plt.figure(figsize=(8,4)) +plt.bar(file_types_count.index, file_types_count) +plt.xticks(rotation=90) +plt.xlabel('File Type') +plt.ylabel('Counts') +plt.title('Count of file types from non-Github repositories'); +``` +### Column +```{python} +#| title: "Clicks from non Github repositories categorized by semester" +# filetypes across hubs +hubs = nbgitpuller_textPayload_df_pull_abnormal.hub.unique() +total_plots = len(hubs) +total_columns = 4 +total_rows = math.ceil(total_plots/total_columns) + +for k in range(total_plots): + hub_file_filter_count = nbgitpuller_textPayload_df_pull_abnormal[nbgitpuller_textPayload_df_pull_abnormal.hub == hubs[k]].file_extension.value_counts().tolist() + hub_file_filter_proportion = nbgitpuller_textPayload_df_pull_abnormal[nbgitpuller_textPayload_df_pull_abnormal.hub == hubs[k]].file_extension.value_counts(normalize = True).mul(100).round(2).tolist() + hub_file_filter_key = nbgitpuller_textPayload_df_pull_abnormal[nbgitpuller_textPayload_df_pull_abnormal.hub == hubs[k]].file_extension.value_counts().keys().tolist() + hub_file_filter_df = pd.DataFrame(data = {'file_type': hub_file_filter_key, 'counts': hub_file_filter_count, 'proportions': hub_file_filter_proportion}) + hub_file_filter_df['count_proportion'] = hub_file_filter_df['counts'].astype(str) + ' (' + hub_file_filter_df['proportions'].astype(str) + ')' + hub_file_filter_df.drop(columns = ['counts', 'proportions'], inplace = True) +# print(hubs[k]) +# print(hub_file_filter_df) + +# for ax in axes.flatten(): +# if not ax.get_visible(): +# ax.set_axis_off() + +# looking at the semester usage +sem_count = nbgitpuller_textPayload_df_pull_abnormal.semester.value_counts() +figure = plt.figure(figsize=(8, 4)) +plt.bar(sem_count.index, sem_count) +plt.xticks(rotation=90, fontsize=6) +plt.xlabel('Semester') +plt.ylabel('Count') +plt.title('Semester Content Count'); +``` + +## Row +### Column + +```{python} +#| title: "Count of clicks from different course repositories" +# looking at the classes +course_count = nbgitpuller_textPayload_df_pull_abnormal.course.value_counts() +figure = plt.figure(figsize = (8,4)) +plt.bar(course_count.index, course_count) +plt.xticks(rotation = 90, fontsize = 6) +plt.xlabel('Course') +plt.ylabel('Count') +plt.title('Count of Clicks by Course'); +``` \ No newline at end of file diff --git a/dashboard/quarto/nbgitpuller_processing_visualization.qmd b/dashboard/quarto/nbgitpuller_processing_visualization.qmd new file mode 100644 index 0000000..564fc08 --- /dev/null +++ b/dashboard/quarto/nbgitpuller_processing_visualization.qmd @@ -0,0 +1,957 @@ +--- +title: All File Types +jupyter: python3 +format: dashboard +server: shiny +--- + +```{r} +library(shiny) +``` + +```{python} +#!pip install pandas +#!pip install matplotlib +#!pip install shiny +``` + +```{python} +#| editable: true +#| slideshow: {slide_type: ''} +#| tags: [] +# importing all packages +import pandas as pd +import gzip +import re +from urllib.parse import urlparse, parse_qsl, unquote +import os +import matplotlib.pyplot as plt +import matplotlib.dates as mdates +import math +import numpy as np +import datetime +#%matplotlib inline +``` + +```{python} +#!conda env list +``` + +```{python} +#| editable: true +#| slideshow: {slide_type: ''} +#| tags: [] +# opens up the nbgitpuller file - uses gzip to open; Please open this file in Stat 20 Hub as it has shared-read drive enabled for admin access +nbgitpuller_filepath = '/home/jovyan/admins-shared/nbgitpuller-clicks-fall-2023.jsonl.gz' +# nbgitpuller_filepath_sp24 = '/home/jovyan/admins-shared/nbgitpuller-clicks-sp24.jsonl.gz' +nbgitpuller_df = pd.read_json(gzip.open(nbgitpuller_filepath), lines = True) +nbgitpuller_df.head() +``` + +```{python} +# obtaining substring after GET and before the redirection +urls_all = nbgitpuller_df.textPayload.apply(lambda x: x[x.find('GET')+3:x.find('->')].strip()) + +# uses urllib.parse to parse the url into path and query +urls_parsed_all = urls_all.apply(lambda x: urlparse(x)) + +# uses the parsed urls to obtain the action from the path +nbgitpuller_df['actions'] = urls_parsed_all.apply(lambda x: os.path.basename(x.path)) +``` + +```{python} +nbgitpuller_df.actions.unique() +``` + +## For git-pull + +```{python} +# function to determine the filetypes +def path_extension_puller(row): + """ + pandas row function; uses apply + function to pull out select file extensions and urlpaths + """ + row_dict = dict(row) + if 'urlpath' in row_dict: + key = 'urlpath' + elif 'subPath' in row_dict: + key = 'subPath' + else: + return 'NaN', 'NaN' + + # files that the analysis is interested in + file_extension_list = ['ipyn[b]?', 'Rmd', 'pdf', 'txt', 'xml', 'ini', 'csv', 'py', 'R', 'md'] + if len(row_dict[key].split('.')) > 1: + file_extension_split_string = row_dict[key].split('.')[-1] + for file_extension in file_extension_list: + if (len(re.findall(file_extension, file_extension_split_string)) > 0): + return row_dict[key], re.findall(file_extension, file_extension_split_string)[-1] + else: + return row_dict[key], 'NaN' + else: + return row_dict[key], 'NaN' + +def get_repo(row): + """ + pandas row function; uses apply + returns repo url from parsed url + """ + for item in row: + key, value = item + if 'repo' in key: + return unquote(value) + return 'NaN' + +def repo_parsing(row): + """ + pandas row function; uses apply + parses the repo url so that it obtains the user and folder/content user is accessing + """ + if row: + if len(row[0].split('/')) > 2: + return row[0].split('/')[1] + else: + return row[0].split('/')[-1] + else: + return 'NaN' +``` + +```{python} +# makes a new dataframe that only contains git-pull and resets index +nbgitpuller_df_pull = nbgitpuller_df[nbgitpuller_df.actions == 'git-pull'].reset_index() + +# obtains all the log info +log_info_pull = nbgitpuller_df_pull.textPayload.apply(lambda x: ''.join(re.findall("\[.*\]", x)).replace('[', '').replace(']', '').split(' ')) + +# retreives the hubs for each textpayload +hub_source_pull = nbgitpuller_df_pull.resource.apply(lambda x: x['labels']['namespace_name']) + +# obtains substring after GET and before the redirection +urls_pull = nbgitpuller_df_pull.textPayload.apply(lambda x: x[x.find('GET')+3:x.find('->')].strip()) + +# uses urllib.parse to parse the url into path and query +urls_parsed_pull = urls_pull.apply(lambda x: urlparse(x)) + +# uses parsed urls to obtain the action as a quality check +actions_pull = urls_parsed_pull.apply(lambda x: os.path.basename(x.path)) + +# breaks apart the parsed query into repo/urlpath +urls_queries_pull = urls_parsed_pull.apply(lambda x: parse_qsl(x.query)) + +# getting the file type from urlpath +path_extension_pull = urls_queries_pull.apply(path_extension_puller) + +# gets repo urls from the parsed url +repos_pull = urls_queries_pull.apply(get_repo) + +# extract ones that have github.com in the repo url or else its a null value +repos_parsed_pull = repos_pull.apply(lambda x: re.findall("github\.com/+(.+)", x) if x else 'NaN') + +# obtains the user and git content from github.com repo urls +git_user_pull = repos_parsed_pull.apply(lambda x: x[0].split('/')[0] if x else 'NaN') +git_user_repo_pull = repos_parsed_pull.apply(repo_parsing) + +# adds it all into a dataframe +nbgitpuller_textPayload_df_pull = pd.DataFrame({'log_info_type': log_info_pull.apply(lambda x: x[0]), + 'timestamp_date': log_info_pull.apply(lambda x: x[1]), + 'timestamp_time': log_info_pull.apply(lambda x: x[2]), + 'action': actions_pull, + 'git_query': urls_queries_pull, + 'repo': repos_pull, + 'git_user_content': repos_parsed_pull, + 'git_user': git_user_pull, + 'git_content': git_user_repo_pull, + 'git_path': path_extension_pull.apply(lambda x: x[0]), + 'file_extension': path_extension_pull.apply(lambda x: x[1]), + 'hub': hub_source_pull}) +``` + +```{python} +nbgitpuller_textPayload_df_pull['git_user_content_path'] = nbgitpuller_textPayload_df_pull.apply(lambda x: ''.join(x['git_user_content']) + '/' + ''.join(x['git_path']), axis = 1) +``` + +```{python} +def course_assigner_regex(row): + """ + pandas row function; uses apply + determines which classes and semesters are for each github repo + """ + courses = {'(data8|ds8)': 'data8', '(ds100|data100)': 'data100', '(prob140)': 'data140', #data + '(caldataeng|data101|ds101)': 'data101', '(data6|ds6)': 'data6', '(data102|ds102)': 'data102', #data + '(data4ac|ds4ac)': 'data4ac', '(data198|ds198)': 'data198', + '(cs189|compsci189)': 'compsci189', '(cs170|compsci170)': 'compsci170', #compsci + '(ee16a|eecs16a)': 'eecs16a', '(ee16b|eecs16b)': 'eecs16b', '(eecs127)': 'eecs127',#eecs + '(ee120|eleng120)': 'eleng120', #electrical engineering + '(physics111b)': 'physics111b', '(physics88)': 'physics88', # physics + '(polsci3|ps3|polisci3)': 'polsci3', '(polsci5|ps5)': 'polsci5', '(polsci88|ps88)': 'polsci88', '(ps109|polsci109)': 'polsci109', # polisci + '(ce190|civeng90)': 'civgeng190', '(ce93|civeng93)': 'civeng93', '(ce200b|civeng200b)': 'civeng200b', '(ce110|civeng110)': 'civeng110', #civileng + '(envecon118|eep118)': 'envecon118', '(eep147|envecon147)': 'envecon147', '(eep153|envecon153)': 'envecon153', #environmental + 'ph[w]?142': 'pbhlth142', 'ph[w]?251': 'pbhlth251', 'ph[w]?290': 'pbhlth290', 'ph[w]?252': 'pbhlth252', 'ph[w]?253': 'pbhlth253', 'pbhlth250c': 'pbhlth250c', + 'ph[w]?196': 'pbhlth196', # public health + 'mcb163l': 'mcellbi163l', 'mcb280': 'mcellbi280', 'mcbc117': 'mcellbic117', 'mcb32': 'mcellbi32', 'mcb288': 'mcellbi288', #molecular cell bio + '(bio1b|biology1b)': 'biology1b', # biology + 'stat88': 'stat88', 'stat157': 'stat157', 'stat159': 'stat159', 'stat131': 'stat131', 'stat135': 'stat135', 'stat20': 'stat20', + 'stat150': 'stat150', #stat + 'math124': 'math124', #math + '(demog180)': 'demog180', 'demog[c]?175': 'demog175', #demography + '(eps130)': 'eps130', '(eps88)': 'eps88', 'eps256': 'eps256', 'eps24': 'eps24', + '(econ140)': 'econ140', '(econ148)': 'econ148', 'econ141': 'econ141', 'econ172': 'econ172', 'econ151': 'econ151', #econ + 'econ157': 'econ157', 'econ130': 'econ130', 'econ143': 'econ143', 'econ135': 'econ135', + '(rbridge)': 'datasci_rbridge', '(midsw241)': 'datasci241', '(midsw203)': 'datasci203', #datasci + '(legal123|legalst123)': 'legalst123', '(legalst190|legal190)': 'legalst190', # legal + '(es22ac|ethstd22ac)': 'ethstd22ac', '(esc164a|ethstdc164a)': 'ethstdc164a', '(es21ac|ethstd21ac)': 'ethstd21ac', # ethnic studies + 'cp201b': 'cyplan201b', '(cityplanning88|cp88)': 'cyplan88', + 'ib120': 'integbi120', 'ibc32': 'integbi32', 'ib134l': 'integbi134l', + 'mse104l': 'matsci104l', + 'are212': 'aresec212', + 'educw142': 'educw142', + '(cogscic131|psych123)': 'cogscic131', 'psych198': 'psych198', + 'anth[ro]?115': 'anthro115', + 'espmc167': 'espmc167', '(ibespm105)': 'espmc105', + 'ls88': 'ls88', + 'dighum101': 'dighum101', 'dighum160': 'dighum160', + 'plantbi135': 'plantbi135', + 'hist160': 'history160', + 'soc88': 'sociol88', 'sw282': 'socwel282', + 'music30': 'music30', 'artw23ac': 'artw23ac'} + # hard coded + git_content_user = {'danielabrahamgit120': 'eleng120', 'evalencialopezw142': 'educw142', 'charismasacey[A-Za-z0-9]+cp201': 'cp201a'} + + #strips anything thats not a letter or number + git_string_cleaned = re.sub(r'[^a-zA-Z0-9]', '', ''.join(row)).lower() + for key in courses: + if re.findall(key, git_string_cleaned): + return courses[key] + for key in git_content_user: + if re.findall(key, git_string_cleaned): + return git_content_user[key] + else: + return 'unknown' +``` + +```{python} +# assigns classes/courses to each log +nbgitpuller_textPayload_df_pull['course'] = nbgitpuller_textPayload_df_pull.git_user_content_path.apply(course_assigner_regex) +``` + +```{python} +def semester_assigner_regex(row): + """ + pandas row function; uses apply + returns the semester of the course material if known + """ + semester = [r'fa[ll]*\d{1,4}', r'su[mmer]*\d{1,4}', r'sp[ring]*\d{1,4}', r'\d{1,4}fa[ll]', r'\d{1,4}su[mmer]*', r'\d{1,4}sp[ring]*'] + sem_match_dict = {'sp': 'spring', 'fa': 'fall', 'su':'summer'} + + git_string_cleaned = re.sub(r'[^a-zA-Z0-9]', '', ''.join(row)).lower() + + year_range = [2018, datetime.datetime.now().year] + + for sem in semester: + try: + if re.findall(sem, git_string_cleaned): + sem_match = re.findall(sem, git_string_cleaned)[-1] + sem_match_split = re.split('(\d+)', sem_match) + sem_char = re.findall('[a-z]+', sem_match)[-1] + sem_year = re.findall('[0-9]+', sem_match)[-1] + for key, value in sem_match_dict.items(): + if key in sem_char and sem_match_split[-1] == '': + if len(sem_year) < 4: + if year_range[0] <= int(f'20{sem_year[-2:]}') <= year_range[1]: + return f'{value}20{sem_year[-2:]}' + else: + return + elif len(sem_year) == 4: + if year_range[0] <= int(sem_year) <= year_range[1]: + return f'{value}{sem_year}' + else: + return 'unknown' + elif key in sem_char and sem_match_split[-1] != '': + if year_range[0] <= int(sem_year) <= year_range[1]: + return f'{value}{sem_year}' + else: + return 'unknown' + except Exception as e: + print(f"Failed findall: {e=} {sem=} {git_string_cleaned=}") + continue + else: + return 'unknown' +``` + +```{python} +#| scrolled: true +# assigns a semester to each log +nbgitpuller_textPayload_df_pull['semester'] = nbgitpuller_textPayload_df_pull.git_user_content_path.apply(semester_assigner_regex) +``` + +```{python} +# transforms timestamp into one and converts from UTC to PST +nbgitpuller_textPayload_df_pull['timestamp_date_time_pst'] = pd.to_datetime(nbgitpuller_textPayload_df_pull.timestamp_date + ' ' + nbgitpuller_textPayload_df_pull.timestamp_time) - pd.Timedelta(8, unit = 'h') +``` + +```{python} +# for ones that have NaN as their filetype, check if git_path contains r_studio +nbgitpuller_textPayload_df_pull['file_extension'] = nbgitpuller_textPayload_df_pull.apply(lambda x: 'rstudio' if 