wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we are analyzing a popular twitter page @dog_rates which is provided as a .csv file from Twitter archive. Tweepy is the API which is used as interface to the twitter API to download JSON data about retweet counts and favorite counts. \n",
    "We also need to download a file from Udacity servers using http request which predicts if the images are of dogs are not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will be gathering, assessing and cleaning the data \n",
    " * To find distribution of the tweets over time.\n",
    " * To find average counts of retweets of the page and favourites\n",
    " * To find average rate dogs receive\n",
    " * To find if dog stages affect ratings.\n",
    " * To find the most popular breed of dog to pet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import json\n",
    "import collections\n",
    "import sqlite3\n",
    "import requests\n",
    "from timeit import default_timer as timer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gathering Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1) Downloading image_predictions.tsv file from Udacity servers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "response = requests.get(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save .tsv to file for future use.\n",
    "with open(\"image_predictions.tsv\", mode = 'wb') as file:\n",
    "    file.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_image = pd.read_csv('image_predictions.tsv', sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2) Reading the data from given 'twitter-archive-enhanced.csv' file into df_tweets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets = pd.read_csv('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Storing the Tweet_ids in a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2356"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tweet_IDs = df_tweets['tweet_id']\n",
    "len(Tweet_IDs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3) Getting Retweet count and favourites count on each Tweet_ID from Twitter using tweepy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     "text": [
      "Rate limit reached. Sleeping for: 501\n"
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     "text": [
      "Rate limit reached. Sleeping for: 492\n"
     ]
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      "666837028449972224: done\n",
      "666835007768551424: done\n",
      "666826780179869698: done\n",
      "666817836334096384: done\n",
      "666804364988780544: done\n",
      "666786068205871104: done\n",
      "666781792255496192: done\n",
      "666776908487630848: done\n",
      "666739327293083650: done\n",
      "666701168228331520: done\n",
      "666691418707132416: done\n",
      "666649482315059201: done\n",
      "666644823164719104: done\n",
      "666454714377183233: done\n",
      "666447344410484738: done\n",
      "666437273139982337: done\n",
      "666435652385423360: done\n",
      "666430724426358785: done\n",
      "666428276349472768: done\n",
      "666421158376562688: done\n",
      "666418789513326592: done\n",
      "666411507551481857: done\n",
      "666407126856765440: done\n",
      "666396247373291520: done\n",
      "666373753744588802: done\n",
      "666362758909284353: done\n",
      "666353288456101888: done\n",
      "666345417576210432: done\n",
      "666337882303524864: done\n",
      "666293911632134144: done\n",
      "666287406224695296: done\n",
      "666273097616637952: done\n",
      "666268910803644416: done\n",
      "666104133288665088: done\n",
      "666102155909144576: done\n",
      "666099513787052032: done\n",
      "666094000022159362: done\n",
      "666082916733198337: done\n",
      "666073100786774016: done\n",
      "666071193221509120: done\n",
      "666063827256086533: done\n",
      "666058600524156928: done\n",
      "666057090499244032: done\n",
      "666055525042405380: done\n",
      "666051853826850816: done\n",
      "666050758794694657: done\n",
      "666049248165822465: done\n",
      "666044226329800704: done\n",
      "666033412701032449: done\n",
      "666029285002620928: done\n",
      "666020888022790149: done\n"
     ]
    }
   ],
   "source": [
    "consumer_key = 'x123'\n",
    "consumer_secret = 'y123'\n",
    "access_token = 'a123'\n",
    "access_secret = 'b123'\n",
    "\n",
    "try:\n",
    "    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "    auth.set_access_token(access_token, access_secret)\n",
    "except tweepy.TweepError as t:\n",
    "    print(t.message)\n",
    "\n",
    "api = tweepy.API(auth, wait_on_rate_limit= True, wait_on_rate_limit_notify= True)\n",
    "tweet_data = []\n",
    "for tweet_id in Tweet_IDs:\n",
    "    try:\n",
    "        temp = api.get_status(tweet_id)._json\n",
    "        tweet_data.append({'tweet_id':temp['id'],\n",
    "                           'favorite_count':temp['favorite_count'],\n",
    "                           'favorited':temp['favorited'],\n",
    "                           'retweet_count':temp['retweet_count'],\n",
    "                           'retweeted':temp['retweeted']})\n",
    "        print(str(tweet_id) + ': done')\n",
    "    except tweepy.TweepError as t:\n",
