wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling Project (We Rate Dogs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import dates\n",
    "import tweepy\n",
    "from tweepy import OAuthHandler\n",
    "import json\n",
    "from timeit import default_timer as timer\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gathering Phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the provided twitter archive data sets into dataframe\n",
    "\n",
    "df_twitter_archive = pd.read_csv('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Downloading the image prediction tsv file and load it into another dataframe,\n",
    "# if an exception occurred during the downloading phase, we will go to use an offline version\n",
    "\n",
    "if not os.path.isfile('image-predictions.tsv'):\n",
    "    try:\n",
    "        url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "        tsv = requests.get(url, allow_redirects=True)\n",
    "        open('image-predictions.tsv', 'wb').write(tsv.content)\n",
    "        df_image_predictions = pd.read_csv('image-predictions.tsv', '\\t', engine='python')\n",
    "    except:\n",
    "        df_image_predictions = pd.read_csv('image-predictions-offline.tsv', '\\t', engine='python')\n",
    "else:\n",
    "    df_image_predictions = pd.read_csv('image-predictions.tsv', '\\t', engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting Tweets metadata using Tweeter Search APIs \n",
    "\n",
    "if not os.path.isfile('tweet_json.txt'):    \n",
    "    consumer_key = 'P9GBS4afNlDrXuF6oy1PbxUab'\n",
    "    consumer_secret = 'CaHoSKeTecyKNhnmbqVHH6vPu8Mqg0plinNmPYuerDktWLhIyM'\n",
    "    access_token = '523273276-eFqsZJX3zc6udeBFN46WYtquvrLqnMow2ouHpoIl'\n",
    "    access_secret = 't1lsf2FmX4zaGFSnv66Bl4SOKP0xBw83F0ly3UVBxBMkn'\n",
    "\n",
    "    auth = OAuthHandler(consumer_key, consumer_secret)\n",
    "    auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "    api = tweepy.API(auth, wait_on_rate_limit=True)\n",
    "\n",
    "    tweet_ids = df_twitter_archive.tweet_id.values\n",
    "    len(tweet_ids)\n",
    "\n",
    "    # Query Twitter's API for JSON data for each tweet ID in the Twitter archive\n",
    "    count = 0\n",
    "    fails_dict = {}\n",
    "    start = timer()\n",
    "    # Save each tweet's returned JSON as a new line in a .txt file\n",
    "    with open('tweet_json.txt', 'w') as outfile:\n",
    "        # This loop will likely take 20-30 minutes to run because of Twitter's rate limit\n",
    "        for tweet_id in tweet_ids:\n",
    "            count += 1\n",
    "            print(str(count) + \": \" + str(tweet_id))\n",
    "            try:\n",
    "                tweet = api.get_status(tweet_id, tweet_mode='extended')\n",
    "                print(\"Success\")\n",
    "                json.dump(tweet._json, outfile)\n",
    "                outfile.write('\\n')\n",
    "            except tweepy.TweepError as e:\n",
    "                print(\"Fail\")\n",
    "                fails_dict[tweet_id] = e\n",
    "                pass\n",
    "    end = timer()\n",
    "    print(end - start)\n",
    "    print(fails_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing the retrieved Tweets json API data\n",
    "\n",
    "tweets_list=[]\n",
    "try:\n",
    "    with open('tweet_json.txt','r') as json_file:\n",
    "        for line in json_file:\n",
    "            tweet_txt = json.loads(line)\n",
    "            tweet_dict = {'tweet_id':tweet_txt['id'],'retweet_count':tweet_txt['retweet_count'],'favorite_count':tweet_txt['favorite_count']}\n",
    "            tweets_list.append(tweet_dict)\n",
    "except:\n",
    "    with open('tweet_json_offline.txt','r') as json_file:\n",
    "        for line in json_file:\n",
    "            tweet_txt = json.loads(line)\n",
    "            tweet_dict = {'tweet_id':tweet_txt['id'],'retweet_count':tweet_txt['retweet_count'],'favorite_count':tweet_txt['favorite_count']}\n",
    "            tweets_list.append(tweet_dict)   \n",
    "            \n",
    "df_retweets_counts = pd.DataFrame(tweets_list)\n",
    "\n",
    "# persist the parsed API data into CSV file\n",
    "df_retweets_counts.to_csv('tweets-counts.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessing Phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\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>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</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",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    .dataframe thead th {\n",
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       "<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>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</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>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "2351  666049248165822465                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "2355  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "2351  2015-11-16 00:24:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "2355  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "2351  Here we have a 1949 1st generation vulpix. Enj...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "2351                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "2351  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator  name doggo floofer pupper puppo  \n",
       "2351                  10  None  None    None   None  None  \n",
       "2352                  10     a  None    None   None  None  \n",
       "2353                  10     a  None    None   None  None  \n",
       "2354                  10     a  None    None   None  None  \n",
       "2355                  10  None  None    None   None  None  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2297"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_twitter_archive.expanded_urls.notnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "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",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>30</th>\n",
       "      <td>886267009285017600</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>2.281182e+09</td>\n",
       "      <td>2017-07-15 16:51:35 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@NonWhiteHat @MayhewMayhem omg hello tanner yo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</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>55</th>\n",
       "      <td>881633300179243008</td>\n",
       "      <td>8.816070e+17</td>\n",
       "      <td>4.738443e+07</td>\n",
       "      <td>2017-07-02 21:58:53 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@roushfenway These are good dogs but 17/10 is ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</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>64</th>\n",
       "      <td>879674319642796034</td>\n",
       "      <td>8.795538e+17</td>\n",
       "      <td>3.105441e+09</td>\n",
       "      <td>2017-06-27 12:14:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@RealKentMurphy 14/10 confirmed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</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>113</th>\n",
       "      <td>870726314365509632</td>\n",
       "      <td>8.707262e+17</td>\n",
       "      <td>1.648776e+07</td>\n",
       "      <td>2017-06-02 19:38:25 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@ComplicitOwl @ShopWeRateDogs &amp;gt;10/10 is res...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</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>148</th>\n",
       "      <td>863427515083354112</td>\n",
       "      <td>8.634256e+17</td>\n",
       "      <td>7.759620e+07</td>\n",
       "      <td>2017-05-13 16:15:35 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@Jack_Septic_Eye I'd need a few more pics to p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</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>...</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",
       "      <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>2038</th>\n",
       "      <td>671550332464455680</td>\n",
       "      <td>6.715449e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-12-01 04:44:10 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>After 22 minutes of careful deliberation this ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</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>2149</th>\n",
       "      <td>669684865554620416</td>\n",
       "      <td>6.693544e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-26 01:11:28 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>After countless hours of research and hundreds...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2169</th>\n",
       "      <td>669353438988365824</td>\n",
       "      <td>6.678065e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-25 03:14:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tessa. She is also very pleased after ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/669353438...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Tessa</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189</th>\n",
       "      <td>668967877119254528</td>\n",
       "      <td>6.689207e+17</td>\n",
       "      <td>2.143566e+07</td>\n",
       "      <td>2015-11-24 01:42:25 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>12/10 good shit Bubka\\n@wane15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</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>2298</th>\n",
       "      <td>667070482143944705</td>\n",
       "      <td>6.670655e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-18 20:02:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>After much debate this dog is being upgraded t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "30    886267009285017600           8.862664e+17         2.281182e+09   \n",
       "55    881633300179243008           8.816070e+17         4.738443e+07   \n",
       "64    879674319642796034           8.795538e+17         3.105441e+09   \n",
       "113   870726314365509632           8.707262e+17         1.648776e+07   \n",
       "148   863427515083354112           8.634256e+17         7.759620e+07   \n",
       "...                  ...                    ...                  ...   \n",
       "2038  671550332464455680           6.715449e+17         4.196984e+09   \n",
       "2149  669684865554620416           6.693544e+17         4.196984e+09   \n",
       "2169  669353438988365824           6.678065e+17         4.196984e+09   \n",
       "2189  668967877119254528           6.689207e+17         2.143566e+07   \n",
       "2298  667070482143944705           6.670655e+17         4.196984e+09   \n",
       "\n",
       "                      timestamp  \\\n",
       "30    2017-07-15 16:51:35 +0000   \n",
       "55    2017-07-02 21:58:53 +0000   \n",
       "64    2017-06-27 12:14:36 +0000   \n",
       "113   2017-06-02 19:38:25 +0000   \n",
       "148   2017-05-13 16:15:35 +0000   \n",
       "...                         ...   \n",
       "2038  2015-12-01 04:44:10 +0000   \n",
       "2149  2015-11-26 01:11:28 +0000   \n",
       "2169  2015-11-25 03:14:30 +0000   \n",
       "2189  2015-11-24 01:42:25 +0000   \n",
       "2298  2015-11-18 20:02:51 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "30    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "55    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "64    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "113   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "148   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2038  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2149  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2169  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2189  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2298  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "30    @NonWhiteHat @MayhewMayhem omg hello tanner yo...                  NaN   \n",
       "55    @roushfenway These are good dogs but 17/10 is ...                  NaN   \n",
       "64                      @RealKentMurphy 14/10 confirmed                  NaN   \n",
       "113   @ComplicitOwl @ShopWeRateDogs &gt;10/10 is res...                  NaN   \n",
       "148   @Jack_Septic_Eye I'd need a few more pics to p...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "2038  After 22 minutes of careful deliberation this ...                  NaN   \n",