'rstudio' in x['git_path'] else x['file_extension'], axis = 1) +``` + +```{python} +# determines if the links are github or non-github +nbgitpuller_textPayload_df_pull['abnormal'] = nbgitpuller_textPayload_df_pull.repo.apply(lambda x: 'N' if 'github.com' in x else 'Y') +``` + +```{python} +nbgitpuller_textPayload_df_pull.head() +``` + +```{python} +# separates abnormal repos +nbgitpuller_textPayload_df_pull_abnormal = nbgitpuller_textPayload_df_pull[nbgitpuller_textPayload_df_pull.abnormal == 'Y'] +nbgitpuller_textPayload_df_pull_normal = nbgitpuller_textPayload_df_pull[nbgitpuller_textPayload_df_pull.abnormal == 'N'] +``` + +## Graph and Visualizations + +### Ideas for Visualizations +1. Clicks by hub +2. What times people are accessing the hubs +3. File types across hubs +4. Semester usage of hubs + +### Github Repos + +```{python} +# clicks by hub for normal +clicks_by_hub = nbgitpuller_textPayload_df_pull_normal.hub.value_counts() +fig = plt.figure(figsize = (8,4)) +plt.bar(clicks_by_hub.index, clicks_by_hub) +plt.xlabel('Hub') +plt.ylabel('Count') +plt.xticks(rotation = 90) +plt.title('Clicks by Hub (Github Repos)'); +``` + +```{python} +# clicks by hub pie chart +# only take the top 8 and then add the rest +clicks_by_hub_top8 = dict(clicks_by_hub[:8]) +fig = plt.figure() +cm = plt.get_cmap('tab20') +num_colors = 9 +clicks_by_hub_top8['others'] = clicks_by_hub[8:].sum() +clicks_by_hub_top8_df = pd.DataFrame(clicks_by_hub_top8.items(), columns = ['hub', 'counts']) +plt.pie(clicks_by_hub_top8_df.counts, labels = clicks_by_hub_top8_df.hub, autopct='%1.0f%%', textprops={'fontsize': 12, 'color': 'k'}, labeldistance= 1.05, colors= [cm(i) for i in range(num_colors)]) +plt.title('Clicks By Hub (Percentage) - Github Repos', fontsize = 14, color = 'k') +``` + +```{python} +fig.savefig("images/hub_pie.png", transparent = True) +``` + +```{python} +# filetypes; removed NaN counts; github repos +file_types_count = nbgitpuller_textPayload_df_pull_normal.file_extension[nbgitpuller_textPayload_df_pull_normal.file_extension != 'NaN'].value_counts() +fig = plt.figure(figsize=(8,4)) +plt.bar(file_types_count.index, file_types_count) +plt.xticks(rotation=90) +plt.xlabel('File Type') +plt.ylabel('Counts') +plt.title('File Type By Counts (Github Repos)'); +``` + +```{python} +# filetypes across hubs +hubs = nbgitpuller_textPayload_df_pull_normal.hub.unique() +total_plots = len(hubs) +total_columns = 4 +total_rows = math.ceil(total_plots/total_columns) + +for k in range(total_plots): + hub_file_filter_count = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.hub == hubs[k]].file_extension.value_counts().tolist() + hub_file_filter_proportion = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.hub == hubs[k]].file_extension.value_counts(normalize = True).mul(100).round(2).tolist() + hub_file_filter_key = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.hub == hubs[k]].file_extension.value_counts().keys().tolist() + hub_file_filter_df = pd.DataFrame(data = {'file_type': hub_file_filter_key, 'counts': hub_file_filter_count, 'proportions': hub_file_filter_proportion}) + hub_file_filter_df['count_proportion'] = hub_file_filter_df['counts'].astype(str) + ' (' + hub_file_filter_df['proportions'].astype(str) + ')' + hub_file_filter_df.drop(columns = ['counts', 'proportions'], inplace = True) + print(hubs[k]) + print(hub_file_filter_df) + +# for ax in axes.flatten(): +# if not ax.get_visible(): +# ax.set_axis_off() +``` + +```{python} +# looking at the classes +course_count = nbgitpuller_textPayload_df_pull_normal.course.value_counts() +figure = plt.figure(figsize = (10,8)) +plt.bar(course_count.index, course_count) +plt.xticks(rotation = 90, fontsize = 6) +plt.xlabel('Course') +plt.ylabel('Count') +plt.title('Count of Clicks by Course'); +``` + +```{python} +# top 10 of the courses +course_count.head(10) +``` + +```{python} +fig = plt.figure() +num_colors = 11 +cm = plt.get_cmap('tab20') +courses_top10 = dict(course_count[:10]) +courses_top10['others'] = course_count[10:].sum() +courses_top10_df = pd.DataFrame(courses_top10.items(), columns = ['course', 'counts']) +plt.pie(courses_top10_df.counts, labels = courses_top10_df.course, autopct='%1.0f%%', textprops={'fontsize': 8}, labeldistance= 1.05, + colors= [cm(i) for i in range(num_colors)]) +plt.title('Counts of Clicks by Course - Github Repos', fontsize = 14); +``` + +```{python} +fig.savefig("images/course_pie.png",bbox_inches='tight', transparent = True) +``` + +```{python} +courses_count_by_hub = nbgitpuller_textPayload_df_pull_normal.groupby('hub').course.value_counts() +courses_count_by_hub.to_csv('courses_count_by_hub.csv') +``` + +```{python} +# looking at the semester usage +sem_count = nbgitpuller_textPayload_df_pull_normal.semester.value_counts() +figure = plt.figure(figsize = (8,4)) +plt.bar(sem_count.index, sem_count) +plt.xticks(rotation = 90, fontsize = 6) +plt.xlabel('Semester') +plt.ylabel('Count') +plt.title('Semester Content Count'); +``` + +```{python} +# looking at what times people access the hubs +fig = plt.figure() +num_colors = 4 +cm = plt.get_cmap('tab10') +sem_top7 = dict(sem_count[:3]) +sem_top7['others'] = sem_count[3:].sum() +sem_top7_df = pd.DataFrame(sem_top7.items(), columns = ['semester', 'counts']) +plt.pie(sem_top7_df.counts, labels = sem_top7_df.semester, autopct='%1.0f%%', textprops={'fontsize': 12}, labeldistance= 1.05, + colors= [cm(i) for i in range(num_colors)]) +plt.title('Semester Content - Github Repos', fontsize = 14); +``` + +```{python} +# looks at date of usage +date_usage = pd.DataFrame(nbgitpuller_textPayload_df_pull_normal.groupby(by = 'timestamp_date').timestamp_date.count()) +date_usage.rename(columns = {'timestamp_date': 'count'}, inplace = True) +date_usage = date_usage.reset_index() +fig,ax = plt.subplots() +plt.bar(date_usage.timestamp_date, date_usage['count'], color = 'w') +ax.xaxis.set_major_locator(mdates.MonthLocator()) +ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) +ax.spines['bottom'].set_color('w') +ax.spines['top'].set_color('w') +ax.spines['left'].set_color('w') +ax.spines['right'].set_color('w') +ax.tick_params(axis ='x', colors = 'w') +ax.tick_params(axis ='y', colors = 'w') +plt.title('Usage by Date', color = 'w', fontsize = 12) +plt.xlabel('Date', color = 'w', fontsize = 12) +plt.ylabel('Frequency', color = 'w', fontsize = 12); +``` + +```{python} +fig.savefig("images/usage_bar.png",bbox_inches='tight', transparent = True) +``` + +```{python} +date_usage['timestamp_date'] = date_usage.timestamp_date.astype('datetime64[ns]') +date_usage['month'] = date_usage['timestamp_date'].apply(lambda x: x.month) +date_usage['day'] = date_usage['timestamp_date'].apply(lambda x: x.day) +unique_months = date_usage['month'].unique() +month_list = {1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun', 7:'Jul', 8:'Aug', 9:'Sep', 10:'Oct', 11:'Nov', 12:'Dec'} + +total_plots = len(unique_months) +total_columns = 2 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(10, 10)) + +for k in range(total_plots): + month = date_usage[date_usage['month'] == unique_months[k]].reset_index() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(month['day'], month['count']) + + ax.set_title(f'{month_list[unique_months[k]]} Usage Frequency') + ax.set_xlabel('Date') + ax.set_ylabel('Frequency') + +#axes[2,1].set_axis_off() +plt.tight_layout() +``` + +```{python} +nbgitpuller_textPayload_df_pull_normal.head() +``` + +```{python} +# looks at date of usage +nbgitpuller_textPayload_df_pull_normal['timestamp_time'] = pd.to_datetime(nbgitpuller_textPayload_df_pull_normal['timestamp_time'], format = '%H:%M:%S.%f') +nbgitpuller_textPayload_df_pull_normal['timestamp_time_hour'] = nbgitpuller_textPayload_df_pull_normal['timestamp_time'].apply(lambda x: x.hour) +usage_time = nbgitpuller_textPayload_df_pull_normal.groupby(by = 'timestamp_time_hour').timestamp_time_hour.count() +usage_time = pd.DataFrame(usage_time) +usage_time.rename(columns = {'timestamp_time_hour': 'count'}, inplace = True) +usage_time = usage_time.reset_index() +fig,ax = plt.subplots() +plt.bar(usage_time.timestamp_time_hour, usage_time['count'], color = '#003262') +ax.spines['bottom'].set_color('k') +ax.spines['top'].set_color('k') +ax.spines['left'].set_color('k') +ax.spines['right'].set_color('k') +ax.tick_params(axis ='x', colors = 'k') +ax.tick_params(axis ='y', colors = 'k') +plt.title('Usage by Time (UTC)', color = 'k') +plt.xlabel('Hour', color = 'k') +plt.ylabel('Frequency', color = 'k'); +``` + +```{python} +fig.savefig("images/time_usage_bar.png",bbox_inches='tight', transparent = True) +``` + +##### Making Graphs of Date of Usage Per Course + +```{python} +# look at usage by courses 1-15 +unique_courses = nbgitpuller_textPayload_df_pull_normal.course.unique() + +# length of unique_courses is 93, plotting 15 plots at a time +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k]].reset_index() + courses_date = course.groupby(by = 'timestamp_date').timestamp_date.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_date.index, courses_date) + + ax.set_title(f'{unique_courses[k]} Usage Frequency') + ax.set_xlabel('Date') + ax.set_ylabel('Frequency') + ax.xaxis.set_major_locator(mdates.MonthLocator()) + ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +plt.tight_layout() +``` + +```{python} +# look at plots 16-30 +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+15]].reset_index() + courses_date = course.groupby(by = 'timestamp_date').timestamp_date.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_date.index, courses_date) + + ax.set_title(f'{unique_courses[k+15]} Usage Frequency') + ax.set_xlabel('Date') + ax.set_ylabel('Frequency') + ax.xaxis.set_major_locator(mdates.MonthLocator()) + ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +# axes[2,1].set_axis_off() +plt.tight_layout() +``` + +```{python} +# look at plots 31-45 +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+30]].reset_index() + courses_date = course.groupby(by = 'timestamp_date').timestamp_date.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_date.index, courses_date) + + ax.set_title(f'{unique_courses[k+30]} Usage Frequency') + ax.set_xlabel('Date') + ax.set_ylabel('Frequency') + ax.xaxis.set_major_locator(mdates.MonthLocator()) + ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +# axes[2,1].set_axis_off() +plt.tight_layout() +``` + +```{python} +#| editable: true +#| slideshow: {slide_type: ''} +#| tags: [] +# look at plots 45-60 +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+45]].reset_index() + courses_date = course.groupby(by = 'timestamp_date').timestamp_date.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_date.index, courses_date) + + ax.set_title(f'{unique_courses[k+45]} Usage Frequency') + ax.set_xlabel('Date') + ax.set_ylabel('Frequency') + ax.xaxis.set_major_locator(mdates.MonthLocator()) + ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +# axes[2,1].set_axis_off() +plt.tight_layout() +``` + +# look at plots 61-75 +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+60]].reset_index() + courses_date = course.groupby(by = 'timestamp_date').timestamp_date.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_date.index, courses_date) + + ax.set_title(f'{unique_courses[k+60]} Usage Frequency') + ax.set_xlabel('Date') + ax.set_ylabel('Frequency') + ax.xaxis.set_major_locator(mdates.MonthLocator()) + ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +# axes[2,1].set_axis_off() +plt.tight_layout() + +# look at plots 76-90 +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+75]].reset_index() + courses_date = course.groupby(by = 'timestamp_date').timestamp_date.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_date.index, courses_date) + + ax.set_title(f'{unique_courses[k+75]} Usage Frequency') + ax.set_xlabel('Date') + ax.set_ylabel('Frequency') + ax.xaxis.set_major_locator(mdates.MonthLocator()) + ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +# axes[2,1].set_axis_off() +plt.tight_layout() + +# look at plots 91-93 +total_plots = 3 +total_columns = 3 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(10, 3)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+90]].reset_index() + courses_date = course.groupby(by = 'timestamp_date').timestamp_date.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_date.index, courses_date) + + ax.set_title(f'{unique_courses[k+90]} Usage Frequency') + ax.set_xlabel('Date') + ax.set_ylabel('Frequency') + ax.xaxis.set_major_locator(mdates.MonthLocator()) + ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +# axes[2,1].set_axis_off() +plt.tight_layout() + +##### Making Graphs of Time of Usage Per Course + +# look at usage time by courses 1-15 +unique_courses = nbgitpuller_textPayload_df_pull_normal.course.unique() + + +# length of unique_courses is 93, plotting 15 plots at a time +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15), sharex = False) +plt.setp(axes, xlim=(-1,24)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k]].reset_index() + course['timestamp_time'] = pd.to_datetime(course['timestamp_time'], format = '%H:%M:%S.%f') + course['timestamp_time_hour'] = course['timestamp_time'].apply(lambda x: x.hour) + courses_time = course.groupby(by = 'timestamp_time_hour').timestamp_time_hour.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_time.index, courses_time) + + ax.set_title(f'{unique_courses[k]} Time Usage Frequency') + ax.set_xlabel('Time') + ax.set_ylabel('Frequency') + # ax.xaxis.set_major_locator(mdates.MonthLocator()) + # ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +plt.tight_layout() + +# look at usage time by course 16-30 +# length of unique_courses is 93, plotting 15 plots at a time +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15)) +plt.setp(axes, xlim=(-1,24)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+15]].reset_index() + course['timestamp_time'] = pd.to_datetime(course['timestamp_time'], format = '%H:%M:%S.