wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# @dog_rates Twitter Wrangling & Analysis\n",
    "\n",
    "## Table of Contents\n",
    "-  <a href = '#intro'>Introduction</a> \n",
    "-  <a href = '#gather'>Gathering</a>\n",
    "-  <a href = '#assess'>Assessment</a>\n",
    "-  <a href = '#clean'>Cleaning</a>\n",
    "-  <a href = '#store'>Storing </a>\n",
    "-  <a href = '#analyse and visualize'>  Analyzing & Visualizing </a>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    " ## Introduction\n",
    " \n",
    "The goal of this project is to gather data of @dog_rates twitter account from twitter api, a csv file and a web-server whose url is provided, then the data will be analysed for Quality and Tidiness, followed by cleaning the dataset and finally provide analyze the data, display insights and visualizations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "import os \n",
    "import numpy as np\n",
    "import tweepy\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "pd.set_option('display.max_colwidth', -1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'gather'></a>\n",
    "## Gathering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the twitter-archive and load it into a dataframe\n",
    "twitter_archive = pd.read_csv('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make an http get request to given url and store the contents in a text file\n",
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "response = requests.get(url)\n",
    "with open(\"image_predictions.txt\",\"w\") as file:\n",
    "    file.write(response.text)\n",
    "# load the response data into a pandas dataframe\n",
    "image_predictions = pd.read_csv(\"image_predictions.txt\", sep = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With proper authentication initialize a tweepy.API() object\n",
    "access_token = ''\n",
    "access_token_secret = ''\n",
    "consumer_key = ''\n",
    "consumer_secret = ''\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "\n",
    "api = tweepy.API(auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a list of all the tweet ids\n",
    "tweet_ids = twitter_archive.tweet_id\n",
    "#create an empty list to store the API response\n",
    "tweet_list = list()\n",
    "#Call the get_status method with each tweet id as parameter to get tweet data and add it to the list\n",
    "for tid in tweet_ids:\n",
    "    try:\n",
    "        tweet = api.get_status(tid, mode = 'extended')\n",
    "        tweet_list.append(tweet._json)\n",
    "        # display tweet id to verify that tweets are being appended\n",
    "        print(tweet._json['id'])\n",
    "    except Exception as e:\n",
    "        # get the error message and tweet ids for tweets that can't be accessed\n",
    "        tweet_list.append({'tweet_id' : tid, 'data' : ' '.join(e.__dict__['reason'].split()[3:])[:-1].replace('{','')})\n",
    "        print(tid, ' '.join(e.__dict__['reason'].split()[3:])[:-1].replace('}',''))\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use json.dumps to covert the list in to a json string and save the data to tweet_json.txt file'\n",
    "jstr = json.dumps(tweet_list, indent = 2)\n",
    "with open('tweet_json.txt', 'w') as json_file:\n",
    "    json_file.write(jstr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data from tweet_json.txt and load it into a dataframe\n",
    "with open('tweet_json.txt') as json_file:\n",
    "    tweet_data_complete = pd.DataFrame(json.loads(json_file.read()))\n",
    "# create another dataframe that contains required info only\n",
    "tweet_data = tweet_data_complete[['id','retweet_count','favorite_count']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>775364825476165632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-12 16:05:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Finley. He's an independent doggo still adjusting to life on his own. 11/10 https://t.co/7FNcBaKbci</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/775364825476165632/photo/1,https://twitter.com/dog_rates/status/775364825476165632/photo/1,https://twitter.com/dog_rates/status/775364825476165632/photo/1,https://twitter.com/dog_rates/status/775364825476165632/photo/1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Finley</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2061</th>\n",
       "      <td>671166507850801152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-30 03:18:59 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Herm. It's his first day of potty training. He's doing great. You got this Herm. 10/10 stellar pup https://t.co/gFl60yFJ0w</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/671166507850801152/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Herm</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>754874841593970688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-18 03:06:01 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>RT @dog_rates: This is Rubio. He has too much skin. 11/10 https://t.co/NLOHmlENag</td>\n",
       "      <td>6.791584e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-12-22 04:35:49 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/679158373988876288/photo/1,https://twitter.com/dog_rates/status/679158373988876288/photo/1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Rubio</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>789599242079838210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-21 22:48:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Brownie. She's wearing a Halloween themed onesie. 12/10 festive af https://t.co/0R4meWXFOx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/789599242079838210/photo/1,https://twitter.com/dog_rates/status/789599242079838210/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Brownie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>890240255349198849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-26 15:59:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Cassie. She is a college pup. Studying international doggo communication and stick theory. 14/10 so elegant much sophisticate https://t.co/t1bfwz5S2A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890240255349198849/photo/1</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>Cassie</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>740995100998766593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-09 19:52:53 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Leo. He's a vape god. Blows o's for days. Probably drives a Subaru. Definitely owns multiple fedoras. 10/10 https://t.co/nt2x3fHaRJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/740995100998766593/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Leo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>680970795137544192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-27 04:37:44 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>I thought I made this very clear. We only rate dogs. Stop sending other things like this shark. Thank you... 9/10 https://t.co/CXSJZ4Stk3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/680970795137544192/photo/1</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714</th>\n",
       "      <td>680440374763077632</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-25 17:30:01 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Merry Christmas. My gift to you is this tiny unicorn running into a wall in slow motion. 11/10 https://t.co/UKqIAnR3He</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/680440374763077632/video/1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>812372279581671427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-23 19:00:19 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Moe. He's a fetty woof. Got a cardboard cutout of himself for Christmas. 13/10 inspirational af https://t.co/gCnAeL2mvT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/812372279581671427/photo/1,https://twitter.com/dog_rates/status/812372279581671427/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Moe</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540</th>\n",
       "      <td>689659372465688576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-20 04:03:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Ricky. He's being escorted out of the dog park for talking shit about the other dogs. 8/10 not cool Ricky https://t.co/XtDkrsdEfF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/689659372465688576/photo/1</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Ricky</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "782   775364825476165632 NaN                    NaN                    \n",
       "2061  671166507850801152 NaN                    NaN                    \n",
       "926   754874841593970688 NaN                    NaN                    \n",
       "674   789599242079838210 NaN                    NaN                    \n",
       "9     890240255349198849 NaN                    NaN                    \n",
       "1064  740995100998766593 NaN                    NaN                    \n",
       "1699  680970795137544192 NaN                    NaN                    \n",
       "1714  680440374763077632 NaN                    NaN                    \n",
       "510   812372279581671427 NaN                    NaN                    \n",
       "1540  689659372465688576 NaN                    NaN                    \n",
       "\n",
       "                      timestamp  \\\n",
       "782   2016-09-12 16:05:54 +0000   \n",
       "2061  2015-11-30 03:18:59 +0000   \n",
       "926   2016-07-18 03:06:01 +0000   \n",
       "674   2016-10-21 22:48:24 +0000   \n",
       "9     2017-07-26 15:59:51 +0000   \n",
       "1064  2016-06-09 19:52:53 +0000   \n",
       "1699  2015-12-27 04:37:44 +0000   \n",
       "1714  2015-12-25 17:30:01 +0000   \n",
       "510   2016-12-23 19:00:19 +0000   \n",
       "1540  2016-01-20 04:03:02 +0000   \n",
       "\n",
       "                                                                                  source  \\\n",
       "782   <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2061  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "926   <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "674   <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "9     <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1064  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1699  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1714  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "510   <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1540  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                                                               text  \\\n",
       "782   This is Finley. He's an independent doggo still adjusting to life on his own. 11/10 https://t.co/7FNcBaKbci                                                     \n",
       "2061  This is Herm. It's his first day of potty training. He's doing great. You got this Herm. 10/10 stellar pup https://t.co/gFl60yFJ0w                              \n",
       "926   RT @dog_rates: This is Rubio. He has too much skin. 11/10 https://t.co/NLOHmlENag                                                                               \n",
       "674   This is Brownie. She's wearing a Halloween themed onesie. 12/10 festive af https://t.co/0R4meWXFOx                                                              \n",
       "9     This is Cassie. She is a college pup. Studying international doggo communication and stick theory. 14/10 so elegant much sophisticate https://t.co/t1bfwz5S2A   \n",
       "1064  This is Leo. He's a vape god. Blows o's for days. Probably drives a Subaru. Definitely owns multiple fedoras. 10/10 https://t.co/nt2x3fHaRJ                     \n",
       "1699  I thought I made this very clear. We only rate dogs. Stop sending other things like this shark. Thank you... 9/10 https://t.co/CXSJZ4Stk3                       \n",
       "1714  Merry Christmas. My gift to you is this tiny unicorn running into a wall in slow motion. 11/10 https://t.co/UKqIAnR3He                                          \n",
       "510   This is Moe. He's a fetty woof. Got a cardboard cutout of himself for Christmas. 13/10 inspirational af https://t.co/gCnAeL2mvT                                 \n",
       "1540  This is Ricky. He's being escorted out of the dog park for talking shit about the other dogs. 8/10 not cool Ricky https://t.co/XtDkrsdEfF                       \n",
       "\n",
       "      retweeted_status_id  retweeted_status_user_id  \\\n",
       "782  NaN                  NaN                         \n",
       "2061 NaN                  NaN                         \n",
       "926   6.791584e+17         4.196984e+09               \n",
       "674  NaN                  NaN                         \n",
       "9    NaN                  NaN                         \n",
       "1064 NaN                  NaN                         \n",
       "1699 NaN                  NaN                         \n",
       "1714 NaN                  NaN                         \n",
       "510  NaN                  NaN                         \n",
       "1540 NaN                  NaN                         \n",
       "\n",
       "     retweeted_status_timestamp  \\\n",
       "782   NaN                         \n",
       "2061  NaN                         \n",
       "926   2015-12-22 04:35:49 +0000   \n",
       "674   NaN                         \n",
       "9     NaN                         \n",
       "1064  NaN                         \n",
       "1699  NaN                         \n",
       "1714  NaN                         \n",
       "510   NaN                         \n",
       "1540  NaN                         \n",
       "\n",
       "                                                                                                                                                                                                                                                        expanded_urls  \\\n",
       "782   https://twitter.com/dog_rates/status/775364825476165632/photo/1,https://twitter.com/dog_rates/status/775364825476165632/photo/1,https://twitter.com/dog_rates/status/775364825476165632/photo/1,https://twitter.com/dog_rates/status/775364825476165632/photo/1   \n",
       "2061  https://twitter.com/dog_rates/status/671166507850801152/photo/1                                                                                                                                                                                                   \n",
       "926   https://twitter.com/dog_rates/status/679158373988876288/photo/1,https://twitter.com/dog_rates/status/679158373988876288/photo/1                                                                                                                                   \n",
       "674   https://twitter.com/dog_rates/status/789599242079838210/photo/1,https://twitter.com/dog_rates/status/789599242079838210/photo/1                                                                                                                                   \n",
       "9     https://twitter.com/dog_rates/status/890240255349198849/photo/1                                                                                                                                                                                                   \n",
       "1064  https://twitter.com/dog_rates/status/740995100998766593/photo/1                                                                                                                                                                                                   \n",
       "1699  https://twitter.com/dog_rates/status/680970795137544192/photo/1                                                                                                                                                                                                   \n",
       "1714  https://twitter.com/dog_rates/status/680440374763077632/video/1                                                                                                                                                                                                   \n",
       "510   https://twitter.com/dog_rates/status/812372279581671427/photo/1,https://twitter.com/dog_rates/status/812372279581671427/photo/1                                                                                                                                   \n",
       "1540  https://twitter.com/dog_rates/status/689659372465688576/photo/1                                                                                                                                                                                                   \n",
       "\n",
       "      rating_numerator  rating_denominator     name  doggo floofer pupper  \\\n",
       "782   11                10                  Finley   doggo  None    None    \n",
       "2061  10                10                  Herm     None   None    None    \n",
       "926   11                10                  Rubio    None   None    None    \n",