       "2149  After countless hours of research and hundreds...                  NaN   \n",
       "2169  This is Tessa. She is also very pleased after ...                  NaN   \n",
       "2189                     12/10 good shit Bubka\\n@wane15                  NaN   \n",
       "2298  After much debate this dog is being upgraded t...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "30                         NaN                        NaN   \n",
       "55                         NaN                        NaN   \n",
       "64                         NaN                        NaN   \n",
       "113                        NaN                        NaN   \n",
       "148                        NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2038                       NaN                        NaN   \n",
       "2149                       NaN                        NaN   \n",
       "2169                       NaN                        NaN   \n",
       "2189                       NaN                        NaN   \n",
       "2298                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "30                                                  NaN                12   \n",
       "55                                                  NaN                17   \n",
       "64                                                  NaN                14   \n",
       "113                                                 NaN                10   \n",
       "148                                                 NaN                12   \n",
       "...                                                 ...               ...   \n",
       "2038                                                NaN                 1   \n",
       "2149                                                NaN                11   \n",
       "2169  https://twitter.com/dog_rates/status/669353438...                10   \n",
       "2189                                                NaN                12   \n",
       "2298                                                NaN                10   \n",
       "\n",
       "      rating_denominator   name doggo floofer pupper puppo  \n",
       "30                    10   None  None    None   None  None  \n",
       "55                    10   None  None    None   None  None  \n",
       "64                    10   None  None    None   None  None  \n",
       "113                   10   None  None    None   None  None  \n",
       "148                   10   None  None    None   None  None  \n",
       "...                  ...    ...   ...     ...    ...   ...  \n",
       "2038                  10   None  None    None   None  None  \n",
       "2149                  10   None  None    None   None  None  \n",
       "2169                  10  Tessa  None    None   None  None  \n",
       "2189                  10   None  None    None   None  None  \n",
       "2298                  10   None  None    None   None  None  \n",
       "\n",
       "[78 rows x 17 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.query('in_reply_to_status_id != \"NaN\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
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       "      <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>19</th>\n",
       "      <td>888202515573088257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-21 01:02:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is Canela. She attempted s...</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-07-19 00:47:34 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887473957...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Canela</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>886054160059072513</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-15 02:45:48 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @Athletics: 12/10 #BATP https://t.co/WxwJmv...</td>\n",
       "      <td>8.860537e+17</td>\n",
       "      <td>1.960740e+07</td>\n",
       "      <td>2017-07-15 02:44:07 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886053434...</td>\n",
       "      <td>12</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>36</th>\n",
       "      <td>885311592912609280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-13 01:35:06 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is Lilly. She just paralle...</td>\n",
       "      <td>8.305833e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-02-12 01:04:29 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/830583320...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Lilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>879130579576475649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-26 00:13:58 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is Emmy. She was adopted t...</td>\n",
       "      <td>8.780576e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-06-23 01:10:23 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/878057613...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>Emmy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>878404777348136964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-24 00:09:53 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: Meet Shadow. In an attempt to r...</td>\n",
       "      <td>8.782815e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-06-23 16:00:04 +0000</td>\n",
       "      <td>https://www.gofundme.com/3yd6y1c,https://twitt...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Shadow</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</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",
       "      <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>1023</th>\n",
       "      <td>746521445350707200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-25 01:52:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is Shaggy. He knows exactl...</td>\n",
       "      <td>6.678667e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-21 00:46:50 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667866724...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Shaggy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>743835915802583040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-17 16:01:16 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: Extremely intelligent dog here....</td>\n",
       "      <td>6.671383e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-19 00:32:12 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667138269...</td>\n",
       "      <td>10</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>1242</th>\n",
       "      <td>711998809858043904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-21 19:31:59 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @twitter: @dog_rates Awesome Tweet! 12/10. ...</td>\n",
       "      <td>7.119983e+17</td>\n",
       "      <td>7.832140e+05</td>\n",
       "      <td>2016-03-21 19:29:52 +0000</td>\n",
       "      <td>https://twitter.com/twitter/status/71199827977...</td>\n",
       "      <td>12</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>2259</th>\n",
       "      <td>667550904950915073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-20 03:51:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @dogratingrating: Exceptional talent. Origi...</td>\n",
       "      <td>6.675487e+17</td>\n",
       "      <td>4.296832e+09</td>\n",
       "      <td>2015-11-20 03:43:06 +0000</td>\n",
       "      <td>https://twitter.com/dogratingrating/status/667...</td>\n",
       "      <td>12</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>2260</th>\n",
       "      <td>667550882905632768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-20 03:51:47 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @dogratingrating: Unoriginal idea. Blatant ...</td>\n",
       "      <td>6.675484e+17</td>\n",
       "      <td>4.296832e+09</td>\n",
       "      <td>2015-11-20 03:41:59 +0000</td>\n",
       "      <td>https://twitter.com/dogratingrating/status/667...</td>\n",
       "      <td>5</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "19    888202515573088257                    NaN                  NaN   \n",
       "32    886054160059072513                    NaN                  NaN   \n",
       "36    885311592912609280                    NaN                  NaN   \n",
       "68    879130579576475649                    NaN                  NaN   \n",
       "73    878404777348136964                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "1023  746521445350707200                    NaN                  NaN   \n",
       "1043  743835915802583040                    NaN                  NaN   \n",
       "1242  711998809858043904                    NaN                  NaN   \n",
       "2259  667550904950915073                    NaN                  NaN   \n",
       "2260  667550882905632768                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "19    2017-07-21 01:02:36 +0000   \n",
       "32    2017-07-15 02:45:48 +0000   \n",
       "36    2017-07-13 01:35:06 +0000   \n",
       "68    2017-06-26 00:13:58 +0000   \n",
       "73    2017-06-24 00:09:53 +0000   \n",
       "...                         ...   \n",
       "1023  2016-06-25 01:52:36 +0000   \n",
       "1043  2016-06-17 16:01:16 +0000   \n",
       "1242  2016-03-21 19:31:59 +0000   \n",
       "2259  2015-11-20 03:51:52 +0000   \n",
       "2260  2015-11-20 03:51:47 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "19    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "32    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "36    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "68    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "73    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "1023  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1043  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1242  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2259  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2260  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "19    RT @dog_rates: This is Canela. She attempted s...         8.874740e+17   \n",
       "32    RT @Athletics: 12/10 #BATP https://t.co/WxwJmv...         8.860537e+17   \n",
       "36    RT @dog_rates: This is Lilly. She just paralle...         8.305833e+17   \n",
       "68    RT @dog_rates: This is Emmy. She was adopted t...         8.780576e+17   \n",
       "73    RT @dog_rates: Meet Shadow. In an attempt to r...         8.782815e+17   \n",
       "...                                                 ...                  ...   \n",
       "1023  RT @dog_rates: This is Shaggy. He knows exactl...         6.678667e+17   \n",
       "1043  RT @dog_rates: Extremely intelligent dog here....         6.671383e+17   \n",
       "1242  RT @twitter: @dog_rates Awesome Tweet! 12/10. ...         7.119983e+17   \n",
       "2259  RT @dogratingrating: Exceptional talent. Origi...         6.675487e+17   \n",
       "2260  RT @dogratingrating: Unoriginal idea. Blatant ...         6.675484e+17   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "19                4.196984e+09  2017-07-19 00:47:34 +0000   \n",
       "32                1.960740e+07  2017-07-15 02:44:07 +0000   \n",
       "36                4.196984e+09  2017-02-12 01:04:29 +0000   \n",
       "68                4.196984e+09  2017-06-23 01:10:23 +0000   \n",
       "73                4.196984e+09  2017-06-23 16:00:04 +0000   \n",
       "...                        ...                        ...   \n",
       "1023              4.196984e+09  2015-11-21 00:46:50 +0000   \n",
       "1043              4.196984e+09  2015-11-19 00:32:12 +0000   \n",
       "1242              7.832140e+05  2016-03-21 19:29:52 +0000   \n",
       "2259              4.296832e+09  2015-11-20 03:43:06 +0000   \n",
       "2260              4.296832e+09  2015-11-20 03:41:59 +0000   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "19    https://twitter.com/dog_rates/status/887473957...                13   \n",
       "32    https://twitter.com/dog_rates/status/886053434...                12   \n",
       "36    https://twitter.com/dog_rates/status/830583320...                13   \n",
       "68    https://twitter.com/dog_rates/status/878057613...                14   \n",
       "73    https://www.gofundme.com/3yd6y1c,https://twitt...                13   \n",
       "...                                                 ...               ...   \n",
       "1023  https://twitter.com/dog_rates/status/667866724...                10   \n",