%f') + course['timestamp_time_hour'] = course['timestamp_time'].apply(lambda x: x.hour) + courses_time = course.groupby(by = 'timestamp_time_hour').timestamp_time_hour.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_time.index, courses_time) + + ax.set_title(f'{unique_courses[k+15]} Time Usage Frequency') + ax.set_xlabel('Time') + ax.set_ylabel('Frequency') + # ax.xaxis.set_major_locator(mdates.MonthLocator()) + # ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +plt.tight_layout() + +# look at usage time by course 31 - 45 +# length of unique_courses is 93, plotting 15 plots at a time +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15)) +plt.setp(axes, xlim=(-1,24)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+30]].reset_index() + course['timestamp_time'] = pd.to_datetime(course['timestamp_time'], format = '%H:%M:%S.%f') + course['timestamp_time_hour'] = course['timestamp_time'].apply(lambda x: x.hour) + courses_time = course.groupby(by = 'timestamp_time_hour').timestamp_time_hour.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_time.index, courses_time) + + ax.set_title(f'{unique_courses[k+30]} Time Usage Frequency') + ax.set_xlabel('Time') + ax.set_ylabel('Frequency') + # ax.xaxis.set_major_locator(mdates.MonthLocator()) + # ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +plt.tight_layout() + +# look at usage time by course 46 - 60 +# length of unique_courses is 93, plotting 15 plots at a time +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15)) +plt.setp(axes, xlim=(-1,24)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+45]].reset_index() + course['timestamp_time'] = pd.to_datetime(course['timestamp_time'], format = '%H:%M:%S.%f') + course['timestamp_time_hour'] = course['timestamp_time'].apply(lambda x: x.hour) + courses_time = course.groupby(by = 'timestamp_time_hour').timestamp_time_hour.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_time.index, courses_time) + + ax.set_title(f'{unique_courses[k+45]} Time Usage Frequency') + ax.set_xlabel('Time') + ax.set_ylabel('Frequency') + # ax.xaxis.set_major_locator(mdates.MonthLocator()) + # ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +plt.tight_layout() + +# look at usage time by course 61 - 75 +# length of unique_courses is 93, plotting 15 plots at a time +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15)) +plt.setp(axes, xlim=(-1,24)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+60]].reset_index() + course['timestamp_time'] = pd.to_datetime(course['timestamp_time'], format = '%H:%M:%S.%f') + course['timestamp_time_hour'] = course['timestamp_time'].apply(lambda x: x.hour) + courses_time = course.groupby(by = 'timestamp_time_hour').timestamp_time_hour.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_time.index, courses_time) + + ax.set_title(f'{unique_courses[k+60]} Time Usage Frequency') + ax.set_xlabel('Time') + ax.set_ylabel('Frequency') + # ax.xaxis.set_major_locator(mdates.MonthLocator()) + # ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +plt.tight_layout() + +# look at usage time by course 76 - 90 +# length of unique_courses is 93, plotting 15 plots at a time +total_plots = 15 +total_columns = 5 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(25, 15)) +plt.setp(axes, xlim=(-1,24)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+75]].reset_index() + course['timestamp_time'] = pd.to_datetime(course['timestamp_time'], format = '%H:%M:%S.%f') + course['timestamp_time_hour'] = course['timestamp_time'].apply(lambda x: x.hour) + courses_time = course.groupby(by = 'timestamp_time_hour').timestamp_time_hour.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_time.index, courses_time) + + ax.set_title(f'{unique_courses[k+75]} Time Usage Frequency') + ax.set_xlabel('Time') + ax.set_ylabel('Frequency') + # ax.xaxis.set_major_locator(mdates.MonthLocator()) + # ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +plt.tight_layout() + +# look at usage time by course 91-83 +# length of unique_courses is 93, plotting 15 plots at a time +total_plots = 3 +total_columns = 3 +total_rows = int(np.ceil(total_plots/total_columns)) + +fig, axes = plt.subplots(total_rows, total_columns, figsize=(10, 5)) +plt.setp(axes, xlim=(-1,24)) + +for k in range(total_plots): + course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal.course == unique_courses[k+90]].reset_index() + course['timestamp_time'] = pd.to_datetime(course['timestamp_time'], format = '%H:%M:%S.%f') + course['timestamp_time_hour'] = course['timestamp_time'].apply(lambda x: x.hour) + courses_time = course.groupby(by = 'timestamp_time_hour').timestamp_time_hour.count() + ax = axes.flatten()[k] if total_plots > 1 else axes # handles single plot cases + ax.bar(courses_time.index, courses_time) + + ax.set_title(f'{unique_courses[k+90]} Time Usage Frequency') + ax.set_xlabel('Time') + ax.set_ylabel('Frequency') + # ax.xaxis.set_major_locator(mdates.MonthLocator()) + # ax.xaxis.set_major_formatter(mdates.DateFormatter('%b')) + +plt.tight_layout() + +### Non-Github Repos + +```{python} +#| editable: true +#| slideshow: {slide_type: ''} +#| tags: [] +# clicks by hub for abnormal +clicks_by_hub = nbgitpuller_textPayload_df_pull_abnormal.hub.value_counts() +fig = plt.figure(figsize = (8,4)) +plt.bar(clicks_by_hub.index, clicks_by_hub) +plt.xlabel('Hub') +plt.ylabel('Count') +plt.xticks(rotation = 90) +plt.title('Clicks By Hub (Non-Github Repos)'); +``` + +```{python} +len(nbgitpuller_textPayload_df_pull.hub.unique()) +``` + +```{python} +clicks_by_hub_top6 = dict(clicks_by_hub[:6]) +clicks_by_hub_top6['others'] = clicks_by_hub[6:].sum() +clicks_by_hub_top6_df = pd.DataFrame(clicks_by_hub_top6.items(), columns = ['hub', 'counts']) +plt.pie(clicks_by_hub_top6_df.counts, labels = clicks_by_hub_top6_df.hub, autopct='%1.0f%%', textprops={'fontsize': 9}, labeldistance= 1.05) +plt.title('Clicks By Hub (Percentage) - Non-Github Repos'); +``` + +```{python} +file_types_count = nbgitpuller_textPayload_df_pull_abnormal.file_extension[nbgitpuller_textPayload_df_pull_abnormal.file_extension != 'NaN'].value_counts() +fig = plt.figure(figsize=(8,4)) +plt.bar(file_types_count.index, file_types_count) +plt.xticks(rotation=90) +plt.xlabel('File Type') +plt.ylabel('Counts') +plt.title('File Type By Counts (Non-Github Repos)'); +``` + +```{python} +# filetypes across hubs +hubs = nbgitpuller_textPayload_df_pull_abnormal.hub.unique() +total_plots = len(hubs) +total_columns = 4 +total_rows = math.ceil(total_plots/total_columns) + +for k in range(total_plots): + hub_file_filter_count = nbgitpuller_textPayload_df_pull_abnormal[nbgitpuller_textPayload_df_pull_abnormal.hub == hubs[k]].file_extension.value_counts().tolist() + hub_file_filter_proportion = nbgitpuller_textPayload_df_pull_abnormal[nbgitpuller_textPayload_df_pull_abnormal.hub == hubs[k]].file_extension.value_counts(normalize = True).mul(100).round(2).tolist() + hub_file_filter_key = nbgitpuller_textPayload_df_pull_abnormal[nbgitpuller_textPayload_df_pull_abnormal.hub == hubs[k]].file_extension.value_counts().keys().tolist() + hub_file_filter_df = pd.DataFrame(data = {'file_type': hub_file_filter_key, 'counts': hub_file_filter_count, 'proportions': hub_file_filter_proportion}) + hub_file_filter_df['count_proportion'] = hub_file_filter_df['counts'].astype(str) + ' (' + hub_file_filter_df['proportions'].astype(str) + ')' + hub_file_filter_df.drop(columns = ['counts', 'proportions'], inplace = True) + print(hubs[k]) + print(hub_file_filter_df) + +# for ax in axes.flatten(): +# if not ax.get_visible(): +# ax.set_axis_off() +``` + +```{python} +# looking at the classes +course_count = nbgitpuller_textPayload_df_pull_abnormal.course.value_counts() +figure = plt.figure(figsize = (8,4)) +plt.bar(course_count.index, course_count) +plt.xticks(rotation = 90, fontsize = 6) +plt.xlabel('Course') +plt.ylabel('Count') +plt.title('Count of Clicks by Course'); +``` + +```{python} +#| editable: true +#| slideshow: {slide_type: ''} +#| tags: [] +# looking at the semester usage +sem_count = nbgitpuller_textPayload_df_pull_abnormal.semester.value_counts() +figure = plt.figure(figsize = (8,4)) +plt.bar(sem_count.index, sem_count) +plt.xticks(rotation = 90, fontsize = 6) +plt.xlabel('Semester') +plt.ylabel('Count') +plt.title('Semester Content Count'); +``` + diff --git a/dashboard/voila/voila_logs_processing_bigquery.ipynb b/dashboard/voila/voila_logs_processing_bigquery.ipynb new file mode 100644 index 0000000..f19309e --- /dev/null +++ b/dashboard/voila/voila_logs_processing_bigquery.ipynb @@ -0,0 +1,512 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "208e01bb-22cf-4059-978a-a5a8dc806c18", + "metadata": {}, + "outputs": [], + "source": [ + "# importing all packages\n", + "import pandas as pd\n", + "import gzip\n", + "import re\n", + "from urllib.parse import urlparse, parse_qsl, unquote\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import math\n", + "import numpy as np\n", + "import datetime \n", + "import ipywidgets as widgets\n", + "from ipywidgets import interact\n", + "from IPython.display import display\n", + "import plotly.graph_objects as go \n", + "import plotly.express as px\n", + "import asyncio\n", + "from plotly.subplots import make_subplots\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f633b44b-6ec8-4fdc-82f8-2d6354857311", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install google-cloud-bigquery\n", + "!pip install pandas-gbq\n", + "\n", + "# Authenticate to GCP in the shell, like this:\n", + "# gcloud auth application-default login\n", + "\n", + "from google.cloud import bigquery\n", + "from pandas.io import gbq\n", + "\n", + "import os\n", + "current_dir = \"/home/jovyan/datahub-usage-analysis/dashboard\"\n", + "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]=current_dir+\"xxx.json\"\n", + "\n", + "# Set up BigQuery client\n", + "project_id = 'ucb-datahub-2018'\n", + "client = bigquery.Client(project=project_id)\n", + "\n", + "# Define SQL query\n", + "# replace the FROM... with the BigQuery table you want to query\n", + "query = \"\"\"\n", + "SELECT *\n", + "FROM `ucb-datahub-2018.datahub_fa24.stderr_*`\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "8491fc76-31af-4d5c-a879-ea930f705505", + "metadata": {}, + "source": [ + "#opens up fall dataset\n", + "nbgitpuller_fall23 = '/home/jovyan/discovery-su24-dataset/nbgitpuller-clicks-fall-2023.jsonl.gz'\n", + "nbgitpuller_fall23 = pd.read_json(gzip.open(nbgitpuller_fall23), lines = True)" + ] + }, + { + "cell_type": "markdown", + "id": "b1bfd652-21b6-41e5-8dcd-234b3dbdfacc", + "metadata": {}, + "source": [ + "# opens up spring dataset\n", + "nbgitpuller_filename = '/home/jovyan/discovery-su24-dataset/nbgitpuller-clicks-sp24.jsonl.gz'\n", + "nbgitpuller_sp24 = pd.read_json(gzip.open(nbgitpuller_filename), lines = True)" + ] + }, + { + "cell_type": "markdown", + "id": "d96dae03-f1fc-4642-a1e3-242674431825", + "metadata": {}, + "source": [ + "# combining both datasets, spring first then fall \n", + "nbgitpuller_df = pd.concat([nbgitpuller_sp24, nbgitpuller_fall23], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbb13502-1721-48a9-bffd-a9d83d0a63eb", + "metadata": {}, + "outputs": [], + "source": [ + "# Execute query and load data into DataFrame\n", + "nbgitpuller_df = gbq.read_gbq(query, project_id=project_id)\n", + "\n", + "nbgitpuller_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5495c05a-cf59-49b1-8485-984d6459f9bf", + "metadata": {}, + "outputs": [], + "source": [ + "nbgitpuller_df.to_csv(\"nbgitpuller_su24.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d901ca85-f6cd-4fb8-8c4c-a265a2b493f7", + "metadata": {}, + "outputs": [], + "source": [ + "# obtaining substring after GET and before the redirection\n", + "urls_all = nbgitpuller_df.textPayload.apply(lambda x: x[x.find('GET')+3:x.find('->')].strip())\n", + "\n", + "# uses urllib.parse to parse the url into path and query\n", + "urls_parsed_all = urls_all.apply(lambda x: urlparse(x))\n", + "\n", + "# uses the parsed urls to obtain the action from the path\n", + "nbgitpuller_df['actions'] = urls_parsed_all.apply(lambda x: os.path.basename(x.path))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ac13f7c-6976-4274-b923-fdb4eacac1f3", + "metadata": {}, + "outputs": [], + "source": [ + "# function to determine the filetypes\n", + "def path_extension_puller(row):\n", + " \"\"\"\n", + " pandas row function; uses apply\n", + " function to pull out select file extensions and urlpaths\n", + " \"\"\"\n", + " row_dict = dict(row)\n", + " if 'urlpath' in row_dict:\n", + " key = 'urlpath'\n", + " elif 'subPath' in row_dict:\n", + " key = 'subPath'\n", + " else: \n", + " return 'NaN', 'NaN'\n", + " \n", + " # files that the analysis is interested in \n", + " file_extension_list = ['ipyn[b]?', 'Rmd', 'pdf', 'txt', 'xml', 'ini', 'csv', 'py', 'R', 'md']\n", + " if len(row_dict[key].split('.')) > 1:\n", + " file_extension_split_string = row_dict[key].split('.')