       "674   12                10                  Brownie  None   None    None    \n",
       "9     14                10                  Cassie   doggo  None    None    \n",
       "1064  10                10                  Leo      None   None    None    \n",
       "1699  9                 10                  None     None   None    None    \n",
       "1714  11                10                  None     None   None    None    \n",
       "510   13                10                  Moe      None   None    None    \n",
       "1540  8                 10                  Ricky    None   None    None    \n",
       "\n",
       "     puppo  \n",
       "782   None  \n",
       "2061  None  \n",
       "926   None  \n",
       "674   None  \n",
       "9     None  \n",
       "1064  None  \n",
       "1699  None  \n",
       "1714  None  \n",
       "510   None  \n",
       "1540  None  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visually assessing twitter_archive data\n",
    "twitter_archive.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2037</th>\n",
       "      <td>884562892145688576</td>\n",
       "      <td>https://pbs.twimg.com/media/DEaZQkfXUAEC7qB.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.546406</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.404291</td>\n",
       "      <td>True</td>\n",
       "      <td>Brabancon_griffon</td>\n",
       "      <td>0.044002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>674767892831932416</td>\n",
       "      <td>https://pbs.twimg.com/media/CV1B0WkWwAEBKVx.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>shower_curtain</td>\n",
       "      <td>0.238855</td>\n",
       "      <td>False</td>\n",
       "      <td>sarong</td>\n",
       "      <td>0.092414</td>\n",
       "      <td>False</td>\n",
       "      <td>kimono</td>\n",
       "      <td>0.056412</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>740365076218183684</td>\n",
       "      <td>https://pbs.twimg.com/media/CkZOGhJWsAAHvPv.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>bow_tie</td>\n",
       "      <td>0.246313</td>\n",
       "      <td>False</td>\n",
       "      <td>Windsor_tie</td>\n",
       "      <td>0.172446</td>\n",
       "      <td>False</td>\n",
       "      <td>mushroom</td>\n",
       "      <td>0.137516</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>669328503091937280</td>\n",
       "      <td>https://pbs.twimg.com/media/CUnuuLEWEAAlKjN.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.424202</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.237660</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.052572</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549</th>\n",
       "      <td>792913359805018113</td>\n",
       "      <td>https://pbs.twimg.com/media/CwD-eCLWIAA6v0B.jpg</td>\n",
       "      <td>4</td>\n",
       "      <td>web_site</td>\n",
       "      <td>0.226716</td>\n",
       "      <td>False</td>\n",
       "      <td>lighter</td>\n",
       "      <td>0.081941</td>\n",
       "      <td>False</td>\n",
       "      <td>switch</td>\n",
       "      <td>0.039009</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>725729321944506368</td>\n",
       "      <td>https://pbs.twimg.com/media/ChJO9YaWYAEL0zC.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>boxer</td>\n",
       "      <td>0.599076</td>\n",
       "      <td>True</td>\n",
       "      <td>bull_mastiff</td>\n",
       "      <td>0.177318</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.141461</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>675706639471788032</td>\n",
       "      <td>https://pbs.twimg.com/media/CWCXj35VEAIFvtk.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.990300</td>\n",
       "      <td>True</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>True</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.002014</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>757400162377592832</td>\n",
       "      <td>https://pbs.twimg.com/media/CoLTbbzXYAElNM6.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>seat_belt</td>\n",
       "      <td>0.523926</td>\n",
       "      <td>False</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.087800</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.075127</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>680191257256136705</td>\n",
       "      <td>https://pbs.twimg.com/media/CXCGVXyWsAAAVHE.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Brittany_spaniel</td>\n",
       "      <td>0.733253</td>\n",
       "      <td>True</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.251634</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.009243</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>877201837425926144</td>\n",
       "      <td>https://pbs.twimg.com/media/DCxyahJWsAAddSC.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.931120</td>\n",
       "      <td>True</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.068698</td>\n",
       "      <td>True</td>\n",
       "      <td>basenji</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "2037  884562892145688576  https://pbs.twimg.com/media/DEaZQkfXUAEC7qB.jpg   \n",
       "455   674767892831932416  https://pbs.twimg.com/media/CV1B0WkWwAEBKVx.jpg   \n",
       "1195  740365076218183684  https://pbs.twimg.com/media/CkZOGhJWsAAHvPv.jpg   \n",
       "181   669328503091937280  https://pbs.twimg.com/media/CUnuuLEWEAAlKjN.jpg   \n",
       "1549  792913359805018113  https://pbs.twimg.com/media/CwD-eCLWIAA6v0B.jpg   \n",
       "1116  725729321944506368  https://pbs.twimg.com/media/ChJO9YaWYAEL0zC.jpg   \n",
       "492   675706639471788032  https://pbs.twimg.com/media/CWCXj35VEAIFvtk.jpg   \n",
       "1328  757400162377592832  https://pbs.twimg.com/media/CoLTbbzXYAElNM6.jpg   \n",
       "616   680191257256136705  https://pbs.twimg.com/media/CXCGVXyWsAAAVHE.jpg   \n",
       "2003  877201837425926144  https://pbs.twimg.com/media/DCxyahJWsAAddSC.jpg   \n",
       "\n",
       "      img_num                p1   p1_conf  p1_dog                      p2  \\\n",
       "2037  1        pug               0.546406  True    French_bulldog           \n",
       "455   1        shower_curtain    0.238855  False   sarong                   \n",
       "1195  1        bow_tie           0.246313  False   Windsor_tie              \n",
       "181   1        Siberian_husky    0.424202  True    Eskimo_dog               \n",
       "1549  4        web_site          0.226716  False   lighter                  \n",
       "1116  1        boxer             0.599076  True    bull_mastiff             \n",
       "492   1        English_springer  0.990300  True    Welsh_springer_spaniel   \n",
       "1328  1        seat_belt         0.523926  False   golden_retriever         \n",
       "616   1        Brittany_spaniel  0.733253  True    Welsh_springer_spaniel   \n",
       "2003  1        Pembroke          0.931120  True    Cardigan                 \n",
       "\n",
       "       p2_conf  p2_dog                 p3   p3_conf  p3_dog  \n",
       "2037  0.404291  True    Brabancon_griffon  0.044002  True    \n",
       "455   0.092414  False   kimono             0.056412  False   \n",
       "1195  0.172446  False   mushroom           0.137516  False   \n",
       "181   0.237660  True    malamute           0.052572  True    \n",
       "1549  0.081941  False   switch             0.039009  False   \n",
       "1116  0.177318  True    French_bulldog     0.141461  True    \n",
       "492   0.002080  True    cocker_spaniel     0.002014  True    \n",
       "1328  0.087800  True    Tibetan_mastiff    0.075127  True    \n",
       "616   0.251634  True    English_springer   0.009243  True    \n",
       "2003  0.068698  True    basenji            0.000082  True    "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#visually assessing image_predictions data\n",
    "image_predictions.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1412</th>\n",
       "      <td>6.989538e+17</td>\n",
       "      <td>892.0</td>\n",
       "      <td>2655.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>7.036115e+17</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>3806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>8.211496e+17</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>8714.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>6.720952e+17</td>\n",
       "      <td>353.0</td>\n",
       "      <td>965.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>7.932714e+17</td>\n",
       "      <td>2435.0</td>\n",
       "      <td>8832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>7.829691e+17</td>\n",
       "      <td>7485.0</td>\n",
       "      <td>24402.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8.877053e+17</td>\n",
       "      <td>4897.0</td>\n",
       "      <td>28237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>7.091984e+17</td>\n",
       "      <td>637.0</td>\n",
       "      <td>2406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>7.345596e+17</td>\n",
       "      <td>392.0</td>\n",
       "      <td>1466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1011</th>\n",
       "      <td>7.474395e+17</td>\n",
       "      <td>1904.0</td>\n",
       "      <td>5390.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id  retweet_count  favorite_count\n",
       "1412  6.989538e+17  892.0          2655.0        \n",
       "1355  7.036115e+17  1516.0         3806.0        \n",
       "428   8.211496e+17  2009.0         8714.0        \n",
       "2020  6.720952e+17  353.0          965.0         \n",
       "638   7.932714e+17  2435.0         8832.0        \n",
       "723   7.829691e+17  7485.0         24402.0       \n",
       "21    8.877053e+17  4897.0         28237.0       \n",
       "1274  7.091984e+17  637.0          2406.0        \n",
       "1108  7.345596e+17  392.0          1466.0        \n",
       "1011  7.474395e+17  1904.0         5390.0        "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#visually assessing tweet_data\n",
    "tweet_data.sample(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      2356 non-null int64\n",
      "in_reply_to_status_id         78 non-null float64\n",
      "in_reply_to_user_id           78 non-null float64\n",
      "timestamp                     2356 non-null object\n",
      "source                        2356 non-null object\n",
      "text                          2356 non-null object\n",
      "retweeted_status_id           181 non-null float64\n",
      "retweeted_status_user_id      181 non-null float64\n",
      "retweeted_status_timestamp    181 non-null object\n",
      "expanded_urls                 2297 non-null object\n",
      "rating_numerator              2356 non-null int64\n",
      "rating_denominator            2356 non-null int64\n",
      "name                          2356 non-null object\n",
      "doggo                         2356 non-null object\n",
      "floofer                       2356 non-null object\n",
      "pupper                        2356 non-null object\n",
      "puppo                         2356 non-null object\n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#twitter_archive summary\n",
    "twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      "tweet_id    2075 non-null int64\n",
      "jpg_url     2075 non-null object\n",
      "img_num     2075 non-null int64\n",
      "p1          2075 non-null object\n",
      "p1_conf     2075 non-null float64\n",
      "p1_dog      2075 non-null bool\n",
      "p2          2075 non-null object\n",
      "p2_conf     2075 non-null float64\n",
      "p2_dog      2075 non-null bool\n",
      "p3          2075 non-null object\n",
      "p3_conf     2075 non-null float64\n",
      "p3_dog      2075 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "#image_predictions summary\n",
    "image_predictions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 3 columns):\n",
      "id                2331 non-null float64\n",
      "retweet_count     2331 non-null float64\n",
      "favorite_count    2331 non-null float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 55.3 KB\n"
     ]
    }
   ],
   "source": [
    "#tweet_data summary\n",
    "tweet_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None           745\n",
       "a              55 \n",
       "Charlie        12 \n",
       "Lucy           11 \n",
       "Cooper         11 \n",
       "Oliver         11 \n",
       "Tucker         10 \n",
       "Lola           10 \n",
       "Penny          10 \n",
       "Bo             9  \n",
       "Winston        9  \n",
       "Sadie          8  \n",
       "the            8  \n",
       "Buddy          7  \n",
       "an             7  \n",
       "Daisy          7  \n",
       "Toby           7  \n",
       "Bailey         7  \n",
       "Koda           6  \n",
       "Dave           6  \n",
       "Leo            6  \n",
       "Oscar          6  \n",
       "Jack           6  \n",
       "Stanley        6  \n",
       "Bella          6  \n",
       "Jax            6  \n",
       "Milo           6  \n",
       "Rusty          6  \n",
       "Scout          6  \n",
       "Oakley         5  \n",
       "              ..  \n",
       "Juckson        1  \n",
       "Kathmandu      1  \n",
       "Pancake        1  \n",
       "Augie          1  \n",
       "Jaspers        1  \n",
       "Tito           1  \n",
       "Sunshine       1  \n",
       "Sid            1  \n",
       "Clyde          1  \n",
       "Ginger         1  \n",
       "Gin            1  \n",
       "Timmy          1  \n",
       "Mauve          1  \n",
       "Aldrick        1  \n",
       "DayZ           1  \n",
       "Ricky          1  \n",
       "Zoe            1  \n",
       "Fiji           1  \n",
       "Stormy         1  \n",
       "Pavlov         1  \n",
       "Marvin         1  \n",
       "Lillie         1  \n",
       "DonDon         1  \n",
       "Danny          1  \n",
       "Karll          1  \n",
       "BeBe           1  \n",
       "infuriating    1  \n",
       "Bodie          1  \n",
       "Kial           1  \n",
       "Pepper         1  \n",
       "Name: name, Length: 957, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# similar dog names\n",
    "twitter_archive.name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1976"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dogs with zero slang category\n",
    "twitter_archive.query('doggo ==\"None\" & floofer == \"None\" & puppo == \"None\" & pupper == \"None\"').shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change the slang category columns to numerical\n",
    "twitter_archive[['doggo','floofer','pupper','puppo']] = pd.get_dummies(twitter_archive, prefix = ['dog_','dog_','dog_','dog_'], columns =  ['doggo','floofer','pupper','puppo'])[['dog__doggo','dog__floofer','dog__pupper','dog__puppo']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1976\n",
       "1    366 \n",
       "2    14  \n",
       "Name: num_slang_categories, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a num_slang_categories column and check if a dog has more than one category\n",
    "slang_category = ['doggo', 'floofer', 'pupper', 'puppo']\n",
    "twitter_archive['num_slang_categories'] = twitter_archive.loc[:,slang_category].sum(axis = 1)\n",
    "twitter_archive['num_slang_categories'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>@docmisterio account started on 11/15/15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp;amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                    text  \\\n",
       "342   @docmisterio account started on 11/15/15                                                                                                             \n",
       "433   The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd                                                  \n",
       "784   RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…         \n",
       "902   Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE                                                                       \n",
       "1068  After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ         \n",
       "1120  Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv                            \n",
       "1165  Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a                                                                                     \n",
       "1202  This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq                                \n",
       "1228  Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1                                                        \n",
       "1254  Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12                                         \n",
       "1274  From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK   \n",
       "1351  Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa                                                                   \n",
       "1433  Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ                                                         \n",