    "        print('{} : {}'.format(tweet_id, t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Storing tweet's entire set of JSON data in a file called tweet_json.txt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "json.dump(tweet_data, open('tweet_json.txt', 'w', encoding ='utf'), ensure_ascii=False, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### reading this .txt file line by line into a pandas DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2334"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('tweet_json.txt','r') as f:\n",
    "    data = json.load(f)\n",
    "tweet_json_df = pd.DataFrame(data)\n",
    "len(tweet_json_df.tweet_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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>favorite_count</th>\n",
       "      <th>favorited</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>retweeted</th>\n",
       "      <th>tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37393</td>\n",
       "      <td>False</td>\n",
       "      <td>8086</td>\n",
       "      <td>False</td>\n",
       "      <td>892420643555336193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32147</td>\n",
       "      <td>False</td>\n",
       "      <td>6001</td>\n",
       "      <td>False</td>\n",
       "      <td>892177421306343426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24230</td>\n",
       "      <td>False</td>\n",
       "      <td>3970</td>\n",
       "      <td>False</td>\n",
       "      <td>891815181378084864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40694</td>\n",
       "      <td>False</td>\n",
       "      <td>8250</td>\n",
       "      <td>False</td>\n",
       "      <td>891689557279858688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38922</td>\n",
       "      <td>False</td>\n",
       "      <td>8942</td>\n",
       "      <td>False</td>\n",
       "      <td>891327558926688256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   favorite_count  favorited  retweet_count  retweeted            tweet_id\n",
       "0           37393      False           8086      False  892420643555336193\n",
       "1           32147      False           6001      False  892177421306343426\n",
       "2           24230      False           3970      False  891815181378084864\n",
       "3           40694      False           8250      False  891689557279858688\n",
       "4           38922      False           8942      False  891327558926688256"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(tweet_data)\n",
    "tweet_json_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy:\n",
    "json_df = tweet_json_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating copies of original dataframe\n",
    "df_image_clean = df_image.copy()\n",
    "df_tweets_clean = df_tweets.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      "tweet_id    2075 non-null int64\n",
      "jpg_url     2075 non-null object\n",
      "img_num     2075 non-null int64\n",
      "p1          2075 non-null object\n",
      "p1_conf     2075 non-null float64\n",
      "p1_dog      2075 non-null bool\n",
      "p2          2075 non-null object\n",
      "p2_conf     2075 non-null float64\n",
      "p2_dog      2075 non-null bool\n",
      "p3          2075 non-null object\n",
      "p3_conf     2075 non-null float64\n",
      "p3_dog      2075 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 119.6+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      2356 non-null int64\n",
      "in_reply_to_status_id         78 non-null float64\n",
      "in_reply_to_user_id           78 non-null float64\n",
      "timestamp                     2356 non-null object\n",
      "source                        2356 non-null object\n",
      "text                          2356 non-null object\n",
      "retweeted_status_id           181 non-null float64\n",
      "retweeted_status_user_id      181 non-null float64\n",
      "retweeted_status_timestamp    181 non-null object\n",
      "expanded_urls                 2297 non-null object\n",
      "rating_numerator              2356 non-null int64\n",
      "rating_denominator            2356 non-null int64\n",
      "name                          2356 non-null object\n",
      "doggo                         2356 non-null object\n",
      "floofer                       2356 non-null object\n",
      "pupper                        2356 non-null object\n",
      "puppo                         2356 non-null object\n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 220.9+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2334 entries, 0 to 2333\n",
      "Data columns (total 5 columns):\n",
      "favorite_count    2334 non-null int64\n",
      "favorited         2334 non-null bool\n",
      "retweet_count     2334 non-null int64\n",
      "retweeted         2334 non-null bool\n",
      "tweet_id          2334 non-null int64\n",
      "dtypes: bool(2), int64(3)\n",
      "memory usage: 59.3 KB\n"
     ]
    }
   ],
   "source": [
    "df_image_clean.info()\n",
    "df_tweets_clean.info()\n",
    "json_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_image_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>favorited</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>retweeted</th>\n",