[-1]\n", + " for file_extension in file_extension_list:\n", + " if (len(re.findall(file_extension, file_extension_split_string)) > 0):\n", + " return row_dict[key], re.findall(file_extension, file_extension_split_string)[-1]\n", + " else:\n", + " return row_dict[key], 'NaN'\n", + " else:\n", + " return row_dict[key], 'NaN'\n", + "\n", + "def get_repo(row):\n", + " \"\"\"\n", + " pandas row function; uses apply\n", + " returns repo url from parsed url\n", + " \"\"\"\n", + " for item in row:\n", + " key, value = item\n", + " if 'repo' in key:\n", + " return unquote(value)\n", + " return 'NaN'\n", + "\n", + "def repo_parsing(row):\n", + " \"\"\"\n", + " pandas row function; uses apply\n", + " parses the repo url so that it obtains the user and folder/content user is accessing\n", + " \"\"\"\n", + " if row:\n", + " if len(row[0].split('/')) > 2:\n", + " return row[0].split('/')[1]\n", + " else:\n", + " return row[0].split('/')[-1]\n", + " else:\n", + " return 'NaN'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed1c0236-54bb-44ca-acde-f548e492c95b", + "metadata": {}, + "outputs": [], + "source": [ + "# makes a new dataframe that only contains git-pull and resets index\n", + "nbgitpuller_df_pull = nbgitpuller_df[nbgitpuller_df.actions == 'git-pull'].reset_index()\n", + "\n", + "# obtains all the log info\n", + "log_info_pull = nbgitpuller_df_pull.textPayload.apply(lambda x: ''.join(re.findall(\"\\[.*\\]\", x)).replace('[', '').replace(']', '').split(' '))\n", + "\n", + "# retreives the hubs for each textpayload\n", + "hub_source_pull = nbgitpuller_df_pull.resource.apply(lambda x: x['labels']['namespace_name'])\n", + "\n", + "# obtains substring after GET and before the redirection\n", + "urls_pull = nbgitpuller_df_pull.textPayload.apply(lambda x: x[x.find('GET')+3:x.find('->')].strip())\n", + "\n", + "# uses urllib.parse to parse the url into path and query\n", + "urls_parsed_pull = urls_pull.apply(lambda x: urlparse(x))\n", + "\n", + "# uses parsed urls to obtain the action as a quality check\n", + "actions_pull = urls_parsed_pull.apply(lambda x: os.path.basename(x.path))\n", + "\n", + "# breaks apart the parsed query into repo/urlpath\n", + "urls_queries_pull = urls_parsed_pull.apply(lambda x: parse_qsl(x.query))\n", + "\n", + "# getting the file type from urlpath\n", + "path_extension_pull = urls_queries_pull.apply(path_extension_puller)\n", + "\n", + "# gets repo urls from the parsed url\n", + "repos_pull = urls_queries_pull.apply(get_repo)\n", + "\n", + "# extract ones that have github.com in the repo url or else its a null value\n", + "repos_parsed_pull = repos_pull.apply(lambda x: re.findall(\"github\\.com/+(.+)\", x) if x else 'NaN')\n", + "\n", + "# obtains the user and git content from github.com repo urls\n", + "git_user_pull = repos_parsed_pull.apply(lambda x: x[0].split('/')[0] if x else 'NaN')\n", + "git_user_repo_pull = repos_parsed_pull.apply(repo_parsing)\n", + "\n", + "# adds it all into a dataframe\n", + "nbgitpuller_textPayload_df_pull = pd.DataFrame({'log_info_type': log_info_pull.apply(lambda x: x[0]),\n", + " 'timestamp_date': log_info_pull.apply(lambda x: x[1]),\n", + " 'timestamp_time': log_info_pull.apply(lambda x: x[2]),\n", + " 'action': actions_pull,\n", + " 'git_query': urls_queries_pull,\n", + " 'repo': repos_pull,\n", + " 'git_user_content': repos_parsed_pull,\n", + " 'git_user': git_user_pull,\n", + " 'git_content': git_user_repo_pull,\n", + " 'git_path': path_extension_pull.apply(lambda x: x[0]),\n", + " 'file_extension': path_extension_pull.apply(lambda x: x[1]),\n", + " 'hub': hub_source_pull})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08ac3620-2690-4a12-9ff1-cdfb9665cb1d", + "metadata": {}, + "outputs": [], + "source": [ + "nbgitpuller_textPayload_df_pull['git_user_content_path'] = nbgitpuller_textPayload_df_pull.apply(lambda x: ''.join(x['git_user_content']) + '/' + ''.join(x['git_path']), axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6ea83c1e-e524-44ac-9383-af5494e92b55", + "metadata": {}, + "outputs": [], + "source": [ + "def course_assigner_regex(row):\n", + " \"\"\"\n", + " pandas row function; uses apply\n", + " determines which classes and semesters are for each github repo\n", + " \"\"\"\n", + " courses = {'(data8|ds8)': 'data8', '(ds100|data100)': 'data100', '(prob140)': 'data140', #data\n", + " '(caldataeng|data101|ds101)': 'data101', '(data6|ds6)': 'data6', '(data102|ds102)': 'data102', #data\n", + " '(data4ac|ds4ac)': 'data4ac', '(data198|ds198)': 'data198',\n", + " '(cs189|compsci189)': 'compsci189', '(cs170|compsci170)': 'compsci170', #compsci \n", + " '(ee16a|eecs16a)': 'eecs16a', '(ee16b|eecs16b)': 'eecs16b', '(eecs127)': 'eecs127',#eecs\n", + " '(ee120|eleng120)': 'eleng120', #electrical engineering\n", + " '(physics111b)': 'physics111b', '(physics88)': 'physics88', # physics\n", + " '(polsci3|ps3|polisci3)': 'polsci3', '(polsci5|ps5)': 'polsci5', '(polsci88|ps88)': 'polsci88', '(ps109|polsci109)': 'polsci109', # polisci\n", + " '(ce190|civeng90)': 'civgeng190', '(ce93|civeng93)': 'civeng93', '(ce200b|civeng200b)': 'civeng200b', '(ce110|civeng110)': 'civeng110', #civileng\n", + " '(envecon118|eep118)': 'envecon118', '(eep147|envecon147)': 'envecon147', '(eep153|envecon153)': 'envecon153', #environmental\n", + " 'ph[w]?142': 'pbhlth142', 'ph[w]?251': 'pbhlth251', 'ph[w]?290': 'pbhlth290', 'ph[w]?252': 'pbhlth252', 'ph[w]?253': 'pbhlth253', 'pbhlth250c': 'pbhlth250c',\n", + " 'ph[w]?196': 'pbhlth196', # public health\n", + " 'mcb163l': 'mcellbi163l', 'mcb280': 'mcellbi280', 'mcbc117': 'mcellbic117', 'mcb32': 'mcellbi32', 'mcb288': 'mcellbi288', #molecular cell bio\n", + " '(bio1b|biology1b)': 'biology1b', # biology\n", + " 'stat88': 'stat88', 'stat157': 'stat157', 'stat159': 'stat159', 'stat131': 'stat131', 'stat135': 'stat135', 'stat20': 'stat20', \n", + " 'stat150': 'stat150', #stat\n", + " 'math124': 'math124', #math\n", + " '(demog180)': 'demog180', 'demog[c]?175': 'demog175', #demography\n", + " '(eps130)': 'eps130', '(eps88)': 'eps88', 'eps256': 'eps256', 'eps24': 'eps24',\n", + " '(econ140)': 'econ140', '(econ148)': 'econ148', 'econ141': 'econ141', 'econ172': 'econ172', 'econ151': 'econ151', #econ\n", + " 'econ157': 'econ157', 'econ130': 'econ130', 'econ143': 'econ143', 'econ135': 'econ135',\n", + " '(rbridge)': 'datasci_rbridge', '(midsw241)': 'datasci241', '(midsw203)': 'datasci203', #datasci\n", + " '(legal123|legalst123)': 'legalst123', '(legalst190|legal190)': 'legalst190', # legal\n", + " '(es22ac|ethstd22ac)': 'ethstd22ac', '(esc164a|ethstdc164a)': 'ethstdc164a', '(es21ac|ethstd21ac)': 'ethstd21ac', # ethnic studies\n", + " 'cp201b': 'cyplan201b', '(cityplanning88|cp88)': 'cyplan88', \n", + " 'ib120': 'integbi120', 'ibc32': 'integbi32', 'ib134l': 'integbi134l',\n", + " 'mse104l': 'matsci104l',\n", + " 'are212': 'aresec212',\n", + " 'educw142': 'educw142',\n", + " '(cogscic131|psych123)': 'cogscic131', 'psych198': 'psych198',\n", + " 'anth[ro]?115': 'anthro115',\n", + " 'espmc167': 'espmc167', '(ibespm105)': 'espmc105',\n", + " 'ls88': 'ls88',\n", + " 'dighum101': 'dighum101', 'dighum160': 'dighum160',\n", + " 'plantbi135': 'plantbi135',\n", + " 'hist160': 'history160',\n", + " 'soc88': 'sociol88', 'sw282': 'socwel282',\n", + " 'music30': 'music30', 'artw23ac': 'artw23ac'} \n", + " # hard coded\n", + " git_content_user = {'danielabrahamgit120': 'eleng120', 'evalencialopezw142': 'educw142', 'charismasacey[A-Za-z0-9]+cp201': 'cp201a'}\n", + "\n", + " #strips anything thats not a letter or number\n", + " git_string_cleaned = re.sub(r'[^a-zA-Z0-9]', '', ''.join(row)).lower()\n", + " for key in courses:\n", + " if re.findall(key, git_string_cleaned):\n", + " return courses[key]\n", + " for key in git_content_user:\n", + " if re.findall(key, git_string_cleaned):\n", + " return git_content_user[key]\n", + " else:\n", + " return 'unknown'\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc471691-1af0-4e44-b4cf-2a524d716b9a", + "metadata": {}, + "outputs": [], + "source": [ + "# assigns classes/courses to each log\n", + "nbgitpuller_textPayload_df_pull['course'] = nbgitpuller_textPayload_df_pull.git_user_content_path.apply(course_assigner_regex)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec36a016-32be-4c25-9dda-c73f44781151", + "metadata": {}, + "outputs": [], + "source": [ + "def semester_assigner_regex(row):\n", + " \"\"\"\n", + " pandas row function; uses apply\n", + " returns the semester of the course material if known\n", + " \"\"\"\n", + " semester = [r'fa[ll]*\\d{1,4}', r'su[mmer]*\\d{1,4}', r'sp[ring]*\\d{1,4}', r'\\d{1,4}fa[ll]', r'\\d{1,4}su[mmer]*', r'\\d{1,4}sp[ring]*']\n", + " sem_match_dict = {'sp': 'spring', 'fa': 'fall', 'su':'summer'}\n", + "\n", + " git_string_cleaned = re.sub(r'[^a-zA-Z0-9]', '', ''.join(row)).lower()\n", + "\n", + " year_range = [2018, datetime.datetime.now().year]\n", + "\n", + " for sem in semester:\n", + " try:\n", + " if re.findall(sem, git_string_cleaned):\n", + " sem_match = re.findall(sem, git_string_cleaned)[-1]\n", + " sem_match_split = re.split('(\\d+)', sem_match)\n", + " sem_char = re.findall('[a-z]+', sem_match)[-1]\n", + " sem_year = re.findall('[0-9]+', sem_match)[-1]\n", + " for key, value in sem_match_dict.items():\n", + " if key in sem_char and sem_match_split[-1] == '':\n", + " if len(sem_year) < 4:\n", + " if year_range[0] <= int(f'20{sem_year[-2:]}') <= year_range[1]:\n", + " return f'{value}20{sem_year[-2:]}'\n", + " else:\n", + " return \n", + " elif len(sem_year) == 4:\n", + " if year_range[0] <= int(sem_year) <= year_range[1]:\n", + " return f'{value}{sem_year}'\n", + " else:\n", + " return 'unknown'\n", + " elif key in sem_char and sem_match_split[-1] != '':\n", + " if year_range[0] <= int(sem_year) <= year_range[1]:\n", + " return f'{value}{sem_year}'\n", + " else:\n", + " return 'unknown'\n", + " except Exception as e:\n", + " print(f\"Failed findall: {e=} {sem=} {git_string_cleaned=}\")\n", + " continue\n", + " else:\n", + " return 'unknown'\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bf7b5c5-9c9d-48e8-85d4-e320ee136d3c", + "metadata": {}, + "outputs": [], + "source": [ + "# assigns a semester to each log\n", + "nbgitpuller_textPayload_df_pull['semester'] = nbgitpuller_textPayload_df_pull.git_user_content_path.apply(semester_assigner_regex)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "110d17f4-6bf4-4570-91a4-191adbd2aae0", + "metadata": {}, + "outputs": [], + "source": [ + "# transforms timestamp into one and converts from UTC to PST\n", + "nbgitpuller_textPayload_df_pull['timestamp_date_time_pst'] = pd.to_datetime(nbgitpuller_textPayload_df_pull.timestamp_date + ' ' + nbgitpuller_textPayload_df_pull.timestamp_time) - pd.Timedelta(8, unit = 'h')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fcd4c0d-beb3-4e77-a976-28538a3d6619", + "metadata": {}, + "outputs": [], + "source": [ + "# for ones that have NaN as their filetype, check if git_path contains r_studio\n", + "nbgitpuller_textPayload_df_pull['file_extension'] = nbgitpuller_textPayload_df_pull.apply(lambda x: 'rstudio' if 'rstudio' in x['git_path'] else x['file_extension'], axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "226424f9-db99-4d96-9aec-d48cc2d95256", + "metadata": {}, + "outputs": [], + "source": [ + "# determines if the links are github or non-github\n", + "nbgitpuller_textPayload_df_pull['abnormal'] = nbgitpuller_textPayload_df_pull.repo.apply(lambda x: 'N' if 'github.com' in x else 'Y')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "380d68e6-f1b1-4673-bc6d-48d8ec6334d9", + "metadata": {}, + "outputs": [], + "source": [ + "nbgitpuller_textPayload_df_pull.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f835edc-1a70-4144-9596-e3cfe5f803df", + "metadata": {}, + "outputs": [], + "source": [ + "# separates abnormal repos \n", + "nbgitpuller_textPayload_df_pull_abnormal = nbgitpuller_textPayload_df_pull[nbgitpuller_textPayload_df_pull.abnormal == 'Y']\n", + "nbgitpuller_textPayload_df_pull_normal = nbgitpuller_textPayload_df_pull[nbgitpuller_textPayload_df_pull.abnormal == 'N']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d8ea788-bc77-4d4d-8a92-3c25e040735c", + "metadata": {}, + "outputs": [], + "source": [ + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ec6865b-4f7b-40ce-a89c-cd776ff2e72e", + "metadata": {}, + "outputs": [], + "source": [ + "# Save DataFrame to a pickle file\n", + "with open('nbgitpuller_df_serialized.pkl', 'wb') as file:\n", + " pickle.dump(nbgitpuller_textPayload_df_pull_normal, file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "338d5c79-9e93-464b-815a-ee1a0689f21c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Save DataFrame to a pickle file\n", + "with open('nbgitpuller_df_abnormal_serialized.pkl', 'wb') as file:\n", + " pickle.dump(nbgitpuller_textPayload_df_pull_abnormal, file)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/dashboard/voila/voila_logs_visualization_modular.ipynb b/dashboard/voila/voila_logs_visualization_modular.ipynb new file mode 100644 index 0000000..79d29d8 --- /dev/null +++ b/dashboard/voila/voila_logs_visualization_modular.ipynb @@ -0,0 +1,15972 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "1181f37e-9fe5-4d36-ab34-d6f3abb3891b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# importing all packages\n", + "import pandas as pd\n", + "import gzip\n", + "import re\n", + "from urllib.parse import urlparse, parse_qsl, unquote\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import math\n", + "import numpy as np\n", + "import datetime \n", + "import ipywidgets as widgets\n", + "from ipywidgets import interact\n", + "from IPython.display import display\n", + "import plotly.graph_objects as go \n", + "import plotly.express as px\n", + "import asyncio\n", + "from plotly.subplots import make_subplots\n", + "import pickle\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "75c748c5-8dd6-43a6-9aec-00a1ccbf4af0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove_output", + "\"remove_cell\"" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data loaded successfully!