       "1598  Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating             \n",
       "1634  Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3                       \n",
       "1635  Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55         \n",
       "1662  This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5          \n",
       "1663  I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible          \n",
       "1779  IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq                                                                               \n",
       "1843  Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw                                          \n",
       "\n",
       "      rating_denominator  \n",
       "342   15                  \n",
       "433   70                  \n",
       "784   11                  \n",
       "902   150                 \n",
       "1068  11                  \n",
       "1120  170                 \n",
       "1165  20                  \n",
       "1202  50                  \n",
       "1228  90                  \n",
       "1254  80                  \n",
       "1274  50                  \n",
       "1351  50                  \n",
       "1433  40                  \n",
       "1598  20                  \n",
       "1634  130                 \n",
       "1635  110                 \n",
       "1662  11                  \n",
       "1663  16                  \n",
       "1779  120                 \n",
       "1843  80                  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for rating discrepancies -- denominator should be 10 also check the text\n",
    "twitter_archive.query('rating_denominator > 10')[['text', 'rating_denominator']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id    2075\n",
       "jpg_url     2009\n",
       "img_num     4   \n",
       "p1          378 \n",
       "p1_conf     2006\n",
       "p1_dog      2   \n",
       "p2          405 \n",
       "p2_conf     2004\n",
       "p2_dog      2   \n",
       "p3          408 \n",
       "p3_conf     2006\n",
       "p3_dog      2   \n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicates in image_preditons dataset\n",
    "image_predictions.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2073\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "#check type of images\n",
    "print(image_predictions[image_predictions['jpg_url'].str[-4:] == '.jpg'].shape[0]) \n",
    "print(image_predictions[image_predictions['jpg_url'].str[-4:] == '.png'].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "golden_retriever             150\n",
      "Labrador_retriever           100\n",
      "Pembroke                     89 \n",
      "Chihuahua                    83 \n",
      "pug                          57 \n",
      "chow                         44 \n",
      "Samoyed                      43 \n",
      "toy_poodle                   39 \n",
      "Pomeranian                   38 \n",
      "malamute                     30 \n",
      "cocker_spaniel               30 \n",
      "French_bulldog               26 \n",
      "miniature_pinscher           23 \n",
      "Chesapeake_Bay_retriever     23 \n",
      "seat_belt                    22 \n",
      "Siberian_husky               20 \n",
      "German_shepherd              20 \n",
      "Staffordshire_bullterrier    20 \n",
      "Cardigan                     19 \n",
      "web_site                     19 \n",
      "beagle                       18 \n",
      "Eskimo_dog                   18 \n",
      "teddy                        18 \n",
      "Shetland_sheepdog            18 \n",
      "Maltese_dog                  18 \n",
      "Lakeland_terrier             17 \n",
      "Rottweiler                   17 \n",
      "Shih-Tzu                     17 \n",
      "kuvasz                       16 \n",
      "Italian_greyhound            16 \n",
      "                             .. \n",
      "three-toed_sloth             1  \n",
      "envelope                     1  \n",
      "skunk                        1  \n",
      "swab                         1  \n",
      "killer_whale                 1  \n",
      "studio_couch                 1  \n",
      "coil                         1  \n",
      "sundial                      1  \n",
      "clumber                      1  \n",
      "long-horned_beetle           1  \n",
      "microwave                    1  \n",
      "bonnet                       1  \n",
      "harp                         1  \n",
      "crash_helmet                 1  \n",
      "bald_eagle                   1  \n",
      "beach_wagon                  1  \n",
      "tiger_shark                  1  \n",
      "polecat                      1  \n",
      "pot                          1  \n",
      "fire_engine                  1  \n",
      "wooden_spoon                 1  \n",
      "syringe                      1  \n",
      "cuirass                      1  \n",
      "barbell                      1  \n",
      "lacewing                     1  \n",
      "marmot                       1  \n",
      "crane                        1  \n",
      "standard_schnauzer           1  \n",
      "Scotch_terrier               1  \n",
      "cheetah                      1  \n",
      "Name: p1, Length: 378, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#predictions value counts\n",
    "print(image_predictions.p1.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                2331\n",
       "retweet_count     1682\n",
       "favorite_count    1987\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tweet_data unique data\n",
    "tweet_data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assessments Report\n",
    "\n",
    "##### Quality\n",
    "`twitter_archived`\n",
    "-  Useless columns like in_`reply_to_status_id`, `in_reply_to_user_id`, etc.\n",
    "-  Data-type of `timestamp` is object which is wrong, it should be datetime.\n",
    "-  `rating_numerator` and `rating_denominator` data is wrong, in some places it is confused with a date present in the text column. While in some places it is used to rate more than one dog, so the rating is multiplied by number of dogs that are being rated.\n",
    "- Data type of `rating_numerator` is float in some tweets, but the dataset has set its type to int\n",
    "- 1976 dogs do not have any slang_category while 14 dogs have more than one category. \n",
    "- `name` column has 745 None entries and some entries are articles like a , an and the. This should be Null data.\n",
    "- The data also includes re-tweets which are not needed.\n",
    "\n",
    "`image_predictions`\n",
    "- The `twitter_archived` has 2356 rows but `image_predictions` has 2075 rows\n",
    "- The `jpg_url` column has 66 duplicate entries. Only 2099 unique entries are present.\n",
    "- The dog breed names in predictions are inconsistent, some names include underscores.\n",
    "- The column names are not descriptive\n",
    "\n",
    "`tweet_data`\n",
    "- Missing rows, only 2331 rows could be accessed.\n",
    "- data type of `id` should be int instead of float.\n",
    "- 25 rows contain only Null values they must be removed.\n",
    "\n",
    "##### Tidiness\n",
    "`twitter_archived`\n",
    "- Headers contain values instead of categories. A slang_category header must be added that contains the values like `doggo`, `floofer`, etc.\n",
    "- `rating_numerator` and `denominator` belong in a single column as `rating` in the str format numerator/denominator.\n",
    "- Create another column `rating_num` that stores the floating point value of numerator/denominator for the purpose of analysis.\n",
    "\n",
    "`image_predictions`\n",
    "- Merge with `twitter_archive`\n",
    "- Merge with `tweet_data`\n",
    "\n",
    "`tweet_data`\n",
    "- Merge with `twitter_archive`\n",
    "- Merge with `image_predictions`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'clean'></a>\n",
    "\n",
    "## Cleaning\n",
    "\n",
    "\n",
    "### Define\n",
    "\n",
    "`twitter_archive`\n",
    "- Remove rows and columns related to retweet data\n",
    "- Change the `timestamp` from object to datetime data\n",
    "- Merge the `slang_category` columns into a single column use headers as values\n",
    "- Extract proper ratings from text, if ratings are multiples of 10 divide the numerator and denominator by the muliple.\n",
    "- Merge the `rating_numerator` and `rating_denominator` columns\n",
    "- Create a `rating_num` column that stores the floating point value of `rating_numerator` / `rating_denominator`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive_clean = twitter_archive.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change datatype of `timestamp`\n",
    "twitter_archive_clean.timestamp = pd.to_datetime(twitter_archive['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the retweet rows\n",
    "twitter_archive_clean = twitter_archive_clean[twitter_archive_clean.retweeted_status_id.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unnecessary columns like `in_reply_to_status_id`\n",
    "twitter_archive_clean.drop(['in_reply_to_status_id','in_reply_to_user_id','retweeted_status_id','retweeted_status_user_id','retweeted_status_timestamp'],axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change category values to 0 dogs which have more than one category\n",
    "twitter_archive_clean.loc[twitter_archive_clean['num_slang_categories'] > 1,['doggo','floofer','pupper','puppo', 'num_slang_categories']] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a list that contains index wise slangcategories\n",
    "slang_categories = ['doggo', 'floofer', 'pupper', 'puppo']\n",
    "twitter_archive_clean['slang_category'] = np.NaN\n",
    "category_list = np.array(list())\n",
    "for i in twitter_archive_clean.index:\n",
    "        if twitter_archive_clean.loc[i,'num_slang_categories'] == 0:\n",
    "            category_list = np.append(category_list,np.NaN)\n",
    "        else:\n",
    "            for y in slang_category:\n",
    "                if twitter_archive_clean.loc[i,y] == 1:\n",
    "                    category_list = np.append(category_list, y)\n",
    "\n",
    "# add slang_category column to dataframe\n",
    "twitter_archive_clean['slang_category'] = category_list\n",
    "twitter_archive_clean['slang_category'] = twitter_archive_clean['slang_category'].replace({'nan':np.NaN})\n",
    "# drop the unnecessary solumns\n",
    "twitter_archive_clean.drop(['doggo','floofer','pupper','puppo','num_slang_categories'], axis = 1, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace names as \"None\", \"a\", \"the\", \"an\" with Null \n",
    "twitter_archive_clean.name = twitter_archive_clean.name.replace({'None' : np.NaN, \"a\":np.NaN, \"the\":np.NaN, \"an\":np.NaN})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rating_denominators that are greater than 10\n",
    "twitter_archive_clean.query('rating_denominator > 10')[['tweet_id', 'text', 'rating_denominator', 'rating_numerator']]\n",
    "#note that tweet id 832088576586297345 has date change rating_numerator and denominator = null\n",
    "twitter_archive_clean.loc[twitter_archive_clean['tweet_id'] == 832088576586297345,['rating_denominator','rating_numerator']] = np.NaN\n",
    "\n",
    "# the tweet with id 740373189193256964 includes a rating 14/10 but they mistook 9/11 as rating\n",
    "twitter_archive_clean.loc[twitter_archive_clean['tweet_id'] == 740373189193256964,['rating_denominator']] = 10\n",
    "twitter_archive_clean.loc[twitter_archive_clean['tweet_id'] == 740373189193256964,['rating_numerator']] = 14\n",
    "\n",
    "# the tweet with id 682962037429899265 includes a rating 10/10 but it was mistaken for 7/11 store\n",
    "twitter_archive_clean.loc[twitter_archive_clean['tweet_id'] == 682962037429899265,['rating_denominator']] = 10\n",
    "twitter_archive_clean.loc[twitter_archive_clean['tweet_id'] == 682962037429899265,['rating_numerator']] = 10\n",
    "\n",
    "#now the other rating_denominators are multiples of 10 because of an attempt to rate more than 1 dog here we will give all dogs equal rating\n",
    "indices = twitter_archive_clean.query('rating_denominator > 10').index\n",
    "for index in indices:\n",
    "    twitter_archive_clean.loc[index,'rating_numerator'] = twitter_archive_clean.loc[index,'rating_numerator'] *10 /twitter_archive_clean.loc[index,'rating_denominator']\n",
    "    twitter_archive_clean.loc[index,'rating_denominator'] =  twitter_archive_clean.loc[index,'rating_denominator'] *10 /twitter_archive_clean.loc[index,'rating_denominator']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tweets that contain floating point numbers in their text, get their position\n",
    "pos = twitter_archive_clean[twitter_archive_clean.text.str.contains(' \\d{1,2}\\.\\d+/10 ')].index\n",
    "#extract the rating numerator\n",
    "twitter_archive_clean.loc[pos,'rating_numerator']= twitter_archive_clean.loc[pos,'text'].str.extract(r'(\\d{1,2}\\.\\d+)/10').values.astype(float)\n",
    "# combine the contents of rating_numerator and denominator into rating column\n",
    "twitter_archive_clean['rating'] = twitter_archive_clean.rating_numerator.astype('str') + '/' + twitter_archive_clean.rating_denominator.astype('str').str[:-2] \n",
    "\n",
    "#create `rating_num` column\n",
    "twitter_archive_clean['rating_num'] = twitter_archive_clean.rating_numerator/twitter_archive_clean.rating_denominator\n",
    "# remove rating_numerator and rating_denominator\n",
    "twitter_archive_clean.drop(['rating_numerator', 'rating_denominator'], axis =1 , inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing \n",
    "`twitter_archive_clean`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>name</th>\n",
       "      <th>slang_category</th>\n",
       "      <th>rating</th>\n",
       "      <th>rating_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421306343426/photo/1</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181378084864/photo/1</td>\n",
       "      <td>Archie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0/10</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557279858688/photo/1</td>\n",
       "      <td>Darla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0/10</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>891087950875897856</td>\n",
       "      <td>2017-07-29 00:08:17</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Here we have a majestic great white breaching off South Africa's coast. Absolutely h*ckin breathtaking. 13/10 (IG: tucker_marlo) #BarkWeek https://t.co/kQ04fDDRmh</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891087950875897856/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>890971913173991426</td>\n",
       "      <td>2017-07-28 16:27:12</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Meet Jax. He enjoys ice cream so much he gets nervous around it. 13/10 help Jax enjoy more things by clicking below\\n\\nhttps://t.co/Zr4hWfAs1H https://t.co/tVJBRMnhxl</td>\n",
       "      <td>https://gofundme.com/ydvmve-surgery-for-jax,https://twitter.com/dog_rates/status/890971913173991426/photo/1</td>\n",
       "      <td>Jax</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>890729181411237888</td>\n",
       "      <td>2017-07-28 00:22:40</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>When you watch your owner call another dog a good boy but then they turn back to you and say you're a great boy. 13/10 https://t.co/v0nONBcwxq</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890729181411237888/photo/1,https://twitter.com/dog_rates/status/890729181411237888/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>890609185150312448</td>\n",
       "      <td>2017-07-27 16:25:51</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Zoey. She doesn't want to be one of the scary sharks. Just wants to be a snuggly pettable boatpet. 13/10 #BarkWeek https://t.co/9TwLuAGH0b</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890609185150312448/photo/1</td>\n",
       "      <td>Zoey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>890240255349198849</td>\n",
       "      <td>2017-07-26 15:59:51</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Cassie. She is a college pup. Studying international doggo communication and stick theory. 14/10 so elegant much sophisticate https://t.co/t1bfwz5S2A</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890240255349198849/photo/1</td>\n",
       "      <td>Cassie</td>\n",
       "      <td>doggo</td>\n",
       "      <td>14.0/10</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>890006608113172480</td>\n",
       "      <td>2017-07-26 00:31:25</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Koda. He is a South Australian deckshark. Deceptively deadly. Frighteningly majestic. 13/10 would risk a petting #BarkWeek https://t.co/dVPW0B0Mme</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890006608113172480/photo/1,https://twitter.com/dog_rates/status/890006608113172480/photo/1</td>\n",
       "      <td>Koda</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>889880896479866881</td>\n",