       "      <th>tweet_id</th>\n",
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       "      <td>37393</td>\n",
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       "      <td>False</td>\n",
       "      <td>892420643555336193</td>\n",
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       "      <td>892177421306343426</td>\n",
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       "      <th>2</th>\n",
       "      <td>24230</td>\n",
       "      <td>False</td>\n",
       "      <td>3970</td>\n",
       "      <td>False</td>\n",
       "      <td>891815181378084864</td>\n",
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       "      <th>3</th>\n",
       "      <td>40694</td>\n",
       "      <td>False</td>\n",
       "      <td>8250</td>\n",
       "      <td>False</td>\n",
       "      <td>891689557279858688</td>\n",
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       "      <th>4</th>\n",
       "      <td>38922</td>\n",
       "      <td>False</td>\n",
       "      <td>8942</td>\n",
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      "text/plain": [
       "   favorite_count  favorited  retweet_count  retweeted            tweet_id\n",
       "0           37393      False           8086      False  892420643555336193\n",
       "1           32147      False           6001      False  892177421306343426\n",
       "2           24230      False           3970      False  891815181378084864\n",
       "3           40694      False           8250      False  891689557279858688\n",
       "4           38922      False           8942      False  891327558926688256"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Quality Issues in df_tweets_clean:\n",
    "* Remove rows with \"in reply to..\" column values as they are not original tweets\n",
    "* Drop the columns not required like source, expanded_urls, in_reply_to_status_id, retweeted_status_id, etc,. \n",
    "* Data type of timestamp column\n",
    "* Keep data only till August 2017\n",
    "* Handle tweets that dont exist any longer\n",
    "* rating_numerator and rating_denominator data type should be float\n",
    "* The rating_numerator and denominator data have alot of issues, handle them\n",
    "\n",
    "#### Data Quality Issues in df_image_clean :\n",
    "* df_image_clean has unnecesasry columns, drop them\n",
    "* keep only tweet_id, p1, p1_dog and p1_conf.\n",
    "* Rename these columns to give more clear info about the contents of columns\n",
    "\n",
    "#### Data Tidiness Issues in df_tweets_clean:\n",
    "* Dog stages to be merged into single column\n",
    "* Merge two tables on Tweet_id tweet_json_df and df_tweets\n",
    "* There are multiple Dog stages for same tweet_id. Handle that.\n",
    "* Handle tweet urls that contain more than one url --- since this column is not required, can ignore this issue\n",
    "* The \"text\" column has tweet contents and urls, should be separated --- since this column is not required, can ignore this issue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Dropping the columns not required from both df_image_clean and df_image_clean\n",
    "#### Define:\n",
    "    - Dropping 'source','retweeted_status_id','retweeted_status_user_id','retweeted_status_timestamp','expanded_urls' from                 df_tweets_clean\n",
    "    - Dropping 'jpg_url', 'img_num', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog' from df_image_clean "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_image_clean.drop(columns = ['jpg_url', 'img_num', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog'], inplace =True)\n",
    "# json_df.drop(columns = ['created_at'], inplace = True)\n",
    "df_tweets_clean.drop(columns = ['source','retweeted_status_id','retweeted_status_user_id','retweeted_status_timestamp','expanded_urls'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2334 entries, 0 to 2333\n",
      "Data columns (total 5 columns):\n",
      "favorite_count    2334 non-null int64\n",
      "favorited         2334 non-null bool\n",
      "retweet_count     2334 non-null int64\n",
      "retweeted         2334 non-null bool\n",
      "tweet_id          2334 non-null int64\n",
      "dtypes: bool(2), int64(3)\n",
      "memory usage: 59.3 KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 12 columns):\n",
      "tweet_id                 2356 non-null int64\n",
      "in_reply_to_status_id    78 non-null float64\n",
      "in_reply_to_user_id      78 non-null float64\n",
      "timestamp                2356 non-null object\n",
      "text                     2356 non-null object\n",
      "rating_numerator         2356 non-null int64\n",
      "rating_denominator       2356 non-null int64\n",
      "name                     2356 non-null object\n",
      "doggo                    2356 non-null object\n",
      "floofer                  2356 non-null object\n",
      "pupper                   2356 non-null object\n",
      "puppo                    2356 non-null object\n",
      "dtypes: float64(2), int64(3), object(7)\n",
      "memory usage: 156.5+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 4 columns):\n",
      "tweet_id    2075 non-null int64\n",
      "p1          2075 non-null object\n",