       "1043  https://twitter.com/dog_rates/status/667138269...                10   \n",
       "1242  https://twitter.com/twitter/status/71199827977...                12   \n",
       "2259  https://twitter.com/dogratingrating/status/667...                12   \n",
       "2260  https://twitter.com/dogratingrating/status/667...                 5   \n",
       "\n",
       "      rating_denominator    name doggo floofer pupper puppo  \n",
       "19                    10  Canela  None    None   None  None  \n",
       "32                    10    None  None    None   None  None  \n",
       "36                    10   Lilly  None    None   None  None  \n",
       "68                    10    Emmy  None    None   None  None  \n",
       "73                    10  Shadow  None    None   None  None  \n",
       "...                  ...     ...   ...     ...    ...   ...  \n",
       "1023                  10  Shaggy  None    None   None  None  \n",
       "1043                  10    None  None    None   None  None  \n",
       "1242                  10    None  None    None   None  None  \n",
       "2259                  10    None  None    None   None  None  \n",
       "2260                  10    None  None    None   None  None  \n",
       "\n",
       "[181 rows x 17 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.query('retweeted_status_id != \"NaN\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_twitter_archive.expanded_urls.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1265    http://goo.gl/ArWZfi,https://twitter.com/dog_r...\n",
       "444     http://us.blastingnews.com/news/2017/01/200-do...\n",
       "391     http://us.blastingnews.com/news/2017/01/kentuc...\n",
       "335     http://us.blastingnews.com/news/2017/02/jfk-an...\n",
       "204     http://www.gofundme.com/bluethewhitehusky,http...\n",
       "                              ...                        \n",
       "1940                                                  NaN\n",
       "2038                                                  NaN\n",
       "2149                                                  NaN\n",
       "2189                                                  NaN\n",
       "2298                                                  NaN\n",
       "Name: expanded_urls, Length: 2356, dtype: object"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.expanded_urls.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>     2221\n",
       "<a href=\"http://vine.co\" rel=\"nofollow\">Vine - Make a Scene</a>                          91\n",
       "<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>                       33\n",
       "<a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>      11\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       745\n",
       "a           55\n",
       "Charlie     12\n",
       "Oliver      11\n",
       "Lucy        11\n",
       "          ... \n",
       "Stewie       1\n",
       "Coleman      1\n",
       "Iggy         1\n",
       "Hanz         1\n",
       "all          1\n",
       "Name: name, Length: 957, dtype: int64"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "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>5</th>\n",
       "      <td>891087950875897856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 00:08:17 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a majestic great white breaching ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891087950...</td>\n",
       "      <td>13</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>7</th>\n",
       "      <td>890729181411237888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-28 00:22:40 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>When you watch your owner call another dog a g...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890729181...</td>\n",
       "      <td>13</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>12</th>\n",
       "      <td>889665388333682689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-25 01:55:32 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here's a puppo that seems to be on the fence a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889665388...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>887343217045368832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-18 16:08:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>You may not have known you needed to see this ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887343217...</td>\n",
       "      <td>13</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>25</th>\n",
       "      <td>887101392804085760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-18 00:07:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This... is a Jubilant Antarctic House Bear. We...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887101392...</td>\n",
       "      <td>12</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>...</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",
       "      <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>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</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>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>807 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "5     891087950875897856                    NaN                  NaN   \n",
       "7     890729181411237888                    NaN                  NaN   \n",
       "12    889665388333682689                    NaN                  NaN   \n",
       "24    887343217045368832                    NaN                  NaN   \n",
       "25    887101392804085760                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "2351  666049248165822465                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "2355  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "5     2017-07-29 00:08:17 +0000   \n",
       "7     2017-07-28 00:22:40 +0000   \n",
       "12    2017-07-25 01:55:32 +0000   \n",
       "24    2017-07-18 16:08:03 +0000   \n",
       "25    2017-07-18 00:07:08 +0000   \n",
       "...                         ...   \n",
       "2351  2015-11-16 00:24:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "2355  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "5     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "7     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "12    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "24    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "25    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "5     Here we have a majestic great white breaching ...                  NaN   \n",
       "7     When you watch your owner call another dog a g...                  NaN   \n",
       "12    Here's a puppo that seems to be on the fence a...                  NaN   \n",
       "24    You may not have known you needed to see this ...                  NaN   \n",
       "25    This... is a Jubilant Antarctic House Bear. We...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "2351  Here we have a 1949 1st generation vulpix. Enj...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "5                          NaN                        NaN   \n",
       "7                          NaN                        NaN   \n",
       "12                         NaN                        NaN   \n",
       "24                         NaN                        NaN   \n",
       "25                         NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2351                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "5     https://twitter.com/dog_rates/status/891087950...                13   \n",
       "7     https://twitter.com/dog_rates/status/890729181...                13   \n",
       "12    https://twitter.com/dog_rates/status/889665388...                13   \n",
       "24    https://twitter.com/dog_rates/status/887343217...                13   \n",
       "25    https://twitter.com/dog_rates/status/887101392...                12   \n",
       "...                                                 ...               ...   \n",
       "2351  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator  name doggo floofer pupper  puppo  \n",
       "5                     10  None  None    None   None   None  \n",
       "7                     10  None  None    None   None   None  \n",
       "12                    10  None  None    None   None  puppo  \n",
       "24                    10  None  None    None   None   None  \n",
       "25                    10  None  None    None   None   None  \n",
       "...                  ...   ...   ...     ...    ...    ...  \n",
       "2351                  10  None  None    None   None   None  \n",
       "2352                  10     a  None    None   None   None  \n",
       "2353                  10     a  None    None   None   None  \n",
       "2354                  10     a  None    None   None   None  \n",
       "2355                  10  None  None    None   None   None  \n",
       "\n",
       "[807 rows x 17 columns]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.query('name ==\"a\" | name ==\"an\" | name==\"None\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      558\n",
       "11      464\n",
       "10      461\n",
       "13      351\n",
       "9       158\n",
       "8       102\n",
       "7        55\n",
       "14       54\n",
       "5        37\n",
       "6        32\n",
       "3        19\n",
       "4        17\n",
       "1         9\n",
       "2         9\n",
       "420       2\n",
       "0         2\n",
       "15        2\n",
       "75        2\n",
       "80        1\n",
       "20        1\n",
       "24        1\n",
       "26        1\n",
       "44        1\n",
       "50        1\n",
       "60        1\n",
       "165       1\n",
       "84        1\n",
       "88        1\n",
       "144       1\n",
       "182       1\n",
       "143       1\n",
       "666       1\n",
       "960       1\n",
       "1776      1\n",
       "17        1\n",
       "27        1\n",
       "45        1\n",
       "99        1\n",
       "121       1\n",
       "204       1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.rating_numerator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "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>45</th>\n",
       "      <td>883482846933004288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-08 00:28:19 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Bella. She hopes her smile made you sm...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/883482846...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>Bella</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>848212111729840128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-04-01 16:35:01 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Jerry. He's doing a distinguished tong...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/848212111...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>Jerry</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>835152434251116546</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-02-24 15:40:31 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>When you're so blinded by your systematic plag...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/835152434...</td>\n",
       "      <td>0</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>387</th>\n",
       "      <td>826598799820865537</td>\n",
       "      <td>8.265984e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-02-01 01:11:25 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>I was going to do 007/10, but the joke wasn't ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</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>462</th>\n",
       "      <td>817502432452313088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-06 22:45:43 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: Meet Herschel. He's slightly bi...</td>\n",
       "      <td>6.924173e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-01-27 18:42:06 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/692417313...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Herschel</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</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",