       "      <td>2017-07-25 16:11:53</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Bruno. He is a service shark. Only gets out of the water to assist you. 13/10 terrifyingly good boy https://t.co/u1XPQMl29g</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889880896479866881/photo/1</td>\n",
       "      <td>Bruno</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>889665388333682689</td>\n",
       "      <td>2017-07-25 01:55:32</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Here's a puppo that seems to be on the fence about something haha no but seriously someone help her. 13/10 https://t.co/BxvuXk0UCm</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889665388333682689/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>puppo</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>889638837579907072</td>\n",
       "      <td>2017-07-25 00:10:02</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Ted. He does his best. Sometimes that's not enough. But it's ok. 12/10 would assist https://t.co/f8dEDcrKSR</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889638837579907072/photo/1,https://twitter.com/dog_rates/status/889638837579907072/photo/1</td>\n",
       "      <td>Ted</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0/10</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>889531135344209921</td>\n",
       "      <td>2017-07-24 17:02:04</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Stuart. He's sporting his favorite fanny pack. Secretly filled with bones only. 13/10 puppared puppo #BarkWeek https://t.co/y70o6h3isq</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889531135344209921/photo/1</td>\n",
       "      <td>Stuart</td>\n",
       "      <td>puppo</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>889278841981685760</td>\n",
       "      <td>2017-07-24 00:19:32</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Oliver. You're witnessing one of his many brutal attacks. Seems to be playing with his victim. 13/10 fr*ckin frightening #BarkWeek https://t.co/WpHvrQedPb</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889278841981685760/video/1</td>\n",
       "      <td>Oliver</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>888917238123831296</td>\n",
       "      <td>2017-07-23 00:22:39</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Jim. He found a fren. Taught him how to sit like the good boys. 12/10 for both https://t.co/chxruIOUJN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888917238123831296/photo/1</td>\n",
       "      <td>Jim</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0/10</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>888804989199671297</td>\n",
       "      <td>2017-07-22 16:56:37</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Zeke. He has a new stick. Very proud of it. Would like you to throw it for him without taking it. 13/10 would do my best https://t.co/HTQ77yNQ5K</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888804989199671297/photo/1,https://twitter.com/dog_rates/status/888804989199671297/photo/1</td>\n",
       "      <td>Zeke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>888554962724278272</td>\n",
       "      <td>2017-07-22 00:23:06</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Ralphus. He's powering up. Attempting maximum borkdrive. 13/10 inspirational af https://t.co/YnYAFCTTiK</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1</td>\n",
       "      <td>Ralphus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>888078434458587136</td>\n",
       "      <td>2017-07-20 16:49:33</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Gerald. He was just told he didn't get the job he interviewed for. A h*ckin injustice. 12/10 didn't want the job anyway https://t.co/DK7iDPfuRX</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888078434458587136/photo/1,https://twitter.com/dog_rates/status/888078434458587136/photo/1</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0/10</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>887705289381826560</td>\n",
       "      <td>2017-07-19 16:06:48</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Jeffrey. He has a monopoly on the pool noodles. Currently running a 'boop for two' midweek sale. 13/10 h*ckin strategic https://t.co/PhrUk20Q64</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887705289381826560/photo/1</td>\n",
       "      <td>Jeffrey</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>887517139158093824</td>\n",
       "      <td>2017-07-19 03:39:09</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887517139158093824/video/1</td>\n",
       "      <td>such</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0/10</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>887473957103951883</td>\n",
       "      <td>2017-07-19 00:47:34</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887473957103951883/photo/1,https://twitter.com/dog_rates/status/887473957103951883/photo/1</td>\n",
       "      <td>Canela</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>887343217045368832</td>\n",
       "      <td>2017-07-18 16:08:03</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>You may not have known you needed to see this today. 13/10 please enjoy (IG: emmylouroo) https://t.co/WZqNqygEyV</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887343217045368832/video/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>887101392804085760</td>\n",
       "      <td>2017-07-18 00:07:08</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This... is a Jubilant Antarctic House Bear. We only rate dogs. Please only send dogs. Thank you... 12/10 would suffocate in floof https://t.co/4Ad1jzJSdp</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887101392804085760/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0/10</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>886983233522544640</td>\n",
       "      <td>2017-07-17 16:17:36</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Maya. She's very shy. Rarely leaves her cup. 13/10 would find her an environment to thrive in https://t.co/I6oNy0CgiT</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886983233522544640/photo/1,https://twitter.com/dog_rates/status/886983233522544640/photo/1</td>\n",
       "      <td>Maya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>886736880519319552</td>\n",
       "      <td>2017-07-16 23:58:41</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Mingus. He's a wonderful father to his smol pup. Confirmed 13/10, but he needs your help\\n\\nhttps://t.co/bVi0Yr4Cff https://t.co/ISvKOSkd5b</td>\n",
       "      <td>https://www.gofundme.com/mingusneedsus,https://twitter.com/dog_rates/status/886736880519319552/photo/1,https://twitter.com/dog_rates/status/886736880519319552/photo/1</td>\n",
       "      <td>Mingus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>886680336477933568</td>\n",
       "      <td>2017-07-16 20:14:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Derek. He's late for a dog meeting. 13/10 pet...al to the metal https://t.co/BCoWue0abA</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886680336477933568/photo/1</td>\n",
       "      <td>Derek</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0/10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>886366144734445568</td>\n",
       "      <td>2017-07-15 23:25:31</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Roscoe. Another pupper fallen victim to spontaneous tongue ejections. Get the BlepiPen immediate. 12/10 deep breaths Roscoe https://t.co/RGE08MIJox</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886366144734445568/photo/1,https://twitter.com/dog_rates/status/886366144734445568/photo/1</td>\n",
       "      <td>Roscoe</td>\n",
       "      <td>pupper</td>\n",
       "      <td>12.0/10</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>886267009285017600</td>\n",
       "      <td>2017-07-15 16:51:35</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>@NonWhiteHat @MayhewMayhem omg hello tanner you are a scary good boy 12/10 would pet with extreme caution</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0/10</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>666411507551481857</td>\n",
       "      <td>2015-11-17 00:24:19</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666411507551481857/photo/1</td>\n",
       "      <td>quite</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0/10</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>666407126856765440</td>\n",
       "      <td>2015-11-17 00:06:54</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is a southern Vesuvius bumblegruff. Can drive a truck (wow). Made friends with 5 other nifty dogs (neat). 7/10 https://t.co/LopTBkKa8h</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666407126856765440/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0/10</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>666396247373291520</td>\n",
       "      <td>2015-11-16 23:23:41</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Oh goodness. A super rare northeast Qdoba kangaroo mix. Massive feet. No pouch (disappointing). Seems alert. 9/10 https://t.co/Dc7b0E8qFE</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666396247373291520/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0/10</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2329</th>\n",
       "      <td>666373753744588802</td>\n",
       "      <td>2015-11-16 21:54:18</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Those are sunglasses and a jean jacket. 11/10 dog cool af https://t.co/uHXrPkUEyl</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666373753744588802/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0/10</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>666362758909284353</td>\n",
       "      <td>2015-11-16 21:10:36</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Unique dog here. Very small. Lives in container of Frosted Flakes (?). Short legs. Must be rare 6/10 would still pet https://t.co/XMD9CwjEnM</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666362758909284353/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0/10</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2331</th>\n",
       "      <td>666353288456101888</td>\n",
       "      <td>2015-11-16 20:32:58</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Here we have a mixed Asiago from the Galápagos Islands. Only one ear working. Big fan of marijuana carpet. 8/10 https://t.co/tltQ5w9aUO</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666353288456101888/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0/10</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2332</th>\n",
       "      <td>666345417576210432</td>\n",
       "      <td>2015-11-16 20:01:42</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Look at this jokester thinking seat belt laws don't apply to him. Great tongue tho 10/10 https://t.co/VFKG1vxGjB</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666345417576210432/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0/10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>666337882303524864</td>\n",
       "      <td>2015-11-16 19:31:45</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666337882303524864/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0/10</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>2015-11-16 16:37:02</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666293911632134144/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0/10</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>2015-11-16 16:11:11</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666287406224695296/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0/2</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2336</th>\n",
       "      <td>666273097616637952</td>\n",
       "      <td>2015-11-16 15:14:19</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Can take selfies 11/10 https://t.co/ws2AMaNwPW</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666273097616637952/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0/10</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>666268910803644416</td>\n",
       "      <td>2015-11-16 14:57:41</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Very concerned about fellow dog trapped in computer. 10/10 https://t.co/0yxApIikpk</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666268910803644416/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0/10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2338</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>2015-11-16 04:02:55</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666104133288665088/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0/10</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339</th>\n",
       "      <td>666102155909144576</td>\n",
       "      <td>2015-11-16 03:55:04</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Oh my. Here you are seeing an Adobe Setter giving birth to twins!!! The world is an amazing place. 11/10 https://t.co/11LvqN4WLq</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666102155909144576/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0/10</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2340</th>\n",
       "      <td>666099513787052032</td>\n",
       "      <td>2015-11-16 03:44:34</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Can stand on stump for what seems like a while. Built that birdhouse? Impressive. Made friends with a squirrel. 8/10 https://t.co/Ri4nMTLq5C</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666099513787052032/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0/10</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2341</th>\n",
       "      <td>666094000022159362</td>\n",
       "      <td>2015-11-16 03:22:39</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This appears to be a Mongolian Presbyterian mix. Very tired. Tongue slip confirmed. 9/10 would lie down with https://t.co/mnioXo3IfP</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666094000022159362/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0/10</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2342</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>2015-11-16 02:38:37</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Here we have a well-established sunblockerspaniel. Lost his other flip-flop. 6/10 not very waterproof https://t.co/3RU6x0vHB7</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666082916733198337/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0/10</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2343</th>\n",
       "      <td>666073100786774016</td>\n",
       "      <td>2015-11-16 01:59:36</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Let's hope this flight isn't Malaysian (lol). What a dog! Almost completely camouflaged. 10/10 I trust this pilot https://t.co/Yk6GHE9tOY</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666073100786774016/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0/10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2344</th>\n",
       "      <td>666071193221509120</td>\n",
       "      <td>2015-11-16 01:52:02</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Here we have a northern speckled Rhododendron. Much sass. Gives 0 fucks. Good tongue. 9/10 would caress sensually https://t.co/ZoL8kq2XFx</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666071193221509120/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0/10</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2345</th>\n",
       "      <td>666063827256086533</td>\n",
       "      <td>2015-11-16 01:22:45</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666063827256086533/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0/10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2346</th>\n",
       "      <td>666058600524156928</td>\n",
       "      <td>2015-11-16 01:01:59</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666058600524156928/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0/10</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>2015-11-16 00:55:59</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666057090499244032/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0/10</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>2015-11-16 00:49:46</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Here is a Siberian heavily armored polar bear mix. Strong owner. 10/10 I would do unspeakable things to pet this dog https://t.co/rdivxLiqEt</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666055525042405380/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0/10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>2015-11-16 00:35:11</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666051853826850816/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0/10</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>2015-11-16 00:30:50</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666050758794694657/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0/10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>2015-11-16 00:24:50</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248165822465/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0/10</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>2015-11-16 00:04:52</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226329800704/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0/10</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>2015-11-15 23:21:54</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412701032449/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0/10</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>2015-11-15 23:05:30</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285002620928/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0/10</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye in Vietnam (?). Big fan of relaxing on stair. 8/10 would pet https://t.co/BLDqew2Ijj</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888022790149/photo/1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0/10</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2175 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id           timestamp  \\\n",