      "p1_conf     2075 non-null float64\n",
      "p1_dog      2075 non-null bool\n",
      "dtypes: bool(1), float64(1), int64(1), object(1)\n",
      "memory usage: 42.6+ KB\n"
     ]
    }
   ],
   "source": [
    "json_df.info()\n",
    "df_tweets_clean.info()\n",
    "df_image_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Renaming the columns of df_image_clean to give more clear idea about the contents of columns\n",
    "#### Define:\n",
    "        - Rename 'p1'to 'Dog_Breed', 'p1_conf' to 'Confidence',and  'p1_dog' to'Dog_or_not'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_image_clean.rename(index=str, columns={'p1': 'Dog_Breed', 'p1_conf' :'Confidence', 'p1_dog' :'Dog_or_not'}, inplace =True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2075 entries, 0 to 2074\n",
      "Data columns (total 4 columns):\n",
      "tweet_id      2075 non-null int64\n",
      "Dog_Breed     2075 non-null object\n",
      "Confidence    2075 non-null float64\n",
      "Dog_or_not    2075 non-null bool\n",
      "dtypes: bool(1), float64(1), int64(1), object(1)\n",
      "memory usage: 50.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_image_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Merging  dfs json_df and df_tweets_clean and df_image_clean\n",
    "#### Define:\n",
    "    - Merge the three dataframes into one. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean = df_tweets_clean.merge(json_df, on ='tweet_id',  how = 'outer',  suffixes=('', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean = df_tweets_clean.merge(df_image_clean, on = 'tweet_id', how = 'outer', suffixes=('',''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2356 entries, 0 to 2355\n",
      "Data columns (total 19 columns):\n",
      "tweet_id                 2356 non-null int64\n",
      "in_reply_to_status_id    78 non-null float64\n",
      "in_reply_to_user_id      78 non-null float64\n",
      "timestamp                2356 non-null object\n",
      "text                     2356 non-null object\n",
      "rating_numerator         2356 non-null int64\n",
      "rating_denominator       2356 non-null int64\n",
      "name                     2356 non-null object\n",
      "doggo                    2356 non-null object\n",
      "floofer                  2356 non-null object\n",
      "pupper                   2356 non-null object\n",
      "puppo                    2356 non-null object\n",
      "favorite_count           2334 non-null float64\n",
      "favorited                2334 non-null object\n",
      "retweet_count            2334 non-null float64\n",
      "retweeted                2334 non-null object\n",
      "Dog_Breed                2075 non-null object\n",
      "Confidence               2075 non-null float64\n",
      "Dog_or_not               2075 non-null object\n",
      "dtypes: float64(5), int64(3), object(11)\n",
      "memory usage: 266.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3970.0</td>\n",
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       "      <td>Chihuahua</td>\n",
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       "    </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>This is Darla. She commenced a snooze mid meal...</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",
       "      <td>40694.0</td>\n",
       "      <td>False</td>\n",
       "      <td>8250.0</td>\n",
       "      <td>False</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</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>This is Franklin. He would like you to stop ca...</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",
       "      <td>38922.0</td>\n",
       "      <td>False</td>\n",
       "      <td>8942.0</td>\n",
       "      <td>False</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</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",
       "                                                text  rating_numerator  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                13   \n",
       "1  This is Tilly. She's just checking pup on you....                13   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                12   \n",
       "3  This is Darla. She commenced a snooze mid meal...                13   \n",
       "4  This is Franklin. He would like you to stop ca...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  favorite_count  \\\n",
       "0                  10   Phineas  None    None   None  None         37393.0   \n",
       "1                  10     Tilly  None    None   None  None         32147.0   \n",
       "2                  10    Archie  None    None   None  None         24230.0   \n",
       "3                  10     Darla  None    None   None  None         40694.0   \n",
       "4                  10  Franklin  None    None   None  None         38922.0   \n",
       "\n",
       "  favorited  retweet_count retweeted    Dog_Breed  Confidence Dog_or_not  \n",
       "0     False         8086.0     False       orange    0.097049      False  \n",
       "1     False         6001.0     False    Chihuahua    0.323581       True  \n",
       "2     False         3970.0     False    Chihuahua    0.716012       True  \n",
       "3     False         8250.0     False  paper_towel    0.170278      False  \n",