       "      <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>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</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>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "45    883482846933004288                    NaN                  NaN   \n",
       "229   848212111729840128                    NaN                  NaN   \n",
       "315   835152434251116546                    NaN                  NaN   \n",
       "387   826598799820865537           8.265984e+17         4.196984e+09   \n",
       "462   817502432452313088                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "2351  666049248165822465                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "2355  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "45    2017-07-08 00:28:19 +0000   \n",
       "229   2017-04-01 16:35:01 +0000   \n",
       "315   2017-02-24 15:40:31 +0000   \n",
       "387   2017-02-01 01:11:25 +0000   \n",
       "462   2017-01-06 22:45:43 +0000   \n",
       "...                         ...   \n",
       "2351  2015-11-16 00:24:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "2355  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "45    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "229   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "315   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "387   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "462   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "45    This is Bella. She hopes her smile made you sm...                  NaN   \n",
       "229   This is Jerry. He's doing a distinguished tong...                  NaN   \n",
       "315   When you're so blinded by your systematic plag...                  NaN   \n",
       "387   I was going to do 007/10, but the joke wasn't ...                  NaN   \n",
       "462   RT @dog_rates: Meet Herschel. He's slightly bi...         6.924173e+17   \n",
       "...                                                 ...                  ...   \n",
       "2351  Here we have a 1949 1st generation vulpix. Enj...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "45                         NaN                        NaN   \n",
       "229                        NaN                        NaN   \n",
       "315                        NaN                        NaN   \n",
       "387                        NaN                        NaN   \n",
       "462               4.196984e+09  2016-01-27 18:42:06 +0000   \n",
       "...                        ...                        ...   \n",
       "2351                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "45    https://twitter.com/dog_rates/status/883482846...                 5   \n",
       "229   https://twitter.com/dog_rates/status/848212111...                 6   \n",
       "315   https://twitter.com/dog_rates/status/835152434...                 0   \n",
       "387                                                 NaN                 7   \n",
       "462   https://twitter.com/dog_rates/status/692417313...                 7   \n",
       "...                                                 ...               ...   \n",
       "2351  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator      name doggo floofer  pupper puppo  \n",
       "45                    10     Bella  None    None    None  None  \n",
       "229                   10     Jerry  None    None    None  None  \n",
       "315                   10      None  None    None    None  None  \n",
       "387                   10      None  None    None    None  None  \n",
       "462                   10  Herschel  None    None  pupper  None  \n",
       "...                  ...       ...   ...     ...     ...   ...  \n",
       "2351                  10      None  None    None    None  None  \n",
       "2352                  10         a  None    None    None  None  \n",
       "2353                  10         a  None    None    None  None  \n",
       "2354                  10         a  None    None    None  None  \n",
       "2355                  10      None  None    None    None  None  \n",
       "\n",
       "[440 rows x 17 columns]"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.query('rating_numerator < 10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "313       0\n",
       "2335      2\n",
       "516       7\n",
       "1576     10\n",
       "1575     10\n",
       "       ... \n",
       "1635    110\n",
       "1779    120\n",
       "1634    130\n",
       "902     150\n",
       "1120    170\n",
       "Name: rating_denominator, Length: 2356, dtype: int64"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.rating_denominator.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "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>313</th>\n",
       "      <td>835246439529840640</td>\n",
       "      <td>8.352460e+17</td>\n",
       "      <td>2.625958e+07</td>\n",
       "      <td>2017-02-24 21:54:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@jonnysun @Lin_Manuel ok jomny I know you're e...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</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>342</th>\n",
       "      <td>832088576586297345</td>\n",
       "      <td>8.320875e+17</td>\n",
       "      <td>3.058208e+07</td>\n",
       "      <td>2017-02-16 04:45:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@docmisterio account started on 11/15/15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>15</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>433</th>\n",
       "      <td>820690176645140481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-15 17:52:40 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>The floofs have been released I repeat the flo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/820690176...</td>\n",
       "      <td>84</td>\n",
       "      <td>70</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>516</th>\n",
       "      <td>810984652412424192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-19 23:06:23 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspir...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.gofundme.com/sams-smile,https://tw...</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Sam</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>775096608509886464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-11 22:20:06 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: After so many requests, this is...</td>\n",
       "      <td>7.403732e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-06-08 02:41:38 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/740373189...</td>\n",
       "      <td>9</td>\n",
       "      <td>11</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>902</th>\n",
       "      <td>758467244762497024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-28 01:00:57 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Why does this never happen at my front door......</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/758467244...</td>\n",
       "      <td>165</td>\n",
       "      <td>150</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>1068</th>\n",
       "      <td>740373189193256964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-08 02:41:38 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>After so many requests, this is Bretagne. She ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/740373189...</td>\n",
       "      <td>9</td>\n",
       "      <td>11</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>1120</th>\n",
       "      <td>731156023742988288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-05-13 16:15:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Say hello to this unbelievably well behaved sq...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/731156023...</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "      <td>this</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>722974582966214656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-21 02:25:47 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/722974582...</td>\n",
       "      <td>4</td>\n",
       "      <td>20</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>1202</th>\n",
       "      <td>716439118184652801</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-03 01:36:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Bluebert. He just saw that both #Final...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/716439118...</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>Bluebert</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>713900603437621249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-27 01:29:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/713900603...</td>\n",
       "      <td>99</td>\n",
       "      <td>90</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>1254</th>\n",
       "      <td>710658690886586372</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-18 02:46:49 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here's a brigade of puppers. All look very pre...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/710658690...</td>\n",
       "      <td>80</td>\n",
       "      <td>80</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>1274</th>\n",
       "      <td>709198395643068416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14 02:04:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/709198395...</td>\n",
       "      <td>45</td>\n",
       "      <td>50</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>1351</th>\n",
       "      <td>704054845121142784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-02-28 21:25:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/704054845...</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>697463031882764288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-02-10 16:51:59 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/697463031...</td>\n",
       "      <td>44</td>\n",
       "      <td>40</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>1598</th>\n",
       "      <td>686035780142297088</td>\n",
       "      <td>6.860340e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-01-10 04:04:10 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Yes I do realize a rating of 4/20 would've bee...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>20</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>1634</th>\n",
       "      <td>684225744407494656</td>\n",
       "      <td>6.842229e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-01-05 04:11:44 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Two sneaky puppers were not initially seen, mo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/684225744...</td>\n",
       "      <td>143</td>\n",
       "      <td>130</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>1635</th>\n",
       "      <td>684222868335505415</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-05 04:00:18 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Someone help the girl is being mugged. Several...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/684222868...</td>\n",
       "      <td>121</td>\n",
       "      <td>110</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>1662</th>\n",
       "      <td>682962037429899265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-01 16:30:13 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darrel. He just robbed a 7/11 and is i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/682962037...</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>Darrel</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>682808988178739200</td>\n",
       "      <td>6.827884e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-01-01 06:22:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>I'm aware that I could've said 20/16, but here...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>16</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>1779</th>\n",
       "      <td>677716515794329600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-18 05:06:23 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/677716515...</td>\n",
       "      <td>144</td>\n",
       "      <td>120</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>1843</th>\n",
       "      <td>675853064436391936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-13 01:41:41 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have an entire platoon of puppers. Tot...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/675853064...</td>\n",
       "      <td>88</td>\n",