\n", + " log_info_type timestamp_date timestamp_time action \\\n", + "0 I 2024-08-22 03:47:54.326 git-pull \n", + "1 I 2024-08-22 02:19:11.021 git-pull \n", + "2 I 2024-08-22 02:19:26.667 git-pull \n", + "3 I 2024-08-22 02:49:28.938 git-pull \n", + "4 I 2024-08-22 02:52:12.066 git-pull \n", + "\n", + " git_query \\\n", + "0 [(repo, https://github.com/stat20/stat20-assig... \n", + "1 [(repo, https://github.com/data-8/materials-sp... \n", + "2 [(repo, https://github.com/data-8/materials-sp... \n", + "3 [(repo, https://github.com/data-8/materials-su... \n", + "4 [(repo, https://github.com/data-8/materials-sp... \n", + "\n", + " repo git_user_content \\\n", + "0 https://github.com/stat20/stat20-assignments [stat20/stat20-assignments] \n", + "1 https://github.com/data-8/materials-sp24 [data-8/materials-sp24] \n", + "2 https://github.com/data-8/materials-sp24 [data-8/materials-sp24] \n", + "3 https://github.com/data-8/materials-su23 [data-8/materials-su23] \n", + "4 https://github.com/data-8/materials-sp23 [data-8/materials-sp23] \n", + "\n", + " git_user git_content \\\n", + "0 stat20 stat20-assignments \n", + "1 data-8 materials-sp24 \n", + "2 data-8 materials-sp24 \n", + "3 data-8 materials-su23 \n", + "4 data-8 materials-sp23 \n", + "\n", + " git_path file_extension \\\n", + "0 rstudio/ rstudio \n", + "1 tree/materials-sp24/lab/lab01/lab01.ipynb ipynb \n", + "2 tree/materials-sp24/lab/lab01/lab01.ipynb ipynb \n", + "3 retro/tree/materials-su23/materials/lab/lab01/... ipynb \n", + "4 retro/tree/materials-sp23/lec/lec13_empty.ipynb ipynb \n", + "\n", + " hub git_user_content_path course \\\n", + "0 stat20-prod stat20/stat20-assignments/rstudio/ stat20 \n", + "1 data8-prod data-8/materials-sp24/tree/materials-sp24/lab/... data8 \n", + "2 data8-prod data-8/materials-sp24/tree/materials-sp24/lab/... data8 \n", + "3 data8-prod data-8/materials-su23/retro/tree/materials-su2... data8 \n", + "4 data8-prod data-8/materials-sp23/retro/tree/materials-sp2... data8 \n", + "\n", + " semester timestamp_date_time_pst abnormal \n", + "0 unknown 2024-08-21 19:47:54.326 N \n", + "1 spring2024 2024-08-21 18:19:11.021 N \n", + "2 spring2024 2024-08-21 18:19:26.667 N \n", + "3 summer2023 2024-08-21 18:49:28.938 N \n", + "4 spring2023 2024-08-21 18:52:12.066 N \n" + ] + } + ], + "source": [ + "# Load DataFrame from the pickle file\n", + "with open('nbgitpuller_df_serialized.pkl', 'rb') as file:\n", + " nbgitpuller_textPayload_df_pull_normal = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c80c5cdf-82ed-44f0-b6b5-f8ea8e2bfa7c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "\"remove_cell\"" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data loaded successfully!\n", + " log_info_type timestamp_date timestamp_time action \\\n", + "6997 I 2024-08-29 07:12:32.038 git-pull \n", + "8939 I 2024-08-29 02:10:24.631 git-pull \n", + "13685 I 2024-09-01 14:24:46.060 git-pull \n", + "13686 I 2024-09-01 14:24:45.672 git-pull \n", + "13687 I 2024-09-01 14:24:45.290 git-pull \n", + "\n", + " git_query \\\n", + "6997 [(repo, https)] \n", + "8939 [(repo, https:)] \n", + "13685 [(repo, https/hub/login?next=/hub/user-redirec... \n", + "13686 [(repo, https/hub/login?next=/hub/user-redirec... \n", + "13687 [(repo, https)] \n", + "\n", + " repo git_user_content \\\n", + "6997 https [] \n", + "8939 https: [] \n", + "13685 https/hub/login?next=/hub/user-redirect/git-pu... [] \n", + "13686 https/hub/login?next=/hub/user-redirect/git-pu... [] \n", + "13687 https [] \n", + "\n", + " git_user git_content git_path file_extension hub \\\n", + "6997 NaN NaN NaN NaN data101-prod \n", + "8939 NaN NaN NaN NaN publichealth-prod \n", + "13685 NaN NaN NaN NaN data100-prod \n", + "13686 NaN NaN NaN NaN data100-prod \n", + "13687 NaN NaN NaN NaN data100-prod \n", + "\n", + " git_user_content_path course semester timestamp_date_time_pst abnormal \n", + "6997 /NaN unknown unknown 2024-08-28 23:12:32.038 Y \n", + "8939 /NaN unknown unknown 2024-08-28 18:10:24.631 Y \n", + "13685 /NaN unknown unknown 2024-09-01 06:24:46.060 Y \n", + "13686 /NaN unknown unknown 2024-09-01 06:24:45.672 Y \n", + "13687 /NaN unknown unknown 2024-09-01 06:24:45.290 Y \n" + ] + } + ], + "source": [ + "# Load DataFrame from the pickle file\n", + "with open('nbgitpuller_df_abnormal_serialized.pkl', 'rb') as file:\n", + " nbgitpuller_textPayload_df_pull_abnormal = pickle.load(file)\n", + "#print(\"Data loaded successfully!\")\n", + "#print(nbgitpuller_textPayload_df_pull_abnormal.head())" + ] + }, + { + "cell_type": "markdown", + "id": "8aad42ba-131b-4726-8923-a80aba393da8", + "metadata": {}, + "source": [ + "# Overall Visualizations" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6c911d20-f5bb-4210-8b49-8616e98e0cb5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hoverinfo": "x+y", + "hoverlabel": { + "bgcolor": "LightGrey", + "font": { + "size": 16 + } + }, + "textposition": "outside", + "type": "bar", + "x": [ + "data8", + "data100", + "unknown", + "data140", + "pbhlth142", + "data101", + "stat20", + "data102", + "econ140", + "data6", + "civeng93", + "eecs16b", + "eecs16a", + "stat131", + "mcellbi32", + "econ141", + "demog180", + "pbhlth251", + "integbi134l", + "econ148", + "polsci88", + "polsci3", + "stat88", + "envecon118", + "eleng120", + "math124", + "datasci_rbridge", + "eecs127", + "biology1b", + "physics88", + "cyplan88", + "ethstd21ac", + "datasci241", + "matsci104l", + "legalst123", + "cogscic131", + "econ130", + "demog175", + "compsci189", + "legalst190", + "physics111b", + "stat159", + "stat157" + ], + "y": [ + 19170, + 11519, + 7444, + 5608, + 3132, + 3027, + 2982, + 1710, + 1166, + 865, + 736, + 644, + 595, + 574, + 414, + 397, + 375, + 179, + 169, + 159, + 150, + 50, + 47, + 37, + 29, + 28, + 27, + 18, + 15, + 11, + 11, + 9, + 8, + 8, + 7, + 6, + 5, + 4, + 3, + 2, + 1, + 1, + 1 + ] + } + ], + "layout": { + "height": 900, + "hovermode": "x unified", + "paper_bgcolor": "white", + "plot_bgcolor": "white", + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Normal Click Counts By Course" + }, + "width": 1000, + "xaxis": { + "autorange": true, + "range": [ + -0.5, + 42.5 + ], + "tickangle": -90, + "tickfont": { + "size": 9 + }, + "tickmode": "array", + "ticktext": [ + "data8", + "data100", + "unknown", + "data140", + "pbhlth142", + "data101", + "stat20", + "data102", + "econ140", + "data6", + "civeng93", + "eecs16b", + "eecs16a", + "stat131", + "mcellbi32", + "econ141", + "demog180", + "pbhlth251", + "integbi134l", + "econ148", + "polsci88", + "polsci3", + "stat88", + "envecon118", + "eleng120", + "math124", + "datasci_rbridge", + "eecs127", + "biology1b", + "physics88", + "cyplan88", + "ethstd21ac", + "datasci241", + "matsci104l", + "legalst123", + "cogscic131", + "econ130", + "demog175", + "compsci189", + "legalst190", + "physics111b", + "stat159", + "stat157" + ], + "tickvals": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42 + ], + "title": { + "text": "Course" + }, + "type": "category" + }, + "yaxis": { + "autorange": true, + "gridcolor": "LightGrey", + "range": [ + 0, + 20178.947368421053 + ], + "tickformat": ",d", + "title": { + "text": "Count" + }, + "type": "linear", + "zerolinecolor": "LightGrey" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "course_count = nbgitpuller_textPayload_df_pull_normal.course.value_counts()\n", + "\n", + "fig = go.Figure(data=[go.Bar(\n", + " x=course_count.index,\n", + " y=course_count,\n", + " text=None,\n", + " textposition='outside'\n", + ")])\n", + "\n", + "fig.update_layout(\n", + " title='Normal Click Counts By Course',\n", + " xaxis_title='Course',\n", + " yaxis_title='Count',\n", + " xaxis=dict(\n", + " tickangle=-90, \n", + " tickmode='array', \n", + " tickvals=list(range(len(course_count))), \n", + " ticktext=course_count.index, \n", + " tickfont=dict(size=9)\n", + " ),\n", + " yaxis=dict(\n", + " tickformat=',d', \n", + " gridcolor='LightGrey', \n", + " zerolinecolor='LightGrey'\n", + " ),\n", + " width=1000, \n", + " height=900, \n", + " plot_bgcolor='white', \n", + " paper_bgcolor='white', \n", + " hovermode='x unified', \n", + ")\n", + "\n", + "for trace in fig.data:\n", + " trace.hoverinfo = 'x+y'\n", + " trace.hoverlabel = dict(bgcolor=\"LightGrey\", font_size=16)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "be9ab4c3-1f9f-4704-8bbf-cf414be3166c", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hoverinfo": "x+y", + "hoverlabel": { + "bgcolor": "LightGrey", + "font": { + "size": 16 + } + }, + "marker": { + "color": "#9467bd" + }, + "textposition": "outside", + "type": "bar", + "x": [ + "ipynb", + "rstudio", + "pdf", + "md", + "R" + ], + "y": [ + 51433, + 7156, + 102, + 16, + 1 + ] + } + ], + "layout": { + "height": 600, + "hovermode": "x unified", + "paper_bgcolor": "white", + "plot_bgcolor": "white", + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "File Type By Counts (Github Repos)" + }, + "width": 900, + "xaxis": { + "autorange": true, + "range": [ + -0.5, + 4.5 + ], + "tickangle": -90, + "title": { + "text": "File Type" + }, + "type": "category" + }, + "yaxis": { + "autorange": true, + "gridcolor": "LightGrey", + "range": [ + 0, + 54140 + ], + "tickformat": ",d", + "title": { + "text": "Counts" + }, + "type": "linear", + "zerolinecolor": "LightGrey" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "file_types_count = nbgitpuller_textPayload_df_pull_normal.file_extension[nbgitpuller_textPayload_df_pull_normal.file_extension != 'NaN'].value_counts()\n", + "\n", + "fig = go.Figure(data=[go.Bar(\n", + " x=file_types_count.index,\n", + " y=file_types_count,\n", + " text=None,\n", + " textposition='outside', \n", + " marker= dict(color='#9467bd')\n", + ")])\n", + "\n", + "fig.update_layout(\n", + " title='File Type By Counts (Github Repos)',\n", + " xaxis_title='File Type',\n", + " yaxis_title='Counts',\n", + " xaxis=dict(\n", + " tickangle=-90, \n", + " ),\n", + " yaxis=dict(\n", + " tickformat=',d', \n", + " gridcolor='LightGrey', \n", + " zerolinecolor='LightGrey'\n", + " ),\n", + " width=900, \n", + " height=600, \n", + " plot_bgcolor='white', \n", + " paper_bgcolor='white', \n", + " hovermode='x unified', \n", + ")\n", + "\n", + "for trace in fig.data:\n", + " trace.hoverinfo = 'x+y'\n", + " trace.hoverlabel = dict(bgcolor=\"LightGrey\", font_size=16)\n", + "\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "79edd623-53a4-4382-bf0e-815a43539838", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hoverinfo": "x+y", + "hoverlabel": { + "bgcolor": "LightGrey", + "font": { + "size": 16 + } + }, + "marker": { + "color": "maroon" + }, + "textposition": "outside", + "type": "bar", + "x": [ + "unknown" + ], + "y": [ + 15 + ] + } + ], + "layout": { + "height": 600, + "hovermode": "x unified", + "paper_bgcolor": "white", + "plot_bgcolor": "white", + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Abnormal Click Counts By Course" + }, + "width": 800, + "xaxis": { + "autorange": true, + "range": [ + -0.5, + 0.5 + ], + "tickangle": -90, + "tickfont": { + "size": 9 + }, + "tickmode": "array", + "ticktext": [ + "unknown" + ], + "tickvals": [ + 0 + ], + "title": { + "text": "Course" + }, + "type": "category" + }, + "yaxis": { + "autorange": true, + "gridcolor": "LightGrey", + "range": [ + 0, + 15.789473684210526 + ], + "tickformat": ",d", + "title": { + "text": "Count" + }, + "type": "linear", + "zerolinecolor": "LightGrey" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "abnormal_course_count = nbgitpuller_textPayload_df_pull_abnormal.course.value_counts()\n", + "\n", + "fig = go.Figure(data=[go.Bar(\n", + " x=abnormal_course_count.index,\n", + " y=abnormal_course_count,\n", + " text=None,\n", + " textposition='outside',\n", + " marker=dict(color='maroon')\n", + "\n", + ")])\n", + "\n", + "fig.update_layout(\n", + " title='Abnormal Click Counts By Course',\n", + " xaxis_title='Course',\n", + " yaxis_title='Count',\n", + " xaxis=dict(\n", + " tickangle=-90, \n", + " tickmode='array', \n", + " tickvals=list(range(len(abnormal_course_count))), \n", + " ticktext=abnormal_course_count.index, \n", + " tickfont=dict(size=9)\n", + " ),\n", + " yaxis=dict(\n", + " tickformat=',d', \n", + " gridcolor='LightGrey', \n", + " zerolinecolor='LightGrey'\n", + " ),\n", + " width=800, \n", + " height=600, \n", + " plot_bgcolor='white', \n", + " paper_bgcolor='white', \n", + " hovermode='x unified', \n", + ")\n", + "\n", + "for trace in fig.data:\n", + " trace.hoverinfo = 'x+y'\n", + " trace.hoverlabel = dict(bgcolor=\"LightGrey\", font_size=16)\n", + "\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "140c5090-98b7-4048-a812-0f5dd0ddeca5", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + }, + "hoverinfo": "label+percent+value", + "hovertemplate": "%{label}
Percentage: %{percent:.2%}
Count: %{value}", + "labels": [ + "data8-prod", + "datahub-prod", + "data100-prod", + "prob140-prod", + "publichealth-prod", + "data101-prod", + "stat20-prod", + "data102-prod", + "others" + ], + "legendgroup": "", + "name": "", + "showlegend": true, + "textinfo": "label", + "type": "pie", + "values": [ + 17816, + 12380, + 11580, + 5655, + 3334, + 3028, + 2986, + 1706, + 2858 + ] + } + ], + "layout": { + "height": 600, + "legend": { + "tracegroupgap": 0 + }, + "showlegend": false, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "font": { + "size": 20 + }, + "text": "Clicks By Hub - Github Repos" + }, + "width": 800 + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clicks_by_hub = nbgitpuller_textPayload_df_pull_normal.hub.value_counts()\n", + "\n", + "clicks_by_hub_top8 = dict(clicks_by_hub[:8])\n", + "clicks_by_hub_top8['others'] = clicks_by_hub[8:].sum()\n", + "\n", + "clicks_by_hub_top8_df = pd.DataFrame(clicks_by_hub_top8.items(), columns=['hub', 'counts'])\n", + "\n", + "fig = px.pie(clicks_by_hub_top8_df, values='counts', names='hub', title='Clicks By Hub - Github Repos')\n", + "\n", + "fig.update_traces(\n", + " textinfo='label', \n", + " hoverinfo='label+percent+value', \n", + " hovertemplate='%{label}
Percentage: %{percent:.2%}
Count: %{value}'\n", + ")\n", + "\n", + "fig.update_layout(\n", + " width=800, \n", + " height=600, \n", + " title_font_size=20,\n", + " showlegend=False\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5419808f-9d0b-465f-b649-2d6f9509fc6a", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + }, + "hoverinfo": "label+percent", + "hovertemplate": "%{label}