       "4     False         8942.0     False       basset    0.555712       True  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Removing retweets and dropping columns that are not required:\n",
    "##### Define:\n",
    "- Keep rows where 'in_reply_to_status_id' and 'in_reply_to_user_id' values are null as these are original tweets\n",
    "- Remove rows where 'text' starts with RT @dog_rates.\n",
    "- dropping columns, 'in_reply_to_status_id', 'in_reply_to_user_id', 'source', 'retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp', 'expanded_urls'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean = df_tweets_clean[df_tweets_clean['in_reply_to_status_id'].isnull() & df_tweets_clean['in_reply_to_user_id'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "156"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking for duplicates, ie, Retweets using text:\n",
    "df_tweets_clean['text'].str.contains('RT @dog_rates', regex=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "## so there are still 156 duplicates to to removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean = df_tweets_clean.drop(df_tweets_clean[df_tweets_clean['text'].str.contains('RT @dog_rates', regex=False)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean.drop(columns=['in_reply_to_status_id', 'in_reply_to_user_id'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets_clean['text'].str.contains('RT @dog_rates', regex=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2122 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id              2122 non-null int64\n",
      "timestamp             2122 non-null object\n",
      "text                  2122 non-null object\n",
      "rating_numerator      2122 non-null int64\n",
      "rating_denominator    2122 non-null int64\n",
      "name                  2122 non-null object\n",
      "doggo                 2122 non-null object\n",
      "floofer               2122 non-null object\n",
      "pupper                2122 non-null object\n",
      "puppo                 2122 non-null object\n",
      "favorite_count        2114 non-null float64\n",
      "favorited             2114 non-null object\n",
      "retweet_count         2114 non-null float64\n",
      "retweeted             2114 non-null object\n",
      "Dog_Breed             1986 non-null object\n",
      "Confidence            1986 non-null float64\n",
      "Dog_or_not            1986 non-null object\n",
      "dtypes: float64(3), int64(3), object(11)\n",
      "memory usage: 207.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) converting column ''timestamp'' to datetime type\n",
    "##### Define: \n",
    "Covert from str datetime type using  to_datetime\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_tweets_clean.timestamp[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean.timestamp =pd.to_datetime(df_tweets_clean.timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_tweets_clean.timestamp[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) Datatype of rating_numerator and rating_denominator should be float and Dog_or_not should be boolean\n",
    "#### Define: \n",
    "- convert the columns datatype from int to float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean['rating_numerator'] = df_tweets_clean['rating_numerator'].astype('float')\n",
    "df_tweets_clean['rating_denominator'] = df_tweets_clean['rating_denominator'].astype('float')\n",
    "df_tweets_clean.Dog_or_not = df_tweets_clean.Dog_or_not.astype('bool')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2122 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id              2122 non-null int64\n",
      "timestamp             2122 non-null datetime64[ns, UTC]\n",
      "text                  2122 non-null object\n",
      "rating_numerator      2122 non-null float64\n",
      "rating_denominator    2122 non-null float64\n",
      "name                  2122 non-null object\n",
      "doggo                 2122 non-null object\n",
      "floofer               2122 non-null object\n",
      "pupper                2122 non-null object\n",
      "puppo                 2122 non-null object\n",
      "favorite_count        2114 non-null float64\n",
      "favorited             2114 non-null object\n",
      "retweet_count         2114 non-null float64\n",
      "retweeted             2114 non-null object\n",
      "Dog_Breed             1986 non-null object\n",
      "Confidence            1986 non-null float64\n",
      "Dog_or_not            2122 non-null bool\n",
      "dtypes: bool(1), datetime64[ns, UTC](1), float64(5), int64(1), object(9)\n",
      "memory usage: 273.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) Keep data only till August 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "- Filter out all data after 1st August 2017 as it is incomplete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean = df_tweets_clean[df_tweets_clean.timestamp <= '2017-07-01 00:00:00 +0000']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2068 entries, 58 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id              2068 non-null int64\n",
      "timestamp             2068 non-null datetime64[ns, UTC]\n",
      "text                  2068 non-null object\n",