       "0     892420643555336193 2017-08-01 16:23:56   \n",
       "1     892177421306343426 2017-08-01 00:17:27   \n",
       "2     891815181378084864 2017-07-31 00:18:03   \n",
       "3     891689557279858688 2017-07-30 15:58:51   \n",
       "4     891327558926688256 2017-07-29 16:00:24   \n",
       "5     891087950875897856 2017-07-29 00:08:17   \n",
       "6     890971913173991426 2017-07-28 16:27:12   \n",
       "7     890729181411237888 2017-07-28 00:22:40   \n",
       "8     890609185150312448 2017-07-27 16:25:51   \n",
       "9     890240255349198849 2017-07-26 15:59:51   \n",
       "10    890006608113172480 2017-07-26 00:31:25   \n",
       "11    889880896479866881 2017-07-25 16:11:53   \n",
       "12    889665388333682689 2017-07-25 01:55:32   \n",
       "13    889638837579907072 2017-07-25 00:10:02   \n",
       "14    889531135344209921 2017-07-24 17:02:04   \n",
       "15    889278841981685760 2017-07-24 00:19:32   \n",
       "16    888917238123831296 2017-07-23 00:22:39   \n",
       "17    888804989199671297 2017-07-22 16:56:37   \n",
       "18    888554962724278272 2017-07-22 00:23:06   \n",
       "20    888078434458587136 2017-07-20 16:49:33   \n",
       "21    887705289381826560 2017-07-19 16:06:48   \n",
       "22    887517139158093824 2017-07-19 03:39:09   \n",
       "23    887473957103951883 2017-07-19 00:47:34   \n",
       "24    887343217045368832 2017-07-18 16:08:03   \n",
       "25    887101392804085760 2017-07-18 00:07:08   \n",
       "26    886983233522544640 2017-07-17 16:17:36   \n",
       "27    886736880519319552 2017-07-16 23:58:41   \n",
       "28    886680336477933568 2017-07-16 20:14:00   \n",
       "29    886366144734445568 2017-07-15 23:25:31   \n",
       "30    886267009285017600 2017-07-15 16:51:35   \n",
       "...                  ...                 ...   \n",
       "2326  666411507551481857 2015-11-17 00:24:19   \n",
       "2327  666407126856765440 2015-11-17 00:06:54   \n",
       "2328  666396247373291520 2015-11-16 23:23:41   \n",
       "2329  666373753744588802 2015-11-16 21:54:18   \n",
       "2330  666362758909284353 2015-11-16 21:10:36   \n",
       "2331  666353288456101888 2015-11-16 20:32:58   \n",
       "2332  666345417576210432 2015-11-16 20:01:42   \n",
       "2333  666337882303524864 2015-11-16 19:31:45   \n",
       "2334  666293911632134144 2015-11-16 16:37:02   \n",
       "2335  666287406224695296 2015-11-16 16:11:11   \n",
       "2336  666273097616637952 2015-11-16 15:14:19   \n",
       "2337  666268910803644416 2015-11-16 14:57:41   \n",
       "2338  666104133288665088 2015-11-16 04:02:55   \n",
       "2339  666102155909144576 2015-11-16 03:55:04   \n",
       "2340  666099513787052032 2015-11-16 03:44:34   \n",
       "2341  666094000022159362 2015-11-16 03:22:39   \n",
       "2342  666082916733198337 2015-11-16 02:38:37   \n",
       "2343  666073100786774016 2015-11-16 01:59:36   \n",
       "2344  666071193221509120 2015-11-16 01:52:02   \n",
       "2345  666063827256086533 2015-11-16 01:22:45   \n",
       "2346  666058600524156928 2015-11-16 01:01:59   \n",
       "2347  666057090499244032 2015-11-16 00:55:59   \n",
       "2348  666055525042405380 2015-11-16 00:49:46   \n",
       "2349  666051853826850816 2015-11-16 00:35:11   \n",
       "2350  666050758794694657 2015-11-16 00:30:50   \n",
       "2351  666049248165822465 2015-11-16 00:24:50   \n",
       "2352  666044226329800704 2015-11-16 00:04:52   \n",
       "2353  666033412701032449 2015-11-15 23:21:54   \n",
       "2354  666029285002620928 2015-11-15 23:05:30   \n",
       "2355  666020888022790149 2015-11-15 22:32:08   \n",
       "\n",
       "                                                                                  source  \\\n",
       "0     <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1     <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2     <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
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       "8     <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "9     <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "10    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "11    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "12    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "13    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "14    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "15    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
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       "17    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
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       "24    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "25    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "26    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "27    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "28    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "29    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "30    <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "...                                                                                  ...   \n",
       "2326  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
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       "2330  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2331  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2332  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2333  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2334  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2335  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2336  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2337  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2338  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2339  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2340  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2341  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2342  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2343  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2344  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2345  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2346  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2347  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2348  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2349  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2350  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                                                                        text  \\\n",
       "0     This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU                                                            \n",
       "1     This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV       \n",
       "2     This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB                        \n",
       "3     This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ                                                                  \n",
       "4     This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f       \n",
       "5     Here we have a majestic great white breaching off South Africa's coast. Absolutely h*ckin breathtaking. 13/10 (IG: tucker_marlo) #BarkWeek https://t.co/kQ04fDDRmh       \n",
       "6     Meet Jax. He enjoys ice cream so much he gets nervous around it. 13/10 help Jax enjoy more things by clicking below\\n\\nhttps://t.co/Zr4hWfAs1H https://t.co/tVJBRMnhxl   \n",
       "7     When you watch your owner call another dog a good boy but then they turn back to you and say you're a great boy. 13/10 https://t.co/v0nONBcwxq                           \n",
       "8     This is Zoey. She doesn't want to be one of the scary sharks. Just wants to be a snuggly pettable boatpet. 13/10 #BarkWeek https://t.co/9TwLuAGH0b                       \n",
       "9     This is Cassie. She is a college pup. Studying international doggo communication and stick theory. 14/10 so elegant much sophisticate https://t.co/t1bfwz5S2A            \n",
       "10    This is Koda. He is a South Australian deckshark. Deceptively deadly. Frighteningly majestic. 13/10 would risk a petting #BarkWeek https://t.co/dVPW0B0Mme               \n",
       "11    This is Bruno. He is a service shark. Only gets out of the water to assist you. 13/10 terrifyingly good boy https://t.co/u1XPQMl29g                                      \n",
       "12    Here's a puppo that seems to be on the fence about something haha no but seriously someone help her. 13/10 https://t.co/BxvuXk0UCm                                       \n",
       "13    This is Ted. He does his best. Sometimes that's not enough. But it's ok. 12/10 would assist https://t.co/f8dEDcrKSR                                                      \n",
       "14    This is Stuart. He's sporting his favorite fanny pack. Secretly filled with bones only. 13/10 puppared puppo #BarkWeek https://t.co/y70o6h3isq                           \n",
       "15    This is Oliver. You're witnessing one of his many brutal attacks. Seems to be playing with his victim. 13/10 fr*ckin frightening #BarkWeek https://t.co/WpHvrQedPb       \n",
       "16    This is Jim. He found a fren. Taught him how to sit like the good boys. 12/10 for both https://t.co/chxruIOUJN                                                           \n",
       "17    This is Zeke. He has a new stick. Very proud of it. Would like you to throw it for him without taking it. 13/10 would do my best https://t.co/HTQ77yNQ5K                 \n",
       "18    This is Ralphus. He's powering up. Attempting maximum borkdrive. 13/10 inspirational af https://t.co/YnYAFCTTiK                                                          \n",
       "20    This is Gerald. He was just told he didn't get the job he interviewed for. A h*ckin injustice. 12/10 didn't want the job anyway https://t.co/DK7iDPfuRX                  \n",
       "21    This is Jeffrey. He has a monopoly on the pool noodles. Currently running a 'boop for two' midweek sale. 13/10 h*ckin strategic https://t.co/PhrUk20Q64                  \n",
       "22    I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba                                     \n",
       "23    This is Canela. She attempted some fancy porch pics. They were unsuccessful. 13/10 someone help her https://t.co/cLyzpcUcMX                                              \n",
       "24    You may not have known you needed to see this today. 13/10 please enjoy (IG: emmylouroo) https://t.co/WZqNqygEyV                                                         \n",
       "25    This... is a Jubilant Antarctic House Bear. We only rate dogs. Please only send dogs. Thank you... 12/10 would suffocate in floof https://t.co/4Ad1jzJSdp                \n",
       "26    This is Maya. She's very shy. Rarely leaves her cup. 13/10 would find her an environment to thrive in https://t.co/I6oNy0CgiT                                            \n",
       "27    This is Mingus. He's a wonderful father to his smol pup. Confirmed 13/10, but he needs your help\\n\\nhttps://t.co/bVi0Yr4Cff https://t.co/ISvKOSkd5b                      \n",
       "28    This is Derek. He's late for a dog meeting. 13/10 pet...al to the metal https://t.co/BCoWue0abA                                                                          \n",
       "29    This is Roscoe. Another pupper fallen victim to spontaneous tongue ejections. Get the BlepiPen immediate. 12/10 deep breaths Roscoe https://t.co/RGE08MIJox              \n",
       "30    @NonWhiteHat @MayhewMayhem omg hello tanner you are a scary good boy 12/10 would pet with extreme caution                                                                \n",
       "...                                                                                                         ...                                                                \n",
       "2326  This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t                             \n",
       "2327  This is a southern Vesuvius bumblegruff. Can drive a truck (wow). Made friends with 5 other nifty dogs (neat). 7/10 https://t.co/LopTBkKa8h                              \n",
       "2328  Oh goodness. A super rare northeast Qdoba kangaroo mix. Massive feet. No pouch (disappointing). Seems alert. 9/10 https://t.co/Dc7b0E8qFE                                \n",
       "2329  Those are sunglasses and a jean jacket. 11/10 dog cool af https://t.co/uHXrPkUEyl                                                                                        \n",
       "2330  Unique dog here. Very small. Lives in container of Frosted Flakes (?). Short legs. Must be rare 6/10 would still pet https://t.co/XMD9CwjEnM                             \n",
       "2331  Here we have a mixed Asiago from the Galápagos Islands. Only one ear working. Big fan of marijuana carpet. 8/10 https://t.co/tltQ5w9aUO                                  \n",
       "2332  Look at this jokester thinking seat belt laws don't apply to him. Great tongue tho 10/10 https://t.co/VFKG1vxGjB                                                         \n",
       "2333  This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL                              \n",
       "2334  This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0                               \n",
       "2335  This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv                                 \n",
       "2336  Can take selfies 11/10 https://t.co/ws2AMaNwPW                                                                                                                           \n",
       "2337  Very concerned about fellow dog trapped in computer. 10/10 https://t.co/0yxApIikpk                                                                                       \n",
       "2338  Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX                                   \n",
       "2339  Oh my. Here you are seeing an Adobe Setter giving birth to twins!!! The world is an amazing place. 11/10 https://t.co/11LvqN4WLq                                         \n",
       "2340  Can stand on stump for what seems like a while. Built that birdhouse? Impressive. Made friends with a squirrel. 8/10 https://t.co/Ri4nMTLq5C                             \n",
       "2341  This appears to be a Mongolian Presbyterian mix. Very tired. Tongue slip confirmed. 9/10 would lie down with https://t.co/mnioXo3IfP                                     \n",
       "2342  Here we have a well-established sunblockerspaniel. Lost his other flip-flop. 6/10 not very waterproof https://t.co/3RU6x0vHB7                                            \n",
       "2343  Let's hope this flight isn't Malaysian (lol). What a dog! Almost completely camouflaged. 10/10 I trust this pilot https://t.co/Yk6GHE9tOY                                \n",
       "2344  Here we have a northern speckled Rhododendron. Much sass. Gives 0 fucks. Good tongue. 9/10 would caress sensually https://t.co/ZoL8kq2XFx                                \n",
       "2345  This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK                                                              \n",
       "2346  Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p                                  \n",
       "2347  My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O                                             \n",
       "2348  Here is a Siberian heavily armored polar bear mix. Strong owner. 10/10 I would do unspeakable things to pet this dog https://t.co/rdivxLiqEt                             \n",
       "2349  This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc                               \n",
       "2350  This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe                             \n",
       "2351  Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq                                                 \n",