       "      <td>80</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>2335</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 16:11:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666287406...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>an</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",
       "313   835246439529840640           8.352460e+17         2.625958e+07   \n",
       "342   832088576586297345           8.320875e+17         3.058208e+07   \n",
       "433   820690176645140481                    NaN                  NaN   \n",
       "516   810984652412424192                    NaN                  NaN   \n",
       "784   775096608509886464                    NaN                  NaN   \n",
       "902   758467244762497024                    NaN                  NaN   \n",
       "1068  740373189193256964                    NaN                  NaN   \n",
       "1120  731156023742988288                    NaN                  NaN   \n",
       "1165  722974582966214656                    NaN                  NaN   \n",
       "1202  716439118184652801                    NaN                  NaN   \n",
       "1228  713900603437621249                    NaN                  NaN   \n",
       "1254  710658690886586372                    NaN                  NaN   \n",
       "1274  709198395643068416                    NaN                  NaN   \n",
       "1351  704054845121142784                    NaN                  NaN   \n",
       "1433  697463031882764288                    NaN                  NaN   \n",
       "1598  686035780142297088           6.860340e+17         4.196984e+09   \n",
       "1634  684225744407494656           6.842229e+17         4.196984e+09   \n",
       "1635  684222868335505415                    NaN                  NaN   \n",
       "1662  682962037429899265                    NaN                  NaN   \n",
       "1663  682808988178739200           6.827884e+17         4.196984e+09   \n",
       "1779  677716515794329600                    NaN                  NaN   \n",
       "1843  675853064436391936                    NaN                  NaN   \n",
       "2335  666287406224695296                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "313   2017-02-24 21:54:03 +0000   \n",
       "342   2017-02-16 04:45:50 +0000   \n",
       "433   2017-01-15 17:52:40 +0000   \n",
       "516   2016-12-19 23:06:23 +0000   \n",
       "784   2016-09-11 22:20:06 +0000   \n",
       "902   2016-07-28 01:00:57 +0000   \n",
       "1068  2016-06-08 02:41:38 +0000   \n",
       "1120  2016-05-13 16:15:54 +0000   \n",
       "1165  2016-04-21 02:25:47 +0000   \n",
       "1202  2016-04-03 01:36:11 +0000   \n",
       "1228  2016-03-27 01:29:02 +0000   \n",
       "1254  2016-03-18 02:46:49 +0000   \n",
       "1274  2016-03-14 02:04:08 +0000   \n",
       "1351  2016-02-28 21:25:30 +0000   \n",
       "1433  2016-02-10 16:51:59 +0000   \n",
       "1598  2016-01-10 04:04:10 +0000   \n",
       "1634  2016-01-05 04:11:44 +0000   \n",
       "1635  2016-01-05 04:00:18 +0000   \n",
       "1662  2016-01-01 16:30:13 +0000   \n",
       "1663  2016-01-01 06:22:03 +0000   \n",
       "1779  2015-12-18 05:06:23 +0000   \n",
       "1843  2015-12-13 01:41:41 +0000   \n",
       "2335  2015-11-16 16:11:11 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "313   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "342   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "433   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "516   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "784   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "902   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1068  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1120  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1165  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1202  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1228  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1254  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1274  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1433  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1598  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1634  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1635  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1662  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1663  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1779  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1843  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2335  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "313   @jonnysun @Lin_Manuel ok jomny I know you're e...                  NaN   \n",
       "342            @docmisterio account started on 11/15/15                  NaN   \n",
       "433   The floofs have been released I repeat the flo...                  NaN   \n",
       "516   Meet Sam. She smiles 24/7 &amp; secretly aspir...                  NaN   \n",
       "784   RT @dog_rates: After so many requests, this is...         7.403732e+17   \n",
       "902   Why does this never happen at my front door......                  NaN   \n",
       "1068  After so many requests, this is Bretagne. She ...                  NaN   \n",
       "1120  Say hello to this unbelievably well behaved sq...                  NaN   \n",
       "1165  Happy 4/20 from the squad! 13/10 for all https...                  NaN   \n",
       "1202  This is Bluebert. He just saw that both #Final...                  NaN   \n",
       "1228  Happy Saturday here's 9 puppers on a bench. 99...                  NaN   \n",
       "1254  Here's a brigade of puppers. All look very pre...                  NaN   \n",
       "1274  From left to right:\\nCletus, Jerome, Alejandro...                  NaN   \n",
       "1351  Here is a whole flock of puppers.  60/50 I'll ...                  NaN   \n",
       "1433  Happy Wednesday here's a bucket of pups. 44/40...                  NaN   \n",
       "1598  Yes I do realize a rating of 4/20 would've bee...                  NaN   \n",
       "1634  Two sneaky puppers were not initially seen, mo...                  NaN   \n",
       "1635  Someone help the girl is being mugged. Several...                  NaN   \n",
       "1662  This is Darrel. He just robbed a 7/11 and is i...                  NaN   \n",
       "1663  I'm aware that I could've said 20/16, but here...                  NaN   \n",
       "1779  IT'S PUPPERGEDDON. Total of 144/120 ...I think...                  NaN   \n",
       "1843  Here we have an entire platoon of puppers. Tot...                  NaN   \n",
       "2335  This is an Albanian 3 1/2 legged  Episcopalian...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "313                        NaN                        NaN   \n",
       "342                        NaN                        NaN   \n",
       "433                        NaN                        NaN   \n",
       "516                        NaN                        NaN   \n",
       "784               4.196984e+09  2016-06-08 02:41:38 +0000   \n",
       "902                        NaN                        NaN   \n",
       "1068                       NaN                        NaN   \n",
       "1120                       NaN                        NaN   \n",
       "1165                       NaN                        NaN   \n",
       "1202                       NaN                        NaN   \n",
       "1228                       NaN                        NaN   \n",
       "1254                       NaN                        NaN   \n",
       "1274                       NaN                        NaN   \n",
       "1351                       NaN                        NaN   \n",
       "1433                       NaN                        NaN   \n",
       "1598                       NaN                        NaN   \n",
       "1634                       NaN                        NaN   \n",
       "1635                       NaN                        NaN   \n",
       "1662                       NaN                        NaN   \n",
       "1663                       NaN                        NaN   \n",
       "1779                       NaN                        NaN   \n",
       "1843                       NaN                        NaN   \n",
       "2335                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "313                                                 NaN               960   \n",
       "342                                                 NaN                11   \n",
       "433   https://twitter.com/dog_rates/status/820690176...                84   \n",
       "516   https://www.gofundme.com/sams-smile,https://tw...                24   \n",
       "784   https://twitter.com/dog_rates/status/740373189...                 9   \n",
       "902   https://twitter.com/dog_rates/status/758467244...               165   \n",
       "1068  https://twitter.com/dog_rates/status/740373189...                 9   \n",
       "1120  https://twitter.com/dog_rates/status/731156023...               204   \n",
       "1165  https://twitter.com/dog_rates/status/722974582...                 4   \n",
       "1202  https://twitter.com/dog_rates/status/716439118...                50   \n",
       "1228  https://twitter.com/dog_rates/status/713900603...                99   \n",
       "1254  https://twitter.com/dog_rates/status/710658690...                80   \n",
       "1274  https://twitter.com/dog_rates/status/709198395...                45   \n",
       "1351  https://twitter.com/dog_rates/status/704054845...                60   \n",
       "1433  https://twitter.com/dog_rates/status/697463031...                44   \n",
       "1598                                                NaN                 4   \n",
       "1634  https://twitter.com/dog_rates/status/684225744...               143   \n",
       "1635  https://twitter.com/dog_rates/status/684222868...               121   \n",
       "1662  https://twitter.com/dog_rates/status/682962037...                 7   \n",
       "1663                                                NaN                20   \n",
       "1779  https://twitter.com/dog_rates/status/677716515...               144   \n",
       "1843  https://twitter.com/dog_rates/status/675853064...                88   \n",
       "2335  https://twitter.com/dog_rates/status/666287406...                 1   \n",
       "\n",
       "      rating_denominator      name doggo floofer pupper puppo  \n",
       "313                    0      None  None    None   None  None  \n",
       "342                   15      None  None    None   None  None  \n",
       "433                   70      None  None    None   None  None  \n",
       "516                    7       Sam  None    None   None  None  \n",
       "784                   11      None  None    None   None  None  \n",
       "902                  150      None  None    None   None  None  \n",
       "1068                  11      None  None    None   None  None  \n",
       "1120                 170      this  None    None   None  None  \n",
       "1165                  20      None  None    None   None  None  \n",
       "1202                  50  Bluebert  None    None   None  None  \n",
       "1228                  90      None  None    None   None  None  \n",
       "1254                  80      None  None    None   None  None  \n",
       "1274                  50      None  None    None   None  None  \n",
       "1351                  50         a  None    None   None  None  \n",
       "1433                  40      None  None    None   None  None  \n",
       "1598                  20      None  None    None   None  None  \n",
       "1634                 130      None  None    None   None  None  \n",
       "1635                 110      None  None    None   None  None  \n",
       "1662                  11    Darrel  None    None   None  None  \n",
       "1663                  16      None  None    None   None  None  \n",
       "1779                 120      None  None    None   None  None  \n",
       "1843                  80      None  None    None   None  None  \n",
       "2335                   2        an  None    None   None  None  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.query('rating_denominator != 10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None     2259\n",
       "doggo      97\n",