Percentage: %{percent:.2%}
Count: %{value}", + "labels": [ + "data8", + "data100", + "unknown", + "data140", + "pbhlth142", + "data101", + "stat20", + "data102", + "others" + ], + "legendgroup": "", + "name": "", + "showlegend": true, + "textinfo": "label", + "type": "pie", + "values": [ + 19170, + 11519, + 7444, + 5608, + 3132, + 3027, + 2982, + 1710, + 6751 + ] + } + ], + "layout": { + "height": 600, + "legend": { + "tracegroupgap": 0 + }, + "showlegend": false, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "font": { + "size": 20 + }, + "text": "Clicks By Course" + }, + "width": 800 + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "courses_top8 = dict(course_count[:8])\n", + "courses_top8['others'] = course_count[8:].sum()\n", + "courses_top8_df = pd.DataFrame(courses_top8.items(), columns = ['course', 'counts'])\n", + "\n", + "fig = px.pie(courses_top8_df, values='counts', names='course', title='Clicks By Course')\n", + "\n", + "fig.update_traces(\n", + " textinfo='label', \n", + " hoverinfo='label+percent', \n", + " hovertemplate='%{label}
Percentage: %{percent:.2%}
Count: %{value}'\n", + ")\n", + "\n", + "fig.update_layout(\n", + " width=800, \n", + " height=600, \n", + " title_font_size=20, \n", + " showlegend=False\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1aa57878-42e8-40ef-a984-7cbede587e99", + "metadata": {}, + "source": [ + "## Distributions By Semester" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3768d128-15db-4b20-b0c2-cb55e931adf4", + "metadata": {}, + "outputs": [], + "source": [ + "#sort df by semester \n", + "#give sem intrinsic numerical value\n", + "sem_order = [\"summer2024\",\"spring2024\", \"fall2023\", \"summer2023\", \"spring2023\", \"fall2022\", \"summer2022\", \"spring2022\", \"fall2021\", \"summer2021\", \"spring2021\", \"fall2020\", \"summer2020\", \"spring2020\", \"fall2019\", \"summer2019\", \"spring2019\", \"unknown\"]\n", + "nbgitpuller_textPayload_df_pull_normal.loc[:, \"semester\"] = pd.Categorical(\n", + " nbgitpuller_textPayload_df_pull_normal[\"semester\"],\n", + " categories=sem_order,\n", + " ordered=True\n", + ")\n", + "nbgitpuller_textPayload_df_pull_normal_sem_sort = nbgitpuller_textPayload_df_pull_normal.sort_values(\"semester\")\n", + "sorted_sem_count = nbgitpuller_textPayload_df_pull_normal_sem_sort.semester.value_counts().reindex(sem_order)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "e84bc2e0-0b17-4e3e-abd7-46cc47cca6b3", + "metadata": {}, + "outputs": [], + "source": [ + "agg_sem_counts = nbgitpuller_textPayload_df_pull_normal_sem_sort.groupby(\"semester\").count().reset_index()\n", + "agg_sem_counts = agg_sem_counts.loc[:, [\"semester\", \"log_info_type\"]].rename(columns={\"log_info_type\":\"count\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7f083b54-796a-4386-b369-86edf14ab123", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hoverinfo": "x+y", + "hoverlabel": { + "bgcolor": "LightGrey", + "font": { + "size": 16 + } + }, + "marker": { + "color": "green" + }, + "textposition": "outside", + "type": "bar", + "x": [ + "fall2019", + "fall2020", + "fall2021", + "fall2022", + "fall2023", + "spring2020", + "spring2021", + "spring2022", + "spring2023", + "spring2024", + "summer2020", + "summer2021", + "summer2022", + "summer2023", + "summer2024", + "unknown" + ], + "y": [ + 162, + 79, + 449, + 564, + 1700, + 138, + 285, + 402, + 745, + 2306, + 139, + 89, + 263, + 391, + 1290, + 4483 + ] + } + ], + "layout": { + "height": 600, + "hovermode": "x unified", + "paper_bgcolor": "white", + "plot_bgcolor": "white", + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Semester Content Count (Github Repos)" + }, + "width": 800, + "xaxis": { + "autorange": true, + "categoryarray": [ + "summer2024", + "spring2024", + "fall2023", + "summer2023", + "spring2023", + "fall2022", + "summer2022", + "spring2022", + "fall2021", + "summer2021", + "spring2021", + "fall2020", + "summer2020", + "spring2020", + "fall2019", + "summer2019", + "spring2019", + "unknown" + ], + "range": [ + -0.5, + 17.5 + ], + "tickangle": -45, + "title": { + "text": "Semester" + }, + "type": "category" + }, + "yaxis": { + "autorange": true, + "gridcolor": "LightGrey", + "range": [ + 0, + 4718.9473684210525 + ], + "tickformat": ",d", + "title": { + "text": "Count" + }, + "type": "linear", + "zerolinecolor": "LightGrey" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure(data=[go.Bar(\n", + " x=agg_sem_counts['semester'], \n", + " y=agg_sem_counts['count'], \n", + " text=None,\n", + " textposition='outside', \n", + " marker= dict(color='green')\n", + ")])\n", + "\n", + "fig.update_layout(\n", + " title='Semester Content Count (Github Repos)',\n", + " xaxis_title='Semester',\n", + " yaxis_title='Count',\n", + " xaxis=dict(\n", + " tickangle=-45, \n", + " categoryarray=sem_order,\n", + " ),\n", + " yaxis=dict(\n", + " tickformat=',d', \n", + " gridcolor='LightGrey', \n", + " zerolinecolor='LightGrey'\n", + " ),\n", + " width=800, \n", + " height=600, \n", + " plot_bgcolor='white', \n", + " paper_bgcolor='white', \n", + " hovermode='x unified', \n", + ")\n", + "\n", + "for trace in fig.data:\n", + " trace.hoverinfo = 'x+y'\n", + " trace.hoverlabel = dict(bgcolor=\"LightGrey\", font_size=16)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a1661f30-4f39-4dd2-b624-359dd2987c0f", + "metadata": {}, + "outputs": [], + "source": [ + "def format_semester(semester):\n", + " if semester == \"Unknown\": \n", + " return \"Unknown\"\n", + " else: \n", + " season = semester[:-4].capitalize()\n", + " year = semester[-4:]\n", + " return f\"{season} {year}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c6b3a938-cba3-43f3-94e5-f296acd889e3", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + }, + "hoverinfo": "label+percent", + "hovertemplate": "%{label}: %{percent:.2%}", + "labels": [ + "unknown", + "spring2024", + "fall2023", + "summer2024", + "spring2023", + "other" + ], + "legendgroup": "", + "name": "", + "showlegend": true, + "textinfo": "label", + "type": "pie", + "values": [ + 4483, + 2306, + 1700, + 1290, + 745, + 2961 + ] + } + ], + "layout": { + "height": 600, + "legend": { + "tracegroupgap": 0 + }, + "showlegend": false, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "font": { + "size": 20 + }, + "text": "Semester Repo Distrubutions (Percentage)" + }, + "width": 800 + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sem_count = nbgitpuller_textPayload_df_pull_normal.semester.value_counts()\n", + "sem_count5 = dict(sem_count[:5])\n", + "sem_count5['other'] = sem_count[5:].sum()\n", + "sem_count5 = pd.DataFrame(sem_count5.items(), columns = ['semester', 'counts'])\n", + "\n", + "fig = px.pie(sem_count5, values = 'counts', names='semester', title='Semester Repo Distrubutions (Percentage)')\n", + "\n", + "fig.update_traces(\n", + " textinfo='label', \n", + " hoverinfo='label+percent', \n", + " hovertemplate='%{label}: %{percent:.2%}'\n", + ")\n", + "\n", + "fig.update_layout(\n", + " width=800, \n", + " height=600, \n", + " title_font_size=20,\n", + " showlegend=False\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0930a700-59f3-4cd8-b4a1-2d3c7f3e58aa", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "domain": { + "x": [ + 0, + 0.45 + ], + "y": [ + 0.825, + 1 + ] + }, + "hoverinfo": "label+percent+value", + "labels": [ + "data100", + "data8", + "data6", + "eecs16a", + "pbhlth142", + "unknown", + "Other" + ], + "name": "summer2024", + "textinfo": "label", + "type": "pie", + "values": [ + 517, + 461, + 186, + 77, + 21, + 19, + 9 + ] + }, + { + "domain": { + "x": [ + 0.55, + 1 + ], + "y": [ + 0.825, + 1 + ] + }, + "hoverinfo": "label+percent+value", + "labels": [ + "data100", + "data8", + "eecs16b", + "data102", + "econ148", + "data101", + "Other" + ], + "name": "spring2024", + "textinfo": "label", + "type": "pie", + "values": [ + 811, + 750, + 240, + 206, + 96, + 89, + 114 + ] + }, + { + "domain": { + "x": [ + 0, + 0.45 + ], + "y": [ + 0.55, + 0.7250000000000001 + ] + }, + "hoverinfo": "label+percent+value", + "labels": [ + "data8", + "data100", + "data102", + "data101", + "eecs16a", + "econ140", + "Other" + ], + "name": "fall2023", + "textinfo": "label", + "type": "pie", + "values": [ + 648, + 409, + 188, + 174, + 55, + 53, + 173 + ] + }, + { + "domain": { + "x": [ + 0.55, + 1 + ], + "y": [ + 0.55, + 0.7250000000000001 + ] + }, + "hoverinfo": "label+percent+value", + "labels": [ + "data8", + "data6", + "data100", + "Other" + ], + "name": "summer2023", + "textinfo": "label", + "type": "pie", + "values": [ + 171, + 130, + 90, + 0 + ] + }, + { + "domain": { + "x": [ + 0, + 0.45 + ], + "y": [ + 0.275, + 0.45 + ] + }, + "hoverinfo": "label+percent+value", + "labels": [ + "data8", + "data100", + "econ148", + "data102", + "eecs16a", + "polsci88", + "Other" + ], + "name": "spring2023", + "textinfo": "label", + "type": "pie", + "values": [ + 311, + 265, + 63, + 63, + 23, + 7, + 13 + ] + }, + { + "domain": { + "x": [ + 0.55, + 1 + ], + "y": [ + 0.275, + 0.45 + ] + }, + "hoverinfo": "label+percent+value", + "labels": [ + "data8", + "data100", + "eecs16b", + "eecs16a", + "envecon118", + "data101", + "Other" + ], + "name": "fall2022", + "textinfo": "label", + "type": "pie", + "values": [ + 287, + 196, + 28, + 19, + 16, + 8, + 10 + ] + }, + { + "domain": { + "x": [ + 0, + 0.45 + ], + "y": [ + 0, + 0.175 + ] + }, + "hoverinfo": "label+percent+value", + "labels": [ + "data6", + "data100", + "eecs16b", + "eecs16a", + "Other" + ], + "name": "summer2022", + "textinfo": "label", + "type": "pie", + "values": [ + 115, + 102, + 40, + 6, + 0 + ] + } + ], + "layout": { + "annotations": [ + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "summer2024", + "x": 0.225, + "xanchor": "center", + "xref": "paper", + "y": 1, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "spring2024", + "x": 0.775, + "xanchor": "center", + "xref": "paper", + "y": 1, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "fall2023", + "x": 0.225, + "xanchor": "center", + "xref": "paper", + "y": 0.7250000000000001, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "summer2023", + "x": 0.775, + "xanchor": "center", + "xref": "paper", + "y": 0.7250000000000001, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "spring2023", + "x": 0.225, + "xanchor": "center", + "xref": "paper", + "y": 0.45, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "fall2022", + "x": 0.775, + "xanchor": "center", + "xref": "paper", + "y": 0.45, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "summer2022", + "x": 0.225, + "xanchor": "center", + "xref": "paper", + "y": 0.175, + "yanchor": "bottom", + "yref": "paper" + } + ], + "autosize": true, + "showlegend": false, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Course Distribution by Semester (Spring2024 - Summer2022)", + "x": 0.5, + "xanchor": "center", + "yanchor": "top" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "unique_sem = ['summer2024','spring2024', 'fall2023', 'summer2023', 'spring2023', 'fall2022', 'summer2022']\n", + "\n", + "rows = len(unique_sem) // 2 + len(unique_sem) % 2 \n", + "\n", + "fig = make_subplots(\n", + " rows=rows, \n", + " cols=2, \n", + " subplot_titles=unique_sem, \n", + " specs=[[{'type':'domain'}]*2]*rows, \n", + " vertical_spacing=0.1\n", + ")\n", + "\n", + "for i, semester in enumerate(unique_sem):\n", + " filtered_sem = nbgitpuller_textPayload_df_pull_normal_sem_sort[\n", + " nbgitpuller_textPayload_df_pull_normal_sem_sort['semester'] == semester\n", + " ]\n", + " semester_dist = filtered_sem['course'].value_counts()[:6]\n", + " sem_other = filtered_sem['course'].value_counts()[6:].sum()\n", + " semester_dist['Other'] = sem_other\n", + " \n", + " row = i // 2 + 1 \n", + " col = i % 2 + 1 \n", + " fig.add_trace(go.Pie(\n", + " labels=semester_dist.index, \n", + " values=semester_dist.values, \n", + " name=semester, \n", + " textinfo='label',\n", + " hoverinfo='label+percent+value' \n", + " ), row=row, col=col)\n", + "\n", + "fig.update_layout(\n", + " title=dict(\n", + " text=\"Course Distribution by Semester (Spring2024 - Summer2022)\",\n", + " x=0.5,\n", + " xanchor=\"center\",\n", + " yanchor=\"top\"\n", + " ), \n", + " height=300 * rows,\n", + " showlegend=False, \n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c351ecd4-cfef-4e6e-93df-4e5726d579fd", + "metadata": {}, + "outputs": [], + "source": [ + "courses_data = nbgitpuller_textPayload_df_pull_normal.groupby(\"timestamp_date\").count()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0d3d56c6-5231-4381-baf5-6a3f4a5cf850", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Date: %{x|%Y-%m-%d}
Action Count: %{y}", + "marker": { + "color": "rgba(0, 0, 255, 0.5)" + }, + "mode": "markers", + "name": "Normal Actions", + "type": "scatter", + "x": [ + "2024-08-21", + "2024-08-22", + "2024-08-23", + "2024-08-24", + "2024-08-25", + "2024-08-26", + "2024-08-27", + "2024-08-28", + "2024-08-29", + "2024-08-30", + "2024-08-31", + "2024-09-01", + "2024-09-02", + "2024-09-03", + "2024-09-04", + "2024-09-05" + ], + "y": [ + 192, + 1245, + 893, + 681, + 627, + 759, + 1057, + 6952, + 7307, + 8543, + 3467, + 2847, + 4447, + 9063, + 8027, + 5236 + ] + }, + { + "hovertemplate": "Date: %{x|%Y-%m-%d}