       "2352  This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx                                \n",
       "2353  Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR                                       \n",
       "2354  This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI                              \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye in Vietnam (?). Big fan of relaxing on stair. 8/10 would pet https://t.co/BLDqew2Ijj                                      \n",
       "\n",
       "                                                                                                                                                                                                                                                        expanded_urls  \\\n",
       "0     https://twitter.com/dog_rates/status/892420643555336193/photo/1                                                                                                                                                                                                   \n",
       "1     https://twitter.com/dog_rates/status/892177421306343426/photo/1                                                                                                                                                                                                   \n",
       "2     https://twitter.com/dog_rates/status/891815181378084864/photo/1                                                                                                                                                                                                   \n",
       "3     https://twitter.com/dog_rates/status/891689557279858688/photo/1                                                                                                                                                                                                   \n",
       "4     https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1                                                                                                                                   \n",
       "5     https://twitter.com/dog_rates/status/891087950875897856/photo/1                                                                                                                                                                                                   \n",
       "6     https://gofundme.com/ydvmve-surgery-for-jax,https://twitter.com/dog_rates/status/890971913173991426/photo/1                                                                                                                                                       \n",
       "7     https://twitter.com/dog_rates/status/890729181411237888/photo/1,https://twitter.com/dog_rates/status/890729181411237888/photo/1                                                                                                                                   \n",
       "8     https://twitter.com/dog_rates/status/890609185150312448/photo/1                                                                                                                                                                                                   \n",
       "9     https://twitter.com/dog_rates/status/890240255349198849/photo/1                                                                                                                                                                                                   \n",
       "10    https://twitter.com/dog_rates/status/890006608113172480/photo/1,https://twitter.com/dog_rates/status/890006608113172480/photo/1                                                                                                                                   \n",
       "11    https://twitter.com/dog_rates/status/889880896479866881/photo/1                                                                                                                                                                                                   \n",
       "12    https://twitter.com/dog_rates/status/889665388333682689/photo/1                                                                                                                                                                                                   \n",
       "13    https://twitter.com/dog_rates/status/889638837579907072/photo/1,https://twitter.com/dog_rates/status/889638837579907072/photo/1                                                                                                                                   \n",
       "14    https://twitter.com/dog_rates/status/889531135344209921/photo/1                                                                                                                                                                                                   \n",
       "15    https://twitter.com/dog_rates/status/889278841981685760/video/1                                                                                                                                                                                                   \n",
       "16    https://twitter.com/dog_rates/status/888917238123831296/photo/1                                                                                                                                                                                                   \n",
       "17    https://twitter.com/dog_rates/status/888804989199671297/photo/1,https://twitter.com/dog_rates/status/888804989199671297/photo/1                                                                                                                                   \n",
       "18    https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1,https://twitter.com/dog_rates/status/888554962724278272/photo/1   \n",
       "20    https://twitter.com/dog_rates/status/888078434458587136/photo/1,https://twitter.com/dog_rates/status/888078434458587136/photo/1                                                                                                                                   \n",
       "21    https://twitter.com/dog_rates/status/887705289381826560/photo/1                                                                                                                                                                                                   \n",
       "22    https://twitter.com/dog_rates/status/887517139158093824/video/1                                                                                                                                                                                                   \n",
       "23    https://twitter.com/dog_rates/status/887473957103951883/photo/1,https://twitter.com/dog_rates/status/887473957103951883/photo/1                                                                                                                                   \n",
       "24    https://twitter.com/dog_rates/status/887343217045368832/video/1                                                                                                                                                                                                   \n",
       "25    https://twitter.com/dog_rates/status/887101392804085760/photo/1                                                                                                                                                                                                   \n",
       "26    https://twitter.com/dog_rates/status/886983233522544640/photo/1,https://twitter.com/dog_rates/status/886983233522544640/photo/1                                                                                                                                   \n",
       "27    https://www.gofundme.com/mingusneedsus,https://twitter.com/dog_rates/status/886736880519319552/photo/1,https://twitter.com/dog_rates/status/886736880519319552/photo/1                                                                                            \n",
       "28    https://twitter.com/dog_rates/status/886680336477933568/photo/1                                                                                                                                                                                                   \n",
       "29    https://twitter.com/dog_rates/status/886366144734445568/photo/1,https://twitter.com/dog_rates/status/886366144734445568/photo/1                                                                                                                                   \n",
       "30    NaN                                                                                                                                                                                                                                                               \n",
       "...   ...                                                                                                                                                                                                                                                               \n",
       "2326  https://twitter.com/dog_rates/status/666411507551481857/photo/1                                                                                                                                                                                                   \n",
       "2327  https://twitter.com/dog_rates/status/666407126856765440/photo/1                                                                                                                                                                                                   \n",
       "2328  https://twitter.com/dog_rates/status/666396247373291520/photo/1                                                                                                                                                                                                   \n",
       "2329  https://twitter.com/dog_rates/status/666373753744588802/photo/1                                                                                                                                                                                                   \n",
       "2330  https://twitter.com/dog_rates/status/666362758909284353/photo/1                                                                                                                                                                                                   \n",
       "2331  https://twitter.com/dog_rates/status/666353288456101888/photo/1                                                                                                                                                                                                   \n",
       "2332  https://twitter.com/dog_rates/status/666345417576210432/photo/1                                                                                                                                                                                                   \n",
       "2333  https://twitter.com/dog_rates/status/666337882303524864/photo/1                                                                                                                                                                                                   \n",
       "2334  https://twitter.com/dog_rates/status/666293911632134144/photo/1                                                                                                                                                                                                   \n",
       "2335  https://twitter.com/dog_rates/status/666287406224695296/photo/1                                                                                                                                                                                                   \n",
       "2336  https://twitter.com/dog_rates/status/666273097616637952/photo/1                                                                                                                                                                                                   \n",
       "2337  https://twitter.com/dog_rates/status/666268910803644416/photo/1                                                                                                                                                                                                   \n",
       "2338  https://twitter.com/dog_rates/status/666104133288665088/photo/1                                                                                                                                                                                                   \n",
       "2339  https://twitter.com/dog_rates/status/666102155909144576/photo/1                                                                                                                                                                                                   \n",
       "2340  https://twitter.com/dog_rates/status/666099513787052032/photo/1                                                                                                                                                                                                   \n",
       "2341  https://twitter.com/dog_rates/status/666094000022159362/photo/1                                                                                                                                                                                                   \n",
       "2342  https://twitter.com/dog_rates/status/666082916733198337/photo/1                                                                                                                                                                                                   \n",
       "2343  https://twitter.com/dog_rates/status/666073100786774016/photo/1                                                                                                                                                                                                   \n",
       "2344  https://twitter.com/dog_rates/status/666071193221509120/photo/1                                                                                                                                                                                                   \n",
       "2345  https://twitter.com/dog_rates/status/666063827256086533/photo/1                                                                                                                                                                                                   \n",
       "2346  https://twitter.com/dog_rates/status/666058600524156928/photo/1                                                                                                                                                                                                   \n",
       "2347  https://twitter.com/dog_rates/status/666057090499244032/photo/1                                                                                                                                                                                                   \n",
       "2348  https://twitter.com/dog_rates/status/666055525042405380/photo/1                                                                                                                                                                                                   \n",
       "2349  https://twitter.com/dog_rates/status/666051853826850816/photo/1                                                                                                                                                                                                   \n",
       "2350  https://twitter.com/dog_rates/status/666050758794694657/photo/1                                                                                                                                                                                                   \n",
       "2351  https://twitter.com/dog_rates/status/666049248165822465/photo/1                                                                                                                                                                                                   \n",
       "2352  https://twitter.com/dog_rates/status/666044226329800704/photo/1                                                                                                                                                                                                   \n",
       "2353  https://twitter.com/dog_rates/status/666033412701032449/photo/1                                                                                                                                                                                                   \n",
       "2354  https://twitter.com/dog_rates/status/666029285002620928/photo/1                                                                                                                                                                                                   \n",