       "Name: doggo, dtype: int64"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.doggo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       2346\n",
       "floofer      10\n",
       "Name: floofer, dtype: int64"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.floofer.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None     2326\n",
       "puppo      30\n",
       "Name: puppo, dtype: int64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.puppo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None      2099\n",
       "pupper     257\n",
       "Name: pupper, dtype: int64"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.pupper.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_twitter_archive.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 0 entries\n",
      "Data columns (total 18 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    0 non-null      int64  \n",
      " 1   in_reply_to_status_id       0 non-null      float64\n",
      " 2   in_reply_to_user_id         0 non-null      float64\n",
      " 3   timestamp                   0 non-null      object \n",
      " 4   source                      0 non-null      object \n",
      " 5   text                        0 non-null      object \n",
      " 6   retweeted_status_id         0 non-null      float64\n",
      " 7   retweeted_status_user_id    0 non-null      float64\n",
      " 8   retweeted_status_timestamp  0 non-null      object \n",
      " 9   expanded_urls               0 non-null      object \n",
      " 10  rating_numerator            0 non-null      int64  \n",
      " 11  rating_denominator          0 non-null      int64  \n",
      " 12  name                        0 non-null      object \n",
      " 13  doggo                       0 non-null      object \n",
      " 14  floofer                     0 non-null      object \n",
      " 15  pupper                      0 non-null      object \n",
      " 16  puppo                       0 non-null      object \n",
      " 17  rating                      0 non-null      object \n",
      "dtypes: float64(4), int64(3), object(11)\n",
      "memory usage: 0.0+ bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "rating_df = df_twitter_archive.copy()\n",
    "\n",
    "rating_df['rating'] = rating_df.rating_numerator.astype(str) + \"/\" + rating_df.rating_denominator.astype(str) \n",
    "rating_df = rating_df[rating_df.apply(lambda x: x.rating not in x.text, axis=1)]\n",
    "print(rating_df.info())\n",
    "# it seems that the rating extraction is correct!!!, but the values have some outlier or exceptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "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>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>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_image_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "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",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_image_predictions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "golden_retriever      150\n",
       "Labrador_retriever    100\n",
       "Pembroke               89\n",
       "Chihuahua              83\n",
       "pug                    57\n",
       "                     ... \n",
       "candle                  1\n",
       "espresso                1\n",
       "bearskin                1\n",
       "convertible             1\n",
       "polecat                 1\n",
       "Name: p1, Length: 378, dtype: int64"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_image_predictions.p1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1561         Afghan_hound\n",
       "1855         Afghan_hound\n",
       "1458         Afghan_hound\n",
       "1804         Afghan_hound\n",
       "446     African_crocodile\n",
       "              ...        \n",
       "253           wood_rabbit\n",
       "1831         wooden_spoon\n",
       "932                  wool\n",
       "246                  wool\n",
       "297                 zebra\n",
       "Name: p1, Length: 2075, dtype: object"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_image_predictions.p1.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</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>6</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>box_turtle</td>\n",
       "      <td>0.933012</td>\n",
       "      <td>False</td>\n",
       "      <td>mud_turtle</td>\n",
       "      <td>0.045885</td>\n",
       "      <td>False</td>\n",
       "      <td>terrapin</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>hen</td>\n",
       "      <td>0.965932</td>\n",
       "      <td>False</td>\n",
       "      <td>cock</td>\n",
       "      <td>0.033919</td>\n",
       "      <td>False</td>\n",
       "      <td>partridge</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>666268910803644416</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>desktop_computer</td>\n",
       "      <td>0.086502</td>\n",
       "      <td>False</td>\n",
       "      <td>desk</td>\n",
       "      <td>0.085547</td>\n",
       "      <td>False</td>\n",
       "      <td>bookcase</td>\n",
       "      <td>0.079480</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>three-toed_sloth</td>\n",
       "      <td>0.914671</td>\n",
       "      <td>False</td>\n",
       "      <td>otter</td>\n",
       "      <td>0.015250</td>\n",
       "      <td>False</td>\n",
       "      <td>great_grey_owl</td>\n",
       "      <td>0.013207</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>666362758909284353</td>\n",
       "      <td>https://pbs.twimg.com/media/CT9lXGsUcAAyUFt.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>guinea_pig</td>\n",
       "      <td>0.996496</td>\n",
       "      <td>False</td>\n",
       "      <td>skunk</td>\n",
       "      <td>0.002402</td>\n",
       "      <td>False</td>\n",
       "      <td>hamster</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>False</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>880935762899988482</td>\n",
       "      <td>https://pbs.twimg.com/media/DDm2Z5aXUAEDS2u.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>street_sign</td>\n",
       "      <td>0.251801</td>\n",
       "      <td>False</td>\n",
       "      <td>umbrella</td>\n",
       "      <td>0.115123</td>\n",
       "      <td>False</td>\n",
       "      <td>traffic_light</td>\n",
       "      <td>0.069534</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>881268444196462592</td>\n",
       "      <td>https://pbs.twimg.com/media/DDrk-f9WAAI-WQv.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>tusker</td>\n",
       "      <td>0.473303</td>\n",
       "      <td>False</td>\n",
       "      <td>Indian_elephant</td>\n",
       "      <td>0.245646</td>\n",
       "      <td>False</td>\n",
       "      <td>ibex</td>\n",
       "      <td>0.055661</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>886680336477933568</td>\n",
       "      <td>https://pbs.twimg.com/media/DE4fEDzWAAAyHMM.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>convertible</td>\n",
       "      <td>0.738995</td>\n",
       "      <td>False</td>\n",
       "      <td>sports_car</td>\n",
       "      <td>0.139952</td>\n",
       "      <td>False</td>\n",
       "      <td>car_wheel</td>\n",
       "      <td>0.044173</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2052</th>\n",
       "      <td>887517139158093824</td>\n",
       "      <td>https://pbs.twimg.com/ext_tw_video_thumb/88751...</td>\n",
       "      <td>1</td>\n",
       "      <td>limousine</td>\n",
       "      <td>0.130432</td>\n",
       "      <td>False</td>\n",
       "      <td>tow_truck</td>\n",
       "      <td>0.029175</td>\n",
       "      <td>False</td>\n",
       "      <td>shopping_cart</td>\n",
       "      <td>0.026321</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>324 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                            jpg_url  \\\n",
       "6     666051853826850816    https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg   \n",
       "17    666104133288665088    https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg   \n",
       "18    666268910803644416    https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg   \n",
       "21    666293911632134144    https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg   \n",
       "25    666362758909284353    https://pbs.twimg.com/media/CT9lXGsUcAAyUFt.jpg   \n",
       "...                  ...                                                ...   \n",
       "2021  880935762899988482    https://pbs.twimg.com/media/DDm2Z5aXUAEDS2u.jpg   \n",
       "2022  881268444196462592    https://pbs.twimg.com/media/DDrk-f9WAAI-WQv.jpg   \n",
       "2046  886680336477933568    https://pbs.twimg.com/media/DE4fEDzWAAAyHMM.jpg   \n",
       "2052  887517139158093824  https://pbs.twimg.com/ext_tw_video_thumb/88751...   \n",
       "2074  892420643555336193    https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num                p1   p1_conf  p1_dog               p2   p2_conf  \\\n",
       "6           1        box_turtle  0.933012   False       mud_turtle  0.045885   \n",
       "17          1               hen  0.965932   False             cock  0.033919   \n",
       "18          1  desktop_computer  0.086502   False             desk  0.085547   \n",
       "21          1  three-toed_sloth  0.914671   False            otter  0.015250   \n",
       "25          1        guinea_pig  0.996496   False            skunk  0.002402   \n",
       "...       ...               ...       ...     ...              ...       ...   \n",
       "2021        1       street_sign  0.251801   False         umbrella  0.115123   \n",
       "2022        1            tusker  0.473303   False  Indian_elephant  0.245646   \n",
       "2046        1       convertible  0.738995   False       sports_car  0.139952   \n",
       "2052        1         limousine  0.130432   False        tow_truck  0.029175   \n",
       "2074        1            orange  0.097049   False            bagel  0.085851   \n",
       "\n",
       "      p2_dog              p3   p3_conf  p3_dog  \n",
       "6      False        terrapin  0.017885   False  \n",
       "17     False       partridge  0.000052   False  \n",
       "18     False        bookcase  0.079480   False  \n",
       "21     False  great_grey_owl  0.013207   False  \n",
       "25     False         hamster  0.000461   False  \n",
       "...      ...             ...       ...     ...  \n",
       "2021   False   traffic_light  0.069534   False  \n",
       "2022   False            ibex  0.055661   False  \n",
       "2046   False       car_wheel  0.044173   False  \n",
       "2052   False   shopping_cart  0.026321   False  \n",
       "2074   False          banana  0.076110   False  \n",
       "\n",
       "[324 rows x 12 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_image_predictions.query('p1_dog == False & p2_dog == False & p3_dog == False')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.384514e+17</td>\n",
       "      <td>1.203855</td>\n",
       "      <td>0.594548</td>\n",
       "      <td>1.345886e-01</td>\n",
       "      <td>6.032417e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.785203e+16</td>\n",
       "      <td>0.561875</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>1.006657e-01</td>\n",
       "      <td>5.090593e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.764835e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364412</td>\n",
       "      <td>5.388625e-02</td>\n",
       "      <td>1.622240e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.119988e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588230</td>\n",
       "      <td>1.181810e-01</td>\n",
       "      <td>4.944380e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.932034e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843855</td>\n",
       "      <td>1.955655e-01</td>\n",
       "      <td>9.180755e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id      img_num      p1_conf       p2_conf       p3_conf\n",
       "count  2.075000e+03  2075.000000  2075.000000  2.075000e+03  2.075000e+03\n",
       "mean   7.384514e+17     1.203855     0.594548  1.345886e-01  6.032417e-02\n",
       "std    6.785203e+16     0.561875     0.271174  1.006657e-01  5.090593e-02\n",
       "min    6.660209e+17     1.000000     0.044333  1.011300e-08  1.740170e-10\n",
       "25%    6.764835e+17     1.000000     0.364412  5.388625e-02  1.622240e-02\n",