Action Count: %{y}", + "marker": { + "color": "rgba(255, 0, 0, 0.5)" + }, + "mode": "markers", + "name": "Abnormal Actions", + "type": "scatter", + "x": [ + "2024-08-28", + "2024-08-29", + "2024-09-01", + "2024-09-04", + "2024-09-05" + ], + "y": [ + 3, + 2, + 8, + 1, + 1 + ] + } + ], + "layout": { + "height": 900, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Clicks Over Time (2024-08-21 to 2024-09-05)" + }, + "width": 1100, + "xaxis": { + "autorange": true, + "range": [ + "2024-08-20 01:59:45.5711", + "2024-09-05 22:00:14.4289" + ], + "tickangle": -45, + "tickformat": "%b", + "tickvals": [ + "2024-09-01" + ], + "title": { + "text": "Month" + }, + "type": "date" + }, + "yaxis": { + "autorange": true, + "range": [ + -561.3957845433256, + 9625.395784543325 + ], + "tickformat": "d", + "title": { + "text": "Count" + }, + "type": "linear" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "courses_data_abnormal = nbgitpuller_textPayload_df_pull_abnormal.groupby(\"timestamp_date\").count()\n", + "\n", + "fig = go.Figure()\n", + "\n", + "fig.add_trace(go.Scatter(\n", + " x=courses_data.index,\n", + " y=courses_data['action'],\n", + " mode='markers',\n", + " name='Normal Actions',\n", + " hovertemplate='Date: %{x|%Y-%m-%d}
Action Count: %{y}',\n", + " marker=dict(color='rgba(0, 0, 255, 0.5)')\n", + "))\n", + "\n", + "fig.add_trace(go.Scatter(\n", + " x=courses_data_abnormal.index,\n", + " y=courses_data_abnormal['action'],\n", + " mode='markers',\n", + " name='Abnormal Actions',\n", + " hovertemplate='Date: %{x|%Y-%m-%d}
Action Count: %{y}',\n", + " marker=dict(color='rgba(255, 0, 0, 0.5)')\n", + "))\n", + "\n", + "fig.update_layout(\n", + " title=f'Clicks Over Time ({courses_data.index[0]} to {courses_data.index[-1]})',\n", + " xaxis_title='Month',\n", + " yaxis_title='Count',\n", + " xaxis=dict(\n", + " tickangle=-45,\n", + " tickformat='%b',\n", + " tickvals=[d.strftime('%Y-%m-%d') for d in pd.date_range(start=courses_data.index[0], end=courses_data.index[-1], freq='MS')]\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='d'\n", + " ),\n", + " width=1100,\n", + " height=900\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "4bc043c6-9acf-4b89-8f0d-6265f1c0b3a4", + "metadata": {}, + "outputs": [], + "source": [ + "agg_course_date= nbgitpuller_textPayload_df_pull_normal.timestamp_date.value_counts()\n", + "agg_course_date = pd.DataFrame(agg_course_date)\n", + "agg_course_date = agg_course_date.reset_index()\n", + "top_freq = agg_course_date.sort_values('count', ascending=False)\n", + "top_freq = top_freq.iloc[:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "75b57a85-18dc-49b1-b896-c7635f442ff4", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "domain": { + "x": [ + 0, + 0.45 + ], + "y": [ + 0.625, + 1 + ] + }, + "hoverinfo": "label+percent", + "labels": [ + "data100", + "data8", + "data140", + "data101", + "unknown", + "econ140", + "other" + ], + "textinfo": "label", + "type": "pie", + "values": [ + 2045, + 1955, + 1131, + 678, + 612, + 578, + 2064 + ] + }, + { + "domain": { + "x": [ + 0.55, + 1 + ], + "y": [ + 0.625, + 1 + ] + }, + "hoverinfo": "label+percent", + "labels": [ + "data8", + "data100", + "unknown", + "data140", + "pbhlth142", + "stat20", + "other" + ], + "textinfo": "label", + "type": "pie", + "values": [ + 2573, + 1668, + 1429, + 1023, + 483, + 454, + 913 + ] + }, + { + "domain": { + "x": [ + 0, + 0.45 + ], + "y": [ + 0, + 0.375 + ] + }, + "hoverinfo": "label+percent", + "labels": [ + "data8", + "data100", + "unknown", + "pbhlth142", + "data102", + "data140", + "other" + ], + "textinfo": "label", + "type": "pie", + "values": [ + 2988, + 1388, + 1273, + 508, + 435, + 423, + 1012 + ] + }, + { + "domain": { + "x": [ + 0.55, + 1 + ], + "y": [ + 0, + 0.375 + ] + }, + "hoverinfo": "label+percent", + "labels": [ + "data8", + "unknown", + "stat20", + "data100", + "data140", + "pbhlth142", + "other" + ], + "textinfo": "label", + "type": "pie", + "values": [ + 3277, + 724, + 703, + 666, + 665, + 498, + 774 + ] + } + ], + "layout": { + "annotations": [ + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "2024-09-03", + "x": 0.225, + "xanchor": "center", + "xref": "paper", + "y": 1, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "2024-08-30", + "x": 0.775, + "xanchor": "center", + "xref": "paper", + "y": 1, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "2024-09-04", + "x": 0.225, + "xanchor": "center", + "xref": "paper", + "y": 0.375, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 16 + }, + "showarrow": false, + "text": "2024-08-29", + "x": 0.775, + "xanchor": "center", + "xref": "paper", + "y": 0.375, + "yanchor": "bottom", + "yref": "paper" + } + ], + "autosize": true, + "showlegend": false, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Distribution by Most Counts", + "x": 0.5, + "xanchor": "center", + "yanchor": "top" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rows = (len(top_freq.timestamp_date) + 1) // 2\n", + "\n", + "fig = make_subplots(\n", + " rows=rows, \n", + " cols=2, \n", + " subplot_titles=[date for date in top_freq.timestamp_date],\n", + " specs=[[{'type': 'domain'}, {'type': 'domain'}] for _ in range(rows)]\n", + ")\n", + "\n", + "for i, date in enumerate(top_freq.timestamp_date):\n", + " filtered_date = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal['timestamp_date'] == date]\n", + " date_dist = filtered_date['course'].value_counts()[:6]\n", + " date_other = filtered_date['course'].value_counts()[6:].sum()\n", + " if date_other > 0:\n", + " date_dist['other'] = date_other\n", + " \n", + " row = i // 2 + 1\n", + " col = i % 2 + 1\n", + " fig.add_trace(go.Pie(\n", + " labels=date_dist.index,\n", + " values=date_dist.values,\n", + " textinfo='label',\n", + " hoverinfo='label+percent'\n", + " ), row=row, col=col)\n", + "\n", + "fig.update_layout(\n", + " title=dict(\n", + " text=\"Distribution by Most Counts\",\n", + " x=0.5,\n", + " xanchor=\"center\",\n", + " yanchor=\"top\"\n", + " ),\n", + " height=300 * rows,\n", + " showlegend=False,\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d8cce730-9fed-48b5-91f2-d6956a733021", + "metadata": {}, + "outputs": [], + "source": [ + "## Usage Frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "3eb06a1c-bc74-443e-a940-4f6831072a96", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hoverinfo": "x+y", + "hovertemplate": "Date: %{x|%Y-%m-%d}
Count: %{y}", + "marker": { + "color": "rgba(0, 0, 255, 0.5)" + }, + "type": "bar", + "x": [ + "2024-09-03T00:00:00", + "2024-08-30T00:00:00", + "2024-09-04T00:00:00", + "2024-08-29T00:00:00", + "2024-08-28T00:00:00", + "2024-09-05T00:00:00", + "2024-09-02T00:00:00", + "2024-08-31T00:00:00", + "2024-09-01T00:00:00", + "2024-08-22T00:00:00", + "2024-08-27T00:00:00", + "2024-08-23T00:00:00", + "2024-08-26T00:00:00", + "2024-08-24T00:00:00", + "2024-08-25T00:00:00", + "2024-08-21T00:00:00" + ], + "y": [ + 9063, + 8543, + 8027, + 7307, + 6952, + 5236, + 4447, + 3467, + 2847, + 1245, + 1057, + 893, + 759, + 681, + 627, + 192 + ] + } + ], + "layout": { + "height": 800, + "hovermode": "x", + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Aggregate Usage Frequency by Date" + }, + "width": 1200, + "xaxis": { + "autorange": true, + "range": [ + "2024-08-20 12:00", + "2024-09-05 12:00" + ], + "tickangle": -45, + "tickformat": "%b", + "tickvals": [ + "2024-09-01" + ], + "title": { + "text": "Month" + }, + "type": "date" + }, + "yaxis": { + "autorange": true, + "range": [ + 0, + 9540 + ], + "tickformat": "d", + "title": { + "text": "Frequency" + }, + "type": "linear" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "agg_course_date['timestamp_date'] = pd.to_datetime(agg_course_date['timestamp_date'])\n", + "\n", + "agg_course_date['month'] = agg_course_date['timestamp_date'].dt.strftime('%b-%Y')\n", + "unique_months = agg_course_date['month'].unique()\n", + "\n", + "fig = go.Figure()\n", + "\n", + "fig.add_trace(go.Bar(\n", + " x=agg_course_date['timestamp_date'],\n", + " y=agg_course_date['count'],\n", + " marker=dict(color='rgba(0, 0, 255, 0.5)'),\n", + " hoverinfo='x+y',\n", + " hovertemplate='Date: %{x|%Y-%m-%d}
Count: %{y}',\n", + "\n", + "))\n", + "\n", + "fig.update_layout(\n", + " title='Aggregate Usage Frequency by Date',\n", + " xaxis_title='Month',\n", + " yaxis_title='Frequency',\n", + " xaxis=dict(\n", + " tickangle=-45,\n", + " tickformat='%b',\n", + " tickvals=[d.strftime('%Y-%m-%d') for d in pd.date_range(start=agg_course_date['timestamp_date'].min(), end=agg_course_date['timestamp_date'].max(), freq='MS')]\n", + " ),\n", + " yaxis=dict(\n", + " tickformat='d'\n", + " ),\n", + " hovermode='x',\n", + " width=1200, \n", + " height=800, \n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c1845c99-2d9d-400d-a2c3-fff7c9967c3e", + "metadata": {}, + "source": [ + "### Usage Frequency Comparisons by Date" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "54f55651-6cba-47c6-9c6d-5988fdb27219", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e816bf62a3054e1baae0b62ffe5ef929", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Course 1', options=('biology1b', 'civeng93', 'cogscic131', 'compsci189', 'cyplan88', 'da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a3ddbf22e0f844958db54a1addac9908", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Course 2', options=('biology1b', 'civeng93', 'cogscic131', 'compsci189', 'cyplan88', 'da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fdf2c22715c24bb1bea2fc51986f812e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Course 3', options=('biology1b', 'civeng93', 'cogscic131', 'compsci189', 'cyplan88', 'da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e5adae4f286749d4a9236125f7c647ae", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Course 4', options=('biology1b', 'civeng93', 'cogscic131', 'compsci189', 'cyplan88', 'da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b88b747514c54119bf741c142c523144", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Course 5', options=('biology1b', 'civeng93', 'cogscic131', 'compsci189', 'cyplan88', 'da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f30eb529feb2440398028cf30ab89570", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_course_usage(selected_courses):\n", + " if len(set(selected_courses)) != 5:\n", + " print(\"Please select exactly 5 unique courses to display the plots.\")\n", + " return\n", + "\n", + " total_plots = len(selected_courses)\n", + " total_columns = 5\n", + " fig, axes = plt.subplots(1, total_columns, figsize=(25, 5))\n", + "\n", + " for k, course_name in enumerate(selected_courses):\n", + " course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal['course'] == course_name].reset_index()\n", + " courses_date = course.groupby(by='timestamp_date').timestamp_date.count()\n", + " ax = axes[k] \n", + " ax.bar(courses_date.index, courses_date)\n", + "\n", + " ax.set_title(f'{course_name} Usage Frequency')\n", + " ax.set_xlabel('Date')\n", + " ax.set_ylabel('Frequency')\n", + " ax.xaxis.set_major_locator(mdates.MonthLocator())\n", + " ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "unique_courses = sorted(nbgitpuller_textPayload_df_pull_normal['course'].unique())\n", + "course_selectors_usage = [widgets.Dropdown(\n", + " options=unique_courses,\n", + " value=None,\n", + " description=f'Course {i+1}',\n", + " disabled=False\n", + ") for i in range(5)]\n", + "\n", + "output_usage = widgets.Output()\n", + "\n", + "def on_selection_change_usage(change):\n", + " with output_usage:\n", + " output_usage.clear_output()\n", + " selected_courses = [selector.value for selector in course_selectors_usage]\n", + " if None in selected_courses:\n", + " print(\"Please select both courses.\")\n", + " elif len(set(selected_courses)) != 5:\n", + " print(\"Please select 5 unique courses.\")\n", + " else:\n", + " plot_course_usage(selected_courses)\n", + "\n", + "for selector in course_selectors_usage:\n", + " selector.observe(on_selection_change_usage, names='value')\n", + "\n", + "display(*course_selectors_usage, output_usage)" + ] + }, + { + "cell_type": "markdown", + "id": "e6c6efa6-516e-443c-99f1-71641b593968", + "metadata": {}, + "source": [ + "### Usage Frequency by Hour" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "0e1495fe-aa62-4090-b244-7296842cfbd7", + "metadata": {}, + "outputs": [], + "source": [ + "agg_course_time= nbgitpuller_textPayload_df_pull_normal.timestamp_time.value_counts()\n", + "agg_course_time = pd.DataFrame(agg_course_time)\n", + "agg_course_time = agg_course_time.reset_index()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "fd19ba6c-e148-4254-b13e-4ad3f914e3a2", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hoverinfo": "x+y", + "hovertemplate": "Hour: %{x}
Count: %{y}", + "marker": { + "color": "green" + }, + "type": "bar", + "x": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23 + ], + "y": [ + 2924, + 2548, + 2187, + 2292, + 2454, + 2258, + 2284, + 1510, + 841, + 483, + 440, + 217, + 251, + 499, + 462, + 1944, + 2480, + 5224, + 5038, + 4241, + 4672, + 4887, + 5023, + 6184 + ] + } + ], + "layout": { + "height": 700, + "hovermode": "x", + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Aggregate Usage Frequency by Time" + }, + "width": 1000, + "xaxis": { + "autorange": true, + "dtick": 1, + "range": [ + -0.5, + 23.5 + ], + "tick0": 0, + "tickmode": "linear", + "title": { + "text": "Time (hours after 12:00 am)" + }, + "type": "linear" + }, + "yaxis": { + "autorange": true, + "range": [ + 0, + 6509.473684210527 + ], + "tickformat": "d", + "title": { + "text": "Frequency" + }, + "type": "linear" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "agg_course_time['timestamp_time'] = pd.to_datetime(agg_course_time['timestamp_time'], format='%H:%M:%S.%f')\n", + "\n", + "agg_course_time['timestamp_time_hour'] = agg_course_time['timestamp_time'].dt.hour\n", + "\n", + "agg_course_time_hourly = agg_course_time.groupby('timestamp_time_hour')['count'].sum().reset_index()\n", + "\n", + "fig = go.Figure()\n", + "\n", + "fig.add_trace(go.Bar(\n", + " x=agg_course_time_hourly['timestamp_time_hour'],\n", + " y=agg_course_time_hourly['count'],\n", + " marker=dict(color='green'),\n", + " hoverinfo='x+y',\n", + " hovertemplate='Hour: %{x}