       "2355  https://twitter.com/dog_rates/status/666020888022790149/photo/1                                                                                                                                                                                                   \n",
       "\n",
       "          name slang_category   rating  rating_num  \n",
       "0     Phineas   NaN            13.0/10  1.3         \n",
       "1     Tilly     NaN            13.0/10  1.3         \n",
       "2     Archie    NaN            12.0/10  1.2         \n",
       "3     Darla     NaN            13.0/10  1.3         \n",
       "4     Franklin  NaN            12.0/10  1.2         \n",
       "5     NaN       NaN            13.0/10  1.3         \n",
       "6     Jax       NaN            13.0/10  1.3         \n",
       "7     NaN       NaN            13.0/10  1.3         \n",
       "8     Zoey      NaN            13.0/10  1.3         \n",
       "9     Cassie    doggo          14.0/10  1.4         \n",
       "10    Koda      NaN            13.0/10  1.3         \n",
       "11    Bruno     NaN            13.0/10  1.3         \n",
       "12    NaN       puppo          13.0/10  1.3         \n",
       "13    Ted       NaN            12.0/10  1.2         \n",
       "14    Stuart    puppo          13.0/10  1.3         \n",
       "15    Oliver    NaN            13.0/10  1.3         \n",
       "16    Jim       NaN            12.0/10  1.2         \n",
       "17    Zeke      NaN            13.0/10  1.3         \n",
       "18    Ralphus   NaN            13.0/10  1.3         \n",
       "20    Gerald    NaN            12.0/10  1.2         \n",
       "21    Jeffrey   NaN            13.0/10  1.3         \n",
       "22    such      NaN            14.0/10  1.4         \n",
       "23    Canela    NaN            13.0/10  1.3         \n",
       "24    NaN       NaN            13.0/10  1.3         \n",
       "25    NaN       NaN            12.0/10  1.2         \n",
       "26    Maya      NaN            13.0/10  1.3         \n",
       "27    Mingus    NaN            13.0/10  1.3         \n",
       "28    Derek     NaN            13.0/10  1.3         \n",
       "29    Roscoe    pupper         12.0/10  1.2         \n",
       "30    NaN       NaN            12.0/10  1.2         \n",
       "...   ...       ...                ...  ...         \n",
       "2326  quite     NaN            2.0/10   0.2         \n",
       "2327  NaN       NaN            7.0/10   0.7         \n",
       "2328  NaN       NaN            9.0/10   0.9         \n",
       "2329  NaN       NaN            11.0/10  1.1         \n",
       "2330  NaN       NaN            6.0/10   0.6         \n",
       "2331  NaN       NaN            8.0/10   0.8         \n",
       "2332  NaN       NaN            10.0/10  1.0         \n",
       "2333  NaN       NaN            9.0/10   0.9         \n",
       "2334  NaN       NaN            3.0/10   0.3         \n",
       "2335  NaN       NaN            1.0/2    0.5         \n",
       "2336  NaN       NaN            11.0/10  1.1         \n",
       "2337  NaN       NaN            10.0/10  1.0         \n",
       "2338  NaN       NaN            1.0/10   0.1         \n",
       "2339  NaN       NaN            11.0/10  1.1         \n",
       "2340  NaN       NaN            8.0/10   0.8         \n",
       "2341  NaN       NaN            9.0/10   0.9         \n",
       "2342  NaN       NaN            6.0/10   0.6         \n",
       "2343  NaN       NaN            10.0/10  1.0         \n",
       "2344  NaN       NaN            9.0/10   0.9         \n",
       "2345  NaN       NaN            10.0/10  1.0         \n",
       "2346  NaN       NaN            8.0/10   0.8         \n",
       "2347  NaN       NaN            9.0/10   0.9         \n",
       "2348  NaN       NaN            10.0/10  1.0         \n",
       "2349  NaN       NaN            2.0/10   0.2         \n",
       "2350  NaN       NaN            10.0/10  1.0         \n",
       "2351  NaN       NaN            5.0/10   0.5         \n",
       "2352  NaN       NaN            6.0/10   0.6         \n",
       "2353  NaN       NaN            9.0/10   0.9         \n",
       "2354  NaN       NaN            7.0/10   0.7         \n",
       "2355  NaN       NaN            8.0/10   0.8         \n",
       "\n",
       "[2175 rows x 9 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0       13.0/10\n",
       "1       13.0/10\n",
       "2       12.0/10\n",
       "3       13.0/10\n",
       "4       12.0/10\n",
       "5       13.0/10\n",
       "6       13.0/10\n",
       "7       13.0/10\n",
       "8       13.0/10\n",
       "9       14.0/10\n",
       "10      13.0/10\n",
       "11      13.0/10\n",
       "12      13.0/10\n",
       "13      12.0/10\n",
       "14      13.0/10\n",
       "15      13.0/10\n",
       "16      12.0/10\n",
       "17      13.0/10\n",
       "18      13.0/10\n",
       "20      12.0/10\n",
       "21      13.0/10\n",
       "22      14.0/10\n",
       "23      13.0/10\n",
       "24      13.0/10\n",
       "25      12.0/10\n",
       "26      13.0/10\n",
       "27      13.0/10\n",
       "28      13.0/10\n",
       "29      12.0/10\n",
       "30      12.0/10\n",
       "         ...   \n",
       "2326    2.0/10 \n",
       "2327    7.0/10 \n",
       "2328    9.0/10 \n",
       "2329    11.0/10\n",
       "2330    6.0/10 \n",
       "2331    8.0/10 \n",
       "2332    10.0/10\n",
       "2333    9.0/10 \n",
       "2334    3.0/10 \n",
       "2335    1.0/2  \n",
       "2336    11.0/10\n",
       "2337    10.0/10\n",
       "2338    1.0/10 \n",
       "2339    11.0/10\n",
       "2340    8.0/10 \n",
       "2341    9.0/10 \n",
       "2342    6.0/10 \n",
       "2343    10.0/10\n",
       "2344    9.0/10 \n",
       "2345    10.0/10\n",
       "2346    8.0/10 \n",
       "2347    9.0/10 \n",
       "2348    10.0/10\n",
       "2349    2.0/10 \n",
       "2350    10.0/10\n",
       "2351    5.0/10 \n",
       "2352    6.0/10 \n",
       "2353    9.0/10 \n",
       "2354    7.0/10 \n",
       "2355    8.0/10 \n",
       "Name: rating, Length: 2175, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Lucy        11\n",
       "Charlie     11\n",
       "Cooper      10\n",
       "Oliver      10\n",
       "Tucker      9 \n",
       "Penny       9 \n",
       "Winston     8 \n",
       "Lola        8 \n",
       "Sadie       8 \n",
       "Toby        7 \n",
       "Daisy       7 \n",
       "Bailey      6 \n",
       "Oscar       6 \n",
       "Koda        6 \n",
       "Bella       6 \n",
       "Bo          6 \n",
       "Jax         6 \n",
       "Stanley     6 \n",
       "Buddy       5 \n",
       "Bentley     5 \n",
       "Milo        5 \n",
       "Chester     5 \n",
       "Rusty       5 \n",
       "Leo         5 \n",
       "Louis       5 \n",
       "Scout       5 \n",
       "Dave        5 \n",
       "Finn        4 \n",
       "Oakley      4 \n",
       "Clarence    4 \n",
       "           .. \n",
       "Holly       1 \n",
       "Sundance    1 \n",
       "Tango       1 \n",
       "Maxwell     1 \n",
       "DayZ        1 \n",
       "Hercules    1 \n",
       "Aldrick     1 \n",
       "Mauve       1 \n",
       "Rhino       1 \n",
       "Tessa       1 \n",
       "Bert        1 \n",
       "Beemo       1 \n",
       "Meyer       1 \n",
       "Obie        1 \n",
       "Ralphie     1 \n",
       "Mo          1 \n",
       "Lulu        1 \n",
       "Blu         1 \n",
       "Kona        1 \n",
       "Noah        1 \n",
       "Hurley      1 \n",
       "Pipsy       1 \n",
       "Carbon      1 \n",
       "Clybe       1 \n",
       "Logan       1 \n",
       "Tanner      1 \n",
       "Ginger      1 \n",
       "Gin         1 \n",
       "Timmy       1 \n",
       "Pepper      1 \n",
       "Name: name, Length: 952, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2175 entries, 0 to 2355\n",
      "Data columns (total 9 columns):\n",
      "tweet_id          2175 non-null int64\n",
      "timestamp         2175 non-null datetime64[ns]\n",
      "source            2175 non-null object\n",
      "text              2175 non-null object\n",
      "expanded_urls     2117 non-null object\n",
      "name              1426 non-null object\n",
      "slang_category    332 non-null object\n",
      "rating            2175 non-null object\n",
      "rating_num        2174 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(6)\n",
      "memory usage: 249.9+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample\n",
    "display(twitter_archive_clean)\n",
    "# cleaned ratings\n",
    "display(twitter_archive_clean.rating)\n",
    "# verify that names are proper\n",
    "display(twitter_archive_clean.name.value_counts())\n",
    "#twitter_archived_clean summary\n",
    "display(twitter_archive_clean.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning\n",
    "\n",
    "`image_predictions`\n",
    "\n",
    "#### Define\n",
    "- Change column names so that they are descriptive.\n",
    "- Delete rows with duplicate url data.\n",
    "- Remove _ from dog breed names.\n",
    "- Set single prediction value and remove useless columns. \n",
    "- Merge with twitter_archive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create clone\n",
    "image_predictions_clean = image_predictions.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'jpg_url', 'img_num', 'p1', 'p1_conf', 'p1_dog', 'p2',\n",
       "       'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions_clean.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#descriptive column names\n",
    "image_predictions_clean.columns = [\n",
    "    'tweet_id',\n",
    "    'img_url',\n",
    "    'img_num',\n",
    "    'prediction1',\n",
    "    'prediction1_confidence',\n",
    "    'prediction1_is_correct',\n",
    "    'prediction2',\n",
    "    'prediction2_confidence',\n",
    "    'prediction2_is_correct',\n",
    "    'prediction3',\n",
    "    'prediction3_confidence',\n",
    "    'prediction3_is_correct',\n",
    "    \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#collect index for which prediction1 is correct\n",
    "p1_indices = image_predictions_clean[image_predictions_clean.prediction1_is_correct == True].index\n",
    "p2_indices = image_predictions_clean.query('prediction1_is_correct == False & prediction2_is_correct == True').index\n",
    "p3_indices = image_predictions_clean.query('prediction1_is_correct == False & prediction2_is_correct == False & prediction3_is_correct == True').index\n",
    "#create a new column predicted_breed and add predictions\n",
    "image_predictions_clean['predicted_breed'] = np.NaN\n",
    "image_predictions_clean.loc[p1_indices,'predicted_breed'] = image_predictions_clean.loc[p1_indices,'prediction1']\n",
    "image_predictions_clean.loc[p2_indices,'predicted_breed'] = image_predictions_clean.loc[p2_indices,'prediction2']\n",
    "image_predictions_clean.loc[p3_indices,'predicted_breed'] = image_predictions_clean.loc[p3_indices,'prediction3']\n",
    "#create a new column prediction_confidence and add the confidence values\n",
    "image_predictions_clean['prediction_confidence'] = np.NaN\n",
    "image_predictions_clean.loc[p1_indices,'prediction_confidence'] = image_predictions_clean.loc[p1_indices,'prediction1_confidence']\n",
    "image_predictions_clean.loc[p2_indices,'prediction_confidence'] = image_predictions_clean.loc[p2_indices,'prediction2_confidence']\n",
    "image_predictions_clean.loc[p3_indices,'prediction_confidence'] = image_predictions_clean.loc[p3_indices,'prediction3_confidence']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'img_url', 'img_num', 'prediction1',\n",
       "       'prediction1_confidence', 'prediction1_is_correct', 'prediction2',\n",
       "       'prediction2_confidence', 'prediction2_is_correct', 'prediction3',\n",
       "       'prediction3_confidence', 'prediction3_is_correct', 'predicted_breed',\n",
       "       'prediction_confidence'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop unnecessary columns \n",
    "image_predictions_clean.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_predictions_clean.drop(['prediction1',\n",
    "       'prediction1_confidence', 'prediction1_is_correct', 'prediction2',\n",
    "       'prediction2_confidence', 'prediction2_is_correct', 'prediction3',\n",
    "       'prediction3_confidence', 'prediction3_is_correct'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop duplicate images\n",
    "image_predictions_clean.drop_duplicates(subset = ['img_url'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assign proper strings to dog_breeds\n",
    "image_predictions_clean['predicted_breed'] = image_predictions_clean.predicted_breed.str.replace(\"_\", ' ').str.title() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge with twitter_archive\n",
    "twitter_archive_clean = pd.merge(twitter_archive_clean, image_predictions_clean, on = 'tweet_id', how = 'left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing \n",
    "`image_predictions_clean`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2009 entries, 0 to 2074\n",
      "Data columns (total 5 columns):\n",
      "tweet_id                 2009 non-null int64\n",
      "img_url                  2009 non-null object\n",
      "img_num                  2009 non-null int64\n",
      "predicted_breed          1691 non-null object\n",
      "prediction_confidence    1691 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 94.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# check dtypes and non-null values\n",
    "image_predictions_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2175 entries, 0 to 2174\n",
      "Data columns (total 13 columns):\n",
      "tweet_id                 2175 non-null int64\n",
      "timestamp                2175 non-null datetime64[ns]\n",
      "source                   2175 non-null object\n",
      "text                     2175 non-null object\n",
      "expanded_urls            2117 non-null object\n",
      "name                     1426 non-null object\n",
      "slang_category           332 non-null object\n",
      "rating                   2175 non-null object\n",
      "rating_num               2174 non-null float64\n",
      "img_url                  1994 non-null object\n",
      "img_num                  1994 non-null float64\n",
      "predicted_breed          1686 non-null object\n",
      "prediction_confidence    1686 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(3), int64(1), object(8)\n",
      "memory usage: 237.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# check if the datasets were merged properly\n",
    "twitter_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning \n",
    "`tweet_data`\n",
    "\n",
    "#### Define\n",
    "- Remove null data\n",
    "- Change data type of id to int\n",
    "- Change column name `id` to `tweet_id`\n",
    "- Change data type of `retweet_count`,`favorite_count` to int \n",
    "- Merge with `twitter_archive`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a copy of the data-set\n",
    "tweet_data_clean = tweet_data.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove null_data\n",
    "tweet_data_clean.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change id type to integer\n",
    "tweet_data_clean.id = tweet_data_clean.id.astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename columns id to `tweet_id`\n",
    "tweet_data_clean.rename(columns = {\"id\" : \"tweet_id\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change data type of retweet count and favorite_count to int\n",
    "tweet_data_clean.retweet_count = tweet_data_clean.retweet_count.astype('int')\n",
    "tweet_data_clean.favorite_count = tweet_data_clean.favorite_count.astype('int')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge with twitter_archive\n",
    "merged_df = pd.merge(twitter_archive_clean, tweet_data_clean, on = 'tweet_id', how = 'left')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2175 entries, 0 to 2174\n",
      "Data columns (total 15 columns):\n",
      "tweet_id                 2175 non-null int64\n",
      "timestamp                2175 non-null datetime64[ns]\n",
      "source                   2175 non-null object\n",
      "text                     2175 non-null object\n",
      "expanded_urls            2117 non-null object\n",
      "name                     1426 non-null object\n",
      "slang_category           332 non-null object\n",
      "rating                   2175 non-null object\n",
      "rating_num               2174 non-null float64\n",
      "img_url                  1994 non-null object\n",
      "img_num                  1994 non-null float64\n",
      "predicted_breed          1686 non-null object\n",
      "prediction_confidence    1686 non-null float64\n",
      "retweet_count            1417 non-null float64\n",
      "favorite_count           1417 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(5), int64(1), object(8)\n",
      "memory usage: 271.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking if merge was successful\n",
    "merged_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<a id = 'store'> </a>\n",
    "## Storing \n",
    "\n",
    "`merged_df`\n",