      "rating_numerator      2068 non-null float64\n",
      "rating_denominator    2068 non-null float64\n",
      "name                  2068 non-null object\n",
      "doggo                 2068 non-null object\n",
      "floofer               2068 non-null object\n",
      "pupper                2068 non-null object\n",
      "puppo                 2068 non-null object\n",
      "favorite_count        2060 non-null float64\n",
      "favorited             2060 non-null object\n",
      "retweet_count         2060 non-null float64\n",
      "retweeted             2060 non-null object\n",
      "Dog_Breed             1935 non-null object\n",
      "Confidence            1935 non-null float64\n",
      "Dog_or_not            2068 non-null bool\n",
      "dtypes: bool(1), datetime64[ns, UTC](1), float64(5), int64(1), object(9)\n",
      "memory usage: 204.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8) Handle tweets that dont exist any longer\n",
    "#### Define:\n",
    "- Only keep data for tweet_ids in df_tweets_clean which also exist in tweet_json_df as these are valid tweet_id only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean = df_tweets_clean[df_tweets_clean['tweet_id'].isin(tweet_json_df['tweet_id'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2060 entries, 58 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id              2060 non-null int64\n",
      "timestamp             2060 non-null datetime64[ns, UTC]\n",
      "text                  2060 non-null object\n",
      "rating_numerator      2060 non-null float64\n",
      "rating_denominator    2060 non-null float64\n",
      "name                  2060 non-null object\n",
      "doggo                 2060 non-null object\n",
      "floofer               2060 non-null object\n",
      "pupper                2060 non-null object\n",
      "puppo                 2060 non-null object\n",
      "favorite_count        2060 non-null float64\n",
      "favorited             2060 non-null object\n",
      "retweet_count         2060 non-null float64\n",
      "retweeted             2060 non-null object\n",
      "Dog_Breed             1928 non-null object\n",
      "Confidence            1928 non-null float64\n",
      "Dog_or_not            2060 non-null bool\n",
      "dtypes: bool(1), datetime64[ns, UTC](1), float64(5), int64(1), object(9)\n",
      "memory usage: 203.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9) There are lot of issues in the rating_numerator and denominator columns, handle them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First Checking for rating discrepancies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "1776.0\n",
      "12.17378640776699\n"
     ]
    }
   ],
   "source": [
    "print(df_tweets_clean.rating_numerator.min())\n",
    "print(df_tweets_clean.rating_numerator.max())\n",
    "print(df_tweets_clean.rating_numerator.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>timestamp</th>\n",
       "      <th>text</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",
       "      <th>favorite_count</th>\n",
       "      <th>favorited</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>retweeted</th>\n",
       "      <th>Dog_Breed</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>Dog_or_not</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>820690176645140481</td>\n",
       "      <td>2017-01-15 17:52:40+00:00</td>\n",
       "      <td>The floofs have been released I repeat the flo...</td>\n",
       "      <td>84.0</td>\n",
       "      <td>70.0</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>12751.0</td>\n",
       "      <td>False</td>\n",
       "      <td>3428.0</td>\n",
       "      <td>False</td>\n",
       "      <td>West_Highland_white_terrier</td>\n",
       "      <td>0.872064</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>810984652412424192</td>\n",
       "      <td>2016-12-19 23:06:23+00:00</td>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspir...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Sam</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>5613.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>False</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.871342</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>786709082849828864</td>\n",
       "      <td>2016-10-13 23:23:56+00:00</td>\n",
       "      <td>This is Logan, the Chow who lived. He solemnly...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Logan</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>19150.0</td>\n",
       "      <td>False</td>\n",
       "      <td>6520.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.467321</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>778027034220126208</td>\n",
       "      <td>2016-09-20 00:24:34+00:00</td>\n",
       "      <td>This is Sophie. She's a Jubilant Bush Pupper. ...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Sophie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "      <td>6855.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>False</td>\n",
       "      <td>clumber</td>\n",
       "      <td>0.946718</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>758467244762497024</td>\n",
       "      <td>2016-07-28 01:00:57+00:00</td>\n