Count: %{y}',\n", + "))\n", + "\n", + "fig.update_layout(\n", + " title='Aggregate Usage Frequency by Time',\n", + " xaxis_title='Time (hours after 12:00 am)',\n", + " yaxis_title='Frequency',\n", + " xaxis=dict(\n", + " tickmode='linear', \n", + " tick0=0, \n", + " dtick=1, \n", + " ),\n", + " yaxis=dict(\n", + " tickformat='d'\n", + " ),\n", + " hovermode='x',\n", + " width=1000, \n", + " height=700, \n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "d9513e7d-d7c5-44e9-8261-93c86ad3578c", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "487309a2b3544d05964903dfe03c9be9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Course 1', options=('biology1b', 'civeng93', 'cogscic131', 'compsci189', 'cyplan88', 'da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "570aa338e9474f6195221261566170ca", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Course 2', options=('biology1b', 'civeng93', 'cogscic131', 'compsci189', 'cyplan88', 'da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "85548f0f789f4b1996622b023ace73e0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Course 3', options=('biology1b', 'civeng93', 'cogscic131', 'compsci189', 'cyplan88', 'da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3c331d0611ac47ab942f592e568c117c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Course 4', options=('biology1b', 'civeng93', 'cogscic131', 'compsci189', 'cyplan88', 'da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2448623a5f26495a83b31acdb935b7ed", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Dropdown(description='Course 5', options=('biology1b', 'civeng93', 'cogscic131', 'compsci189', 'cyplan88', 'da…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3c8b0608e12040d686326ae2bbd311fe", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_course_time_usage(time_selected_courses):\n", + " if len(set(time_selected_courses)) != 5:\n", + " print(\"Please select exactly 5 unique courses to display the plots.\")\n", + " return\n", + "\n", + " total_plots = len(time_selected_courses)\n", + " total_columns = 5\n", + " fig, axes = plt.subplots(1, total_columns, figsize=(25, 5))\n", + "\n", + " for k, course_name in enumerate(time_selected_courses):\n", + " course = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal['course'] == course_name].reset_index()\n", + " course['timestamp_time'] = pd.to_datetime(course['timestamp_time'], format='%H:%M:%S.%f')\n", + " course['timestamp_time_hour'] = course['timestamp_time'].apply(lambda x: x.hour)\n", + " courses_time = course.groupby(by='timestamp_time_hour').timestamp_time_hour.count()\n", + " ax = axes[k] \n", + " ax.bar(courses_time.index, courses_time, color='green')\n", + "\n", + " ax.set_title(f'{course_name} Time Usage Frequency')\n", + " ax.set_xlabel('Time (hours after 12:00 am)')\n", + " ax.set_ylabel('Frequency')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "course_selectors_time = [widgets.Dropdown(\n", + " options=unique_courses,\n", + " value=None,\n", + " description=f'Course {i+1}',\n", + " disabled=False\n", + ") for i in range(5)]\n", + "\n", + "output_time = widgets.Output()\n", + "\n", + "def on_selection_change_time(change):\n", + " with output_time:\n", + " output_time.clear_output()\n", + " time_selected_courses = [selector.value for selector in course_selectors_time]\n", + " if None in time_selected_courses:\n", + " print(\"Please select both courses.\")\n", + " elif len(set(time_selected_courses)) != 5:\n", + " print(\"Please select 5 unique courses.\")\n", + " else:\n", + " plot_course_time_usage(time_selected_courses)\n", + "\n", + "for selector in course_selectors_time:\n", + " selector.observe(on_selection_change_time, names='value')\n", + "\n", + "display(*course_selectors_time, output_time)" + ] + }, + { + "cell_type": "markdown", + "id": "22192536-838d-4017-8c2e-d46bcf28f2b0", + "metadata": {}, + "source": [ + "# Data Science Courses Only" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "21bb381d-2efb-4260-a83d-e1baa1ac7e3f", + "metadata": {}, + "outputs": [], + "source": [ + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "d9417e3a-7dc7-4c7e-b340-8683dafc136d", + "metadata": {}, + "outputs": [], + "source": [ + "pattern = r\"^data\"\n", + "data_courses_only = nbgitpuller_textPayload_df_pull_normal[nbgitpuller_textPayload_df_pull_normal[\"course\"].str.contains(pattern, regex=True)]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "06257a05-f007-4b65-9d61-98cf143e60d5", + "metadata": {}, + "outputs": [], + "source": [ + "ds_counts = data_courses_only[\"course\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "0496f539-30df-483b-95a3-b3e77930ce70", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hoverinfo": "x+y", + "hoverlabel": { + "bgcolor": "LightGrey", + "font": { + "size": 16 + } + }, + "marker": { + "color": "Grey" + }, + "textposition": "outside", + "type": "bar", + "x": [ + "data8", + "data100", + "data140", + "data101", + "data102", + "data6", + "datasci_rbridge", + "datasci241" + ], + "y": [ + 19170, + 11519, + 5608, + 3027, + 1710, + 865, + 27, + 8 + ] + } + ], + "layout": { + "height": 600, + "hovermode": "x unified", + "paper_bgcolor": "white", + "plot_bgcolor": "white", + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Clicks by Data Science Course" + }, + "width": 900, + "xaxis": { + "autorange": true, + "range": [ + -0.5, + 7.5 + ], + "tickangle": -90, + "title": { + "text": "Course" + }, + "type": "category" + }, + "yaxis": { + "autorange": true, + "gridcolor": "LightGrey", + "range": [ + 0, + 20178.947368421053 + ], + "tickformat": ",d", + "title": { + "text": "Count" + }, + "type": "linear", + "zerolinecolor": "LightGrey" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure(data=[go.Bar(\n", + " x=ds_counts.index,\n", + " y=ds_counts.values,\n", + " text=None, \n", + " textposition='outside',\n", + " marker=dict(color='Grey')\n", + ")])\n", + "\n", + "fig.update_layout(\n", + " title='Clicks by Data Science Course', \n", + " xaxis_title='Course',\n", + " yaxis_title='Count', \n", + " xaxis=dict(tickangle=-90),\n", + " yaxis=dict(\n", + " tickformat=',d',\n", + " gridcolor='LightGrey',\n", + " zerolinecolor='LightGrey'\n", + " ),\n", + " width=900,\n", + " height=600,\n", + " plot_bgcolor='white',\n", + " paper_bgcolor='white',\n", + " hovermode='x unified',\n", + " )\n", + "\n", + "for trace in fig.data: \n", + " trace.hoverinfo = 'x+y'\n", + " trace.hoverlabel = dict(bgcolor='LightGrey', font_size=16)\n", + " \n", + " fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "070b12fd-cd22-4e60-bb44-759b59fa30e0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_347/1923266441.py:5: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n" + ] + } + ], + "source": [ + "pattern = r'(lab|hw|Homework|lec|proj|project|textbook)'\n", + "\n", + "types = [re.search(pattern, path) for path in data_courses_only[\"git_path\"]]\n", + "types = [match.group(1) if match else \"unknown\" for match in types]\n", + "data_courses_only[\"type\"] = types" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "0503a076-2442-4281-be71-e5ea9fc70e0b", + "metadata": {}, + "outputs": [], + "source": [ + "# NOTE: % of DS courses unaccounted for\n", + "boo = [(type == None) for type in types]\n", + "none_val = data_courses_only[boo].git_path\n", + "missing_val = sum([type == \"unknown\" for type in types])\n", + "percent=missing_val/len(data_courses_only)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "26835ab9-bd18-4a55-9942-a408c89d9a5c", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "insidetextorientation": "radial", + "labels": [ + "lab", + "hw", + "lec", + "textbook", + "unknown", + "proj" + ], + "textinfo": "label", + "type": "pie", + "values": [ + 27940, + 8261, + 4254, + 775, + 406, + 298 + ] + } + ], + "layout": { + "height": 500, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "white", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "white", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "#C8D4E3", + "linecolor": "#C8D4E3", + "minorgridcolor": "#C8D4E3", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "white", + "showlakes": true, + "showland": true, + "subunitcolor": "#C8D4E3" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "white", + "polar": { + "angularaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + }, + "bgcolor": "white", + "radialaxis": { + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "yaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + }, + "zaxis": { + "backgroundcolor": "white", + "gridcolor": "#DFE8F3", + "gridwidth": 2, + "linecolor": "#EBF0F8", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#EBF0F8" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Course Usage Occasion" + }, + "width": 800 + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "type_counts = data_courses_only.type.value_counts()\n", + "\n", + "fig = go.Figure()\n", + "\n", + "fig.add_trace(go.Pie(\n", + " labels=type_counts.index,\n", + " values=type_counts,\n", + " textinfo='label',\n", + " insidetextorientation='radial'\n", + "))\n", + "\n", + "fig.update_layout(\n", + " title='Course Usage Occasion',\n", + " template='plotly_white',\n", + " width=800,\n", + " height=500\n", + ")\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "3990b0de-e735-45d0-90ac-89121310b526", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "domain": { + "x": [ + 0, + 0.45 + ], + "y": [ + 0.7777777777777778, + 1 + ] + }, + "hoverinfo": "label+percent", + "labels": [ + "lab", + "lec", + "textbook", + "hw", + "unknown", + "proj" + ], + "name": "data100", + "type": "pie", + "values": [ + 11203, + 160, + 110, + 21, + 14, + 11 + ] + }, + { + "domain": { + "x": [ + 0.55, + 1 + ], + "y": [ + 0.7777777777777778, + 1 + ] + }, + "hoverinfo": "label+percent", + "labels": [ + "lab", + "hw", + "lec", + "proj", + "textbook", + "unknown" + ], + "name": "data8", + "type": "pie", + "values": [ + 9711, + 5725, + 3388, + 277, + 37, + 32 + ] + }, + { + "domain": { + "x": [ + 0, + 0.45 + ], + "y": [ + 0.3888888888888889, + 0.6111111111111112 + ] + }, + "hoverinfo": "label+percent", + "labels": [ + "lab", + "proj" + ], + "name": "data101", + "type": "pie", + "values": [ + 3019, + 8 + ] + }, + { + "domain": { + "x": [ + 0.55, + 1 + ], + "y": [ + 0.3888888888888889, + 0.6111111111111112 + ] + }, + "hoverinfo": "label+percent", + "labels": [ + "lab", + "lec", + "unknown" + ], + "name": "data102", + "type": "pie", + "values": [ + 1575, + 132, + 3 + ] + }, + { + "domain": { + "x": [ + 0, + 0.45 + ], + "y": [ + 0, + 0.22222222222222224 + ] + }, + "hoverinfo": "label+percent", + "labels": [ + "hw", + "lab", + "textbook", + "unknown", + "lec" + ], + "name": "data140", + "type": "pie", + "values": [ + 2465, + 2194, + 628, + 318, + 3 + ] + }, + { + "domain": { + "x": [ + 0.55, + 1 + ], + "y": [ + 0, + 0.22222222222222224 + ] + }, + "hoverinfo": "label+percent", + "labels": [ + "lec", + "lab", + "hw", + "unknown", + "proj" + ], + "name": "data6", + "type": "pie", + "values": [ + 571, + 238, + 50, + 4, + 2 + ] + } + ], + "layout": { + "annotations": [ + { + "font": { + "size": 12 + }, + "showarrow": false, + "text": "data100", + "x": 0.05, + "xanchor": "center", + "xref": "paper", + "y": 0.8666666666666667, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 12 + }, + "showarrow": false, + "text": "data8", + "x": 0.55, + "xanchor": "center", + "xref": "paper", + "y": 0.8666666666666667, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 12 + }, + "showarrow": false, + "text": "data101", + "x": 0.05, + "xanchor": "center", + "xref": "paper", + "y": 0.5333333333333333, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 12 + }, + "showarrow": false, + "text": "data102", + "x": 0.55, + "xanchor": "center", + "xref": "paper", + "y": 0.5333333333333333, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 12 + }, + "showarrow": false, + "text": "data140", + "x": 0.05, + "xanchor": "center", + "xref": "paper", + "y": 0.2, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": { + "size": 12 + }, + "showarrow": false, + "text": "data6", + "x": 0.55, + "xanchor": "center", + "xref": "paper", + "y": 0.2, + "yanchor": "bottom", + "yref": "paper" + } + ], + "autosize": true, + "showlegend": true, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Type Distribution in Data Science Courses", + "x": 0.5, + "xanchor": "center", + "y": 0.987, + "yanchor": "top" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "unique_courses = ['data100', 'data8', 'data101', 'data102', 'data140', 'data6']\n", + "\n", + "rows = len(unique_courses) // 2 + len(unique_courses) % 2\n", + "fig = make_subplots(rows=rows, cols=2, subplot_titles=unique_courses, specs=[[{'type': 'domain'}] * 2] * rows)\n", + "\n", + "for i, course in enumerate(unique_courses):\n", + " filtered_course = data_courses_only[data_courses_only[\"course\"] == course]\n", + " course_counts = filtered_course[\"type\"].value_counts()\n", + "\n", + " row = i // 2 + 1\n", + " col = i % 2 + 1\n", + " fig.add_trace(go.Pie(labels=course_counts.index, values=course_counts.values, name=course, hoverinfo='label+percent'), row=row, col=col)\n", + "\n", + "annotations = [\n", + " dict(\n", + " text=title,\n", + " x=(col - 1) / 2 + 0.05, \n", + " y=(rows - row) / rows + 0.2, \n", + " xref='paper',\n", + " yref='paper',\n", + " showarrow=False,\n", + " font=dict(size=12)\n", + " )\n", + " for i, title in enumerate(unique_courses)\n", + " for row in [i // 2 + 1]\n", + " for col in [i % 2 + 1]\n", + "]\n", + "\n", + "fig.update_layout(\n", + " title=dict(\n", + " text='Type Distribution in Data Science Courses',\n", + " y=0.987, \n", + " x=0.5,\n", + " xanchor='center',\n", + " yanchor='top'\n", + " ),\n", + " height=300 * rows, \n", + " showlegend=True,\n", + " annotations=annotations\n", + ")\n", + "\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}