    "- Store the data-set in a csv file named `twitter_archive_master.csv`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df.to_csv('twitter_archive_master.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id ='analyse and visualize'></a>\n",
    "\n",
    "## Analyzing & Visualizing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     2175   \n",
       "unique    30     \n",
       "top       12.0/10\n",
       "freq      504    \n",
       "Name: rating, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.rating.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be observed that most common rating is 12/10, dogs were given this rating 504 times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Insight: Dog Breed with Most Retweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predicted_breed\n",
       "Golden Retriever               358957.0\n",
       "Pembroke                       185728.0\n",
       "Labrador Retriever             178721.0\n",
       "Chihuahua                      147133.0\n",
       "French Bulldog                 83052.0 \n",
       "Malamute                       71029.0 \n",
       "Samoyed                        67188.0 \n",
       "Pug                            63084.0 \n",
       "Chow                           62957.0 \n",
       "Cocker Spaniel                 61916.0 \n",
       "Pomeranian                     61127.0 \n",
       "Chesapeake Bay Retriever       56991.0 \n",
       "Cardigan                       56189.0 \n",
       "Standard Poodle                44988.0 \n",
       "Whippet                        43926.0 \n",
       "Toy Poodle                     43555.0 \n",
       "Bedlington Terrier             40239.0 \n",
       "Flat-Coated Retriever          32135.0 \n",
       "Norwegian Elkhound             30170.0 \n",
       "Rottweiler                     29347.0 \n",
       "Basset                         27647.0 \n",
       "Eskimo Dog                     27226.0 \n",
       "German Shepherd                27082.0 \n",
       "Shih-Tzu                       22762.0 \n",
       "Siberian Husky                 21347.0 \n",
       "Shetland Sheepdog              21216.0 \n",
       "Boxer                          21149.0 \n",
       "Pekinese                       21095.0 \n",
       "Kelpie                         18797.0 \n",
       "Great Pyrenees                 18634.0 \n",
       "                                ...    \n",
       "German Short-Haired Pointer    4143.0  \n",
       "Miniature Schnauzer            4113.0  \n",
       "Leonberg                       3635.0  \n",
       "Soft-Coated Wheaten Terrier    3545.0  \n",
       "Cairn                          2981.0  \n",
       "Lhasa                          2612.0  \n",
       "Welsh Springer Spaniel         2489.0  \n",
       "Newfoundland                   2450.0  \n",
       "Bluetick                       2328.0  \n",
       "Rhodesian Ridgeback            2312.0  \n",
       "Komondor                       2303.0  \n",
       "Doberman                       2177.0  \n",
       "Sussex Spaniel                 2166.0  \n",
       "Greater Swiss Mountain Dog     1925.0  \n",
       "Scottish Deerhound             1900.0  \n",
       "Tibetan Terrier                1674.0  \n",
       "Silky Terrier                  1644.0  \n",
       "Clumber                        1633.0  \n",
       "Standard Schnauzer             790.0   \n",
       "Irish Wolfhound                722.0   \n",
       "Brabancon Griffon              515.0   \n",
       "Japanese Spaniel               391.0   \n",
       "Groenendael                    122.0   \n",
       "Toy Terrier                    42.0    \n",
       "Black-And-Tan Coonhound        32.0    \n",
       "Scotch Terrier                 0.0     \n",
       "Entlebucher                    0.0     \n",
       "Appenzeller                    0.0     \n",
       "Curly-Coated Retriever         0.0     \n",
       "Bouvier Des Flandres           0.0     \n",
       "Name: retweet_count, Length: 113, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get dog breed with the sum of their retweets\n",
    "retweets_data = merged_df.groupby('predicted_breed').sum()['retweet_count'].sort_values(ascending = False)\n",
    "retweets_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be observed that `Golden Retrievers` get more retweets.\n",
    "Below is a plot for breed vs number of retweets for top 5 values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predicted_breed\n",
       "Golden Retriever      358957.0\n",
       "Pembroke              185728.0\n",
       "Labrador Retriever    178721.0\n",
       "Chihuahua             147133.0\n",
       "French Bulldog        83052.0 \n",
       "Name: retweet_count, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f239baf2080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "locs = retweets_data.index[:5].tolist()\n",
    "heights = retweets_data.values[:5].astype('int').tolist()\n",
    "plt.figure(figsize = (8,6))\n",
    "plt.barh(locs, heights)\n",
    "plt.ylabel('Breed')\n",
    "plt.xlabel('Retweets')\n",
    "plt.title('Breed vs Retweets');\n",
    "display(retweets_data[:5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight 2 : Number of Retweets and Favorites from Nov 2015 to July 2017\n",
    "\n",
    "The plot in the next cell provides a visual depiction of month wise `favorite_count` and `retweet_count` from `November 2015` to `July 2017`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year-month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11</th>\n",
       "      <td>94361.0</td>\n",
       "      <td>225730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12</th>\n",
       "      <td>366810.0</td>\n",
       "      <td>887657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01</th>\n",
       "      <td>163051.0</td>\n",
       "      <td>445641.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02</th>\n",
       "      <td>118036.0</td>\n",
       "      <td>330439.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03</th>\n",
       "      <td>107458.0</td>\n",
       "      <td>314293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04</th>\n",
       "      <td>58319.0</td>\n",
       "      <td>178363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05</th>\n",
       "      <td>86837.0</td>\n",
       "      <td>254356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06</th>\n",
       "      <td>150124.0</td>\n",
       "      <td>411484.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07</th>\n",
       "      <td>187757.0</td>\n",
       "      <td>508208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08</th>\n",
       "      <td>121754.0</td>\n",
       "      <td>357323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09</th>\n",
       "      <td>146513.0</td>\n",
       "      <td>481318.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10</th>\n",
       "      <td>221960.0</td>\n",
       "      <td>657342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11</th>\n",
       "      <td>127810.0</td>\n",
       "      <td>422033.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12</th>\n",
       "      <td>207737.0</td>\n",
       "      <td>631222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01</th>\n",
       "      <td>172138.0</td>\n",
       "      <td>596803.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02</th>\n",
       "      <td>130467.0</td>\n",
       "      <td>549418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03</th>\n",
       "      <td>102495.0</td>\n",
       "      <td>450185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04</th>\n",
       "      <td>145677.0</td>\n",
       "      <td>635181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05</th>\n",
       "      <td>165145.0</td>\n",
       "      <td>661914.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06</th>\n",
       "      <td>164696.0</td>\n",
       "      <td>812913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07</th>\n",
       "      <td>214423.0</td>\n",
       "      <td>1028697.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            retweet_count  favorite_count\n",
       "year-month                               \n",
       "2015-11     94361.0        225730.0      \n",
       "2015-12     366810.0       887657.0      \n",
       "2016-01     163051.0       445641.0      \n",
       "2016-02     118036.0       330439.0      \n",
       "2016-03     107458.0       314293.0      \n",
       "2016-04     58319.0        178363.0      \n",
       "2016-05     86837.0        254356.0      \n",
       "2016-06     150124.0       411484.0      \n",
       "2016-07     187757.0       508208.0      \n",
       "2016-08     121754.0       357323.0      \n",
       "2016-09     146513.0       481318.0      \n",
       "2016-10     221960.0       657342.0      \n",
       "2016-11     127810.0       422033.0      \n",
       "2016-12     207737.0       631222.0      \n",
       "2017-01     172138.0       596803.0      \n",
       "2017-02     130467.0       549418.0      \n",
       "2017-03     102495.0       450185.0      \n",
       "2017-04     145677.0       635181.0      \n",
       "2017-05     165145.0       661914.0      \n",
       "2017-06     164696.0       812913.0      \n",
       "2017-07     214423.0       1028697.0     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f239bac3da0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df = merged_df.copy()\n",
    "new_df['year-month'] = new_df['timestamp'].dt.to_period('M')\n",
    "df = new_df.groupby('year-month').sum()[['retweet_count','favorite_count']]\n",
    "twts = df.retweet_count.tolist()\n",
    "favs = df.favorite_count.tolist()\n",
    "locs = df.index.astype('str').tolist()\n",
    "x = np.arange(1,23,1)\n",
    "plt.figure(figsize = (14, 14))\n",
    "plt.plot(locs[:-1], twts[:-1], marker = 'o', label = 'retweet_count')\n",
    "plt.plot(locs[:-1], favs[:-1], marker = '*', label = 'favorite_count')\n",
    "plt.xticks(fontsize = 14, rotation = 90)\n",
    "plt.yticks(np.arange(50000, 1070000,20000 ),fontsize = 14)\n",
    "plt.legend(fontsize = 14)\n",
    "plt.xlabel('Month', fontsize = 14)\n",
    "plt.title('Retweet and Favorite Count from November 2011 to July 2017', fontsize = 14);\n",
    "display(df.iloc[:-1,:])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>It can be observed that the number of favorites has always been more than the number of retweets for the account, during the mentioned period the account received most number of retweets in `December 2015` and most number of favorites in `July 2017`. `April 2016` was not a good month since the lowest retweets and favorites were from that period.</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight 3 : Amount of tweets of the Account from November 2011 to July 2017\n",
    "\n",
    "The plot in the following depicts month-wise `tweet-counts` made by the account \n",
    "from `November 2011` to `July 2017`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year-month\n",
       "2015-11    300\n",
       "2015-12    388\n",
       "2016-01    194\n",
       "2016-02    125\n",
       "2016-03    136\n",
       "2016-04    60 \n",
       "2016-05    60 \n",
       "2016-06    94 \n",
       "2016-07    97 \n",
       "2016-08    63 \n",
       "2016-09    69 \n",
       "2016-10    73 \n",
       "2016-11    58 \n",
       "2016-12    59 \n",
       "2017-01    71 \n",
       "2017-02    72 \n",
       "2017-03    57 \n",
       "2017-04    49 \n",
       "2017-05    45 \n",
       "2017-06    50 \n",
       "2017-07    53 \n",
       "Freq: M, Name: tweet_id, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f239b80c710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweet_counts = new_df.groupby('year-month').count()['tweet_id'][:-1]\n",
    "display(tweet_counts)\n",
    "plt.figure(figsize = (10,10))\n",
    "plt.plot(tweet_counts.index.astype('str').tolist(), tweet_counts.values.tolist(), marker = 'o', color = 'r')\n",
    "plt.xticks(rotation = 90, fontsize = 14)\n",
    "plt.yticks(np.arange(0,450,50), fontsize = 14)\n",
    "plt.xlabel(\"Month\", fontsize = 14)\n",
    "plt.ylabel(\"Tweets\", fontsize = 14)\n",
    "plt.title(\"Number of Tweets from Nov 2011 to July 2017\", fontsize = 14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>It can be observed that `tweet_count` has decreased drastically since `December 2012`. The maximum number of tweets were made during `December 2012`.<i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight 4 : Ratings received by dog breeds\n",
    "\n",
    "Each tweet has a `predicted_breed` and `rating` associated with it, we will find the median rating received by each do breed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "predicted_breed\n",
       "Afghan Hound                   1.300\n",
       "Bouvier Des Flandres           1.300\n",
       "Saluki                         1.300\n",
       "Gordon Setter                  1.200\n",
       "Chow                           1.200\n",
       "Flat-Coated Retriever          1.200\n",
       "Samoyed                        1.200\n",
       "Great Pyrenees                 1.200\n",
       "Rottweiler                     1.200\n",
       "Golden Retriever               1.200\n",
       "Greater Swiss Mountain Dog     1.200\n",
       "Eskimo Dog                     1.200\n",
       "Silky Terrier                  1.200\n",
       "Irish Water Spaniel            1.200\n",
       "Briard                         1.200\n",
       "Standard Schnauzer             1.200\n",
       "Pembroke                       1.200\n",
       "Tibetan Mastiff                1.200\n",
       "Border Terrier                 1.200\n",
       "Pekinese                       1.200\n",
       "French Bulldog                 1.200\n",
       "Norfolk Terrier                1.200\n",
       "Bernese Mountain Dog           1.200\n",
       "Leonberg                       1.200\n",
       "Kuvasz                         1.163\n",
       "Australian Terrier             1.150\n",
       "Giant Schnauzer                1.150\n",
       "Cocker Spaniel                 1.150\n",
       "Irish Setter                   1.150\n",
       "Wire-Haired Fox Terrier        1.150\n",
       "                               ...  \n",
       "Borzoi                         1.000\n",
       "Boston Bull                    1.000\n",
       "Irish Terrier                  1.000\n",
       "Boxer                          1.000\n",
       "Brabancon Griffon              1.000\n",
       "Standard Poodle                1.000\n",
       "Bull Mastiff                   1.000\n",
       "Pug                            1.000\n",
       "Soft-Coated Wheaten Terrier    1.000\n",
       "Ibizan Hound                   1.000\n",
       "Chesapeake Bay Retriever       1.000\n",
       "Groenendael                    1.000\n",
       "Miniature Poodle               1.000\n",
       "Basenji                        1.000\n",
       "Newfoundland                   1.000\n",
       "Norwich Terrier                1.000\n",
       "West Highland White Terrier    1.000\n",
       "Beagle                         1.000\n",
       "German Short-Haired Pointer    1.000\n",
       "Whippet                        1.000\n",
       "Saint Bernard                  1.000\n",
       "Welsh Springer Spaniel         0.950\n",
       "Yorkshire Terrier              0.950\n",
       "Scottish Deerhound             0.950\n",
       "Dalmatian                      0.950\n",
       "English Setter                 0.950\n",
       "Tibetan Terrier                0.900\n",
       "Curly-Coated Retriever         0.900\n",
       "Scotch Terrier                 0.900\n",
       "Japanese Spaniel               0.500\n",
       "Name: rating_num, Length: 113, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get median rating of each breed\n",
    "breed_rating = new_df.groupby('predicted_breed').median()['rating_num'].sort_values(ascending = False)\n",
    "breed_rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*It can be observed that the median rating for `Afghan Hound`, `Saluki`, `Bouvier Des Flandres` is highest = `1.3`.*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "locs = breed_rating.index.tolist()[:10]\n",
    "display(breed_rating[:10])\n",
    "heights = breed_rating.values.tolist()[:10]\n",
    "plt.figure(figsize = (10,10))\n",
    "plt.barh(locs, heights, color = 'g')\n",
    "plt.xticks(np.arange(0,1.6,0.1),fontsize = 14)\n",
    "plt.yticks(fontsize = 14)\n",
    "plt.ylabel('Dog Breeds', fontsize = 14)\n",
    "plt.xlabel('Median Rating', fontsize = 14)\n",
    "plt.title('Dog Breeds vs Median Rating', fontsize = 14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The above plot depicts the `median rating` of top 10 `dog breeds` (based on their median rating) the breeds are\n",
    "`Afghan Hound`, `Bouvier Des Flandres`, `Saluki`, `Gordon Setter`, `Chow`, `Flat-Coated Retriever`, `Samoyed`, `Great Pyrenees`, `Rottweiler` and `Golden Retriever`.*         \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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