       "50%    7.119988e+17     1.000000     0.588230  1.181810e-01  4.944380e-02\n",
       "75%    7.932034e+17     1.000000     0.843855  1.955655e-01  9.180755e-02\n",
       "max    8.924206e+17     4.000000     1.000000  4.880140e-01  2.734190e-01"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_image_predictions.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, jpg_url, img_num, p1, p1_conf, p1_dog, p2, p2_conf, p2_dog, p3, p3_conf, p3_dog]\n",
       "Index: []"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_image_predictions.query('p2_conf > p1_conf | p3_conf > p1_conf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>7651</td>\n",
       "      <td>35962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>5649</td>\n",
       "      <td>31035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>3747</td>\n",
       "      <td>23356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>7815</td>\n",
       "      <td>39263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>8410</td>\n",
       "      <td>37503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2321</th>\n",
       "      <td>666058600524156928</td>\n",
       "      <td>53</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2322</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>124</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>219</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>771</td>\n",
       "      <td>1119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>51</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2326 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  retweet_count  favorite_count\n",
       "0     892420643555336193           7651           35962\n",
       "1     892177421306343426           5649           31035\n",
       "2     891815181378084864           3747           23356\n",
       "3     891689557279858688           7815           39263\n",
       "4     891327558926688256           8410           37503\n",
       "...                  ...            ...             ...\n",
       "2321  666058600524156928             53             105\n",
       "2322  666057090499244032            124             268\n",
       "2323  666055525042405380            219             406\n",
       "2324  666051853826850816            771            1119\n",
       "2325  666050758794694657             51             124\n",
       "\n",
       "[2326 rows x 3 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_retweets_counts.head(-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2331 entries, 0 to 2330\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   tweet_id        2331 non-null   int64\n",
      " 1   retweet_count   2331 non-null   int64\n",
      " 2   favorite_count  2331 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 54.8 KB\n"
     ]
    }
   ],
   "source": [
    "df_retweets_counts.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issues (Completeness, Validity, Accuracy, Consistency)\n",
    "\n",
    "#### twitter_archive\n",
    "\n",
    "- timestamp column is object not date\n",
    "- day of week and hour of day are important for that data set and need to be extracted\n",
    "- there are rating_numerator have value < 10\n",
    "- there are rating_denominator have value != 10\n",
    "- invalid rows, which has no dog images\n",
    "- invalid rows, which are retweeted and not original tweets\n",
    "- invalid rows, which are replies to orginal tweets\n",
    "- retweeted_status_id is flost not int\n",
    "- there are None values not NaN/False\n",
    "- dogs are named with 'a' or 'an.\n",
    "\n",
    "\n",
    "#### image_preddication\n",
    "\n",
    "- p1,p2,p3 shall be category not object\n",
    "- there is none predicted dog values and shall be removed \n",
    "\n",
    "\n",
    "#### retweet count\n",
    "\n",
    "- nothing, as its gathered in a good quality manner\n",
    "\n",
    "\n",
    "### Tidiness Issues (Structural)\n",
    "\n",
    "- useless columns: source, in_reply_to_status_id, in_reply_to_user_id, retweeted_status_id\tretweeted_status_user_id\t  retweeted_status_timestamp\n",
    "- dog stages not well structured ,shall be one column\n",
    "    - but the it will yeild very small data set as the detected stages are not too much\n",
    "- image prediction p1,p2,p2 we shall keep the highiest one with dog as true"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "##### Missing Data Fixing\n",
    "- nothing to complete, no extra data needed\n",
    "\n",
    "##### Removing invalid Data \n",
    "- remove from twiter archive where retweeted_status_id is not NaN\n",
    "- remove from twiter archive where in_reply_to_status_id is not NaN\n",
    "- remove from twiter archive where expanded_urls is NaN\n",
    "- remove from image prediection where the all dog predication are false\n",
    "-- remove rows that has inconsist rating, numerator less than 10 and denominator not equal to 10\n",
    "- remove columns (source, in_reply_to_status_id, in_reply_to_user_id, retweeted_status_id retweeted_status_user_id retweeted_status_timestamp)\n",
    "\n",
    "##### Tidiness Fixing\n",
    "- twitter archive data set\n",
    "    - melt dog stages (to be one column)\n",
    "- image predeiction data set\n",
    "    - melt P columns to be one P which has the highest conf value and dog type is Ture\n",
    "        - make addtional P column firstly that statify the above criteria, then remove all (P, Conf, is dog)\n",
    "- merge twitter archieve with image prediction and tweets counta\n",
    "\n",
    "##### Remaining Qulity Issues\n",
    "- convert timestamp to datetime\n",
    "- add addtional feilds from timestamp(day of week, hour)\n",
    "- convert None values to NaN for all columns\n",
    "- convert a or an as dog name to NaN\n",
    "- convert dog breed to category\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "#take copyies from the orginal data sets\n",
    "\n",
    "df_image_predictions_clean = df_image_predictions.copy()\n",
    "df_twitter_archive_clean = df_twitter_archive.copy()\n",
    "df_retweets_counts_clean = df_retweets_counts.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing invalid rows\n",
    "\n",
    "df_twitter_archive_clean.drop(df_twitter_archive_clean.query('retweeted_status_id != \"NaN\"').index, axis=0, inplace=True)\n",
    "df_twitter_archive_clean.drop(df_twitter_archive_clean.query('in_reply_to_status_id != \"NaN\"').index, axis=0, inplace=True)\n",
    "df_twitter_archive_clean.dropna(subset=['expanded_urls'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "#solving tidiness issues for twitter archievice data set\n",
    "\n",
    "df_twitter_archive_clean = df_twitter_archive_clean.drop(['source','in_reply_to_status_id','in_reply_to_user_id','retweeted_status_id','retweeted_status_user_id','retweeted_status_timestamp'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "#solving tidiness issues for image prediction data set\n",
    "\n",
    "def aggregate_p(row):\n",
    "    if row['p1_dog'] == True:\n",
    "        row['dog_breed'] = row['p1']\n",
    "        row['dog_prob'] = row['p1_conf']\n",
    "    elif row['p2_dog'] == True:\n",
    "        row['dog_breed'] = row['p2']\n",
    "        row['dog_prob'] = row['p2_conf']\n",
    "    elif row['p3_dog'] == True:\n",
    "        row['dog_breed'] = row['p3']\n",
    "        row['dog_prob'] = row['p3_conf']\n",
    "    else:\n",
    "        row['dog_breed'] = np.nan\n",
    "        row['dog_prob'] = np.nan\n",
    "    return row\n",
    "\n",
    "df_image_predictions_clean = df_image_predictions_clean.apply(aggregate_p , axis=1)\n",
    "df_image_predictions_clean = df_image_predictions_clean.drop(['p1','p1_conf','p1_dog','p2','p2_conf','p2_dog','p3','p3_conf','p3_dog'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge twitter archive with tweets count and image predication\n",
    "\n",
    "df_twitter_archive_clean_master = pd.merge(df_twitter_archive_clean, df_retweets_counts_clean, on=['tweet_id'], how='left')\n",
    "df_twitter_archive_clean_master = pd.merge(df_twitter_archive_clean_master, df_image_predictions_clean, on=['tweet_id'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "# solve quality issues\n",
    "\n",
    "df_twitter_archive_clean_master.timestamp = pd.to_datetime(df_twitter_archive_clean_master.timestamp)\n",
    "df_twitter_archive_clean_master['day_of_week'] = df_twitter_archive_clean_master.timestamp.dt.day_name()\n",
    "df_twitter_archive_clean_master['hour_of_day'] = df_twitter_archive_clean_master.timestamp.dt.hour\n",
    "\n",
    "df_twitter_archive_clean_master = df_twitter_archive_clean_master.replace('None', np.nan)\n",
    "df_twitter_archive_clean_master.name.fillna('a', inplace=True)\n",
    "df_twitter_archive_clean_master.name.fillna('an', inplace=True)\n",
    "\n",
    "df_twitter_archive_clean_master = df_twitter_archive_clean_master[df_twitter_archive_clean_master['dog_breed'].notna()]\n",
    "df_twitter_archive_clean_master.dog_breed = df_twitter_archive_clean_master.dog_breed.astype('category')\n",
    "\n",
    "df_twitter_archive_clean_master.to_csv('twitter_archive_master.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take addtional tidiness actions related to the final and merged data sets with consist rating numbers and dog stages\n",
    "# save this as addtional cleaned option\n",
    "df_twitter_archive_clean_more_tidy = pd.melt(df_twitter_archive_clean_master, id_vars=['tweet_id','timestamp','text','expanded_urls','rating_numerator','rating_denominator','name','retweet_count','favorite_count','jpg_url','img_num','dog_breed','dog_prob','day_of_week','hour_of_day'],var_name='dog_stage',value_name='stage_confirmation')\n",
    "df_twitter_archive_clean_more_tidy = df_twitter_archive_clean_more_tidy[df_twitter_archive_clean_more_tidy['stage_confirmation'].notna()]\n",
    "df_twitter_archive_clean_more_tidy.drop(df_twitter_archive_clean_more_tidy.query('rating_denominator != 10').index, axis=0, inplace=True)\n",
    "df_twitter_archive_clean_more_tidy = df_twitter_archive_clean_more_tidy.drop(['stage_confirmation'],axis=1)\n",
    "df_twitter_archive_clean_more_tidy.dog_stage = df_twitter_archive_clean_more_tidy.dog_stage.astype('category')\n",
    "df_twitter_archive_clean_more_tidy.to_csv('twitter_archive_master_more_tidiy.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_twitter_archive_clean_more_tidy.dog_breed.isna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1666 entries, 1 to 2093\n",
      "Data columns (total 19 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            1666 non-null   int64              \n",
      " 1   timestamp           1666 non-null   datetime64[ns, UTC]\n",
      " 2   text                1666 non-null   object             \n",
      " 3   expanded_urls       1666 non-null   object             \n",
      " 4   rating_numerator    1666 non-null   int64              \n",
      " 5   rating_denominator  1666 non-null   int64              \n",
      " 6   name                1666 non-null   object             \n",
      " 7   doggo               63 non-null     object             \n",
      " 8   floofer             8 non-null      object             \n",
      " 9   pupper              173 non-null    object             \n",
      " 10  puppo               22 non-null     object             \n",
      " 11  retweet_count       1659 non-null   float64            \n",
      " 12  favorite_count      1659 non-null   float64            \n",
      " 13  jpg_url             1666 non-null   object             \n",
      " 14  img_num             1666 non-null   float64            \n",
      " 15  dog_breed           1666 non-null   category           \n",
      " 16  dog_prob            1666 non-null   float64            \n",
      " 17  day_of_week         1666 non-null   object             \n",
      " 18  hour_of_day         1666 non-null   int64              \n",
      "dtypes: category(1), datetime64[ns, UTC](1), float64(4), int64(4), object(9)\n",
      "memory usage: 254.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive_clean_master.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 266 entries, 8 to 5715\n",
      "Data columns (total 16 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            266 non-null    int64              \n",
      " 1   timestamp           266 non-null    datetime64[ns, UTC]\n",
      " 2   text                266 non-null    object             \n",
      " 3   expanded_urls       266 non-null    object             \n",
      " 4   rating_numerator    266 non-null    int64              \n",
      " 5   rating_denominator  266 non-null    int64              \n",
      " 6   name                266 non-null    object             \n",
      " 7   retweet_count       265 non-null    float64            \n",
      " 8   favorite_count      265 non-null    float64            \n",
      " 9   jpg_url             266 non-null    object             \n",
      " 10  img_num             266 non-null    float64            \n",
      " 11  dog_breed           266 non-null    category           \n",
      " 12  dog_prob            266 non-null    float64            \n",
      " 13  day_of_week         266 non-null    object             \n",
      " 14  hour_of_day         266 non-null    int64              \n",
      " 15  dog_stage           266 non-null    category           \n",
      "dtypes: category(2), datetime64[ns, UTC](1), float64(4), int64(4), object(5)\n",
      "memory usage: 37.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive_clean_more_tidy.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "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>text</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>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</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                  timestamp  \\\n",
       "0  892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "1  892177421306343426  2017-08-01 00:17:27 +0000   \n",
       "2  891815181378084864  2017-07-31 00:18:03 +0000   \n",
       "3  891689557279858688  2017-07-30 15:58:51 +0000   \n",
       "4  891327558926688256  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                                text  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...   \n",
       "1  This is Tilly. She's just checking pup on you....   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...   \n",
       "3  This is Darla. She commenced a snooze mid meal...   \n",
       "4  This is Franklin. He would like you to stop ca...   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "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>text</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>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</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                  timestamp  \\\n",
       "0  892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "1  892177421306343426  2017-08-01 00:17:27 +0000   \n",
       "2  891815181378084864  2017-07-31 00:18:03 +0000   \n",
       "3  891689557279858688  2017-07-30 15:58:51 +0000   \n",
       "4  891327558926688256  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                                text  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...   \n",
       "1  This is Tilly. She's just checking pup on you....   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...   \n",
       "3  This is Darla. She commenced a snooze mid meal...   \n",
       "4  This is Franklin. He would like you to stop ca...   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1666 entries, 1 to 2093\n",
      "Data columns (total 19 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            1666 non-null   int64              \n",
      " 1   timestamp           1666 non-null   datetime64[ns, UTC]\n",
      " 2   text                1666 non-null   object             \n",
      " 3   expanded_urls       1666 non-null   object             \n",
      " 4   rating_numerator    1666 non-null   int64              \n",
      " 5   rating_denominator  1666 non-null   int64              \n",
      " 6   name                1666 non-null   object             \n",
      " 7   doggo               63 non-null     object             \n",
      " 8   floofer             8 non-null      object             \n",
      " 9   pupper              173 non-null    object             \n",
      " 10  puppo               22 non-null     object             \n",
      " 11  retweet_count       1659 non-null   float64            \n",
      " 12  favorite_count      1659 non-null   float64            \n",
      " 13  jpg_url             1666 non-null   object             \n",
      " 14  img_num             1666 non-null   float64            \n",
      " 15  dog_breed           1666 non-null   category           \n",
      " 16  dog_prob            1666 non-null   float64            \n",
      " 17  day_of_week         1666 non-null   object             \n",
      " 18  hour_of_day         1666 non-null   int64              \n",
      "dtypes: category(1), datetime64[ns, UTC](1), float64(4), int64(4), object(9)\n",
      "memory usage: 254.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive_clean_master.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>130.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2091</th>\n",
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       "      <td>0.596461</td>\n",
       "      <td>23</td>\n",
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       "      <th>2092</th>\n",
       "      <td>666029285002620928</td>\n",
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       "    <tr>\n",
       "      <th>2093</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
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       "      <td>22</td>\n",
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       "  </tbody>\n",
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       "<p>1666 rows × 8 columns</p>\n",
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       "                tweet_id  rating_numerator  rating_denominator  retweet_count  \\\n",
       "1     892177421306343426                13                  10         5649.0   \n",
       "2     891815181378084864                12                  10         3747.0   \n",
       "3     891689557279858688                13                  10         7815.0   \n",
       "4     891327558926688256                12                  10         8410.0   \n",
       "5     891087950875897856                13                  10         2819.0   \n",
       "...                  ...               ...                 ...            ...   \n",
       "2089  666049248165822465                 5                  10           40.0   \n",
       "2090  666044226329800704                 6                  10          130.0   \n",
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       "2092           120.0      1.0  0.506826           23  \n",
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       "\n",
       "[1666 rows x 8 columns]"
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     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_clean_master.select_dtypes(include=[np.float64,np.int64])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2090</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>130.0</td>\n",
       "      <td>270.0</td>\n",
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       "      <th>2091</th>\n",
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      "text/plain": [
       "                tweet_id  rating_numerator  rating_denominator  retweet_count  \\\n",
       "1     892177421306343426                13                  10         5649.0   \n",
       "2     891815181378084864                12                  10         3747.0   \n",
       "3     891689557279858688                13                  10         7815.0   \n",
       "4     891327558926688256                12                  10         8410.0   \n",
       "5     891087950875897856                13                  10         2819.0   \n",
       "...                  ...               ...                 ...            ...   \n",
       "2089  666049248165822465                 5                  10           40.0   \n",
       "2090  666044226329800704                 6                  10          130.0   \n",
       "2091  666033412701032449                 9                  10           41.0   \n",
       "2092  666029285002620928                 7                  10           42.0   \n",
       "2093  666020888022790149                 8                  10          460.0   \n",
       "\n",
       "      favorite_count  img_num  dog_prob  hour_of_day  \n",
       "1            31035.0      1.0  0.323581            0  \n",
       "2            23356.0      1.0  0.716012            0  \n",
       "3            39263.0      1.0  0.168086           15  \n",
       "4            37503.0      2.0  0.555712           16  \n",
       "5            18889.0      1.0  0.425595            0  \n",
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       "2090           270.0      1.0  0.408143            0  \n",
       "2091           112.0      1.0  0.596461           23  \n",
       "2092           120.0      1.0  0.506826           23  \n",
       "2093          2395.0      1.0  0.465074           22  \n",
       "\n",
       "[1666 rows x 8 columns]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_clean_master.select_dtypes(include=[np.float64,np.int64])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b46ae1e588>"
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     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
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    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = df_twitter_archive_clean_master.select_dtypes(include=[np.float64,np.int64]).corr()\n",
    "sns.heatmap(correlation, xticklabels=correlation.columns, yticklabels=correlation.columns, cmap=sns.diverging_palette(220, 10, as_cmap=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1b46a91abc8>"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "sns.lmplot(x='retweet_count', y='favorite_count', data=df_twitter_archive_clean_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b46a697688>"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "sns.lineplot(x='timestamp', y='favorite_count', data=df_twitter_archive_clean_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1b46aa0aa88>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='rating_numerator', y='favorite_count', data=df_twitter_archive_clean_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1b46af8fcc8>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='hour_of_day', y='retweet_count', data=df_twitter_archive_clean_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b46adfd6c8>"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='day_of_week', y='retweet_count', data=df_twitter_archive_clean_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\matef\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1311: RuntimeWarning: invalid value encountered in less\n",
      "  off_low = points < low_gutter\n",
      "D:\\Users\\matef\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:1315: RuntimeWarning: invalid value encountered in greater\n",
      "  off_high = points > high_gutter\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 909.25x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"dog_stage\", y=\"retweet_count\",hue='dog_breed',kind=\"swarm\", height=2, aspect=5 ,data=df_twitter_archive_clean_more_tidy).savefig(\"output.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_twitter_archive_clean_more_tidy.dog_breed.unique()\n",
    "\n",
    "group_df = df_twitter_archive_clean_more_tidy\n",
    "group_df.dog_breed = group_df.dog_breed.astype(str)\n",
    "\n",
    "group_df=group_df.groupby('dog_breed').agg(\n",
    "    breed_count = ('dog_breed', 'count'),\n",
    "    retweet_sum = ('retweet_count', 'sum'),\n",
    "    favorite_sum = ('favorite_count', 'sum'),\n",
    "    retweet_mean = ('retweet_count', 'mean'),\n",
    "    favorite_mean = ('favorite_count', 'mean'),\n",
    "    rating_numer_mean = ('rating_numerator', 'mean')\n",
    "    ,)\n",
    "\n",
    "group_df = group_df.dropna()\n",
    "group_df = group_df.sort_values(by=['breed_count']).tail(25)\n",
    "\n",
    "group_df.plot(figsize=(25,10),kind='bar').figure.savefig('visulization.jpg')\n",
    "\n",
    "#plt.show()\n",
    "\n",
    "#group_df.reset_index(level=0, inplace=True)\n",
    "#group_df.dog_breed = group_df.dog_breed.astype(str)\n",
    "#plt.figure(figsize=(20,5))\n",
    "\n",
    "#group_df = group_df.melt('dog_breed', var_name='summary',  value_name='vals')\n",
    "#g = sns.catplot(x=\"dog_breed\", y=\"vals\", hue='summary',kind='strip', data=group_df)\n",
    "#g.fig.set_figwidth(15.27)\n",
    "#g.fig.set_figheight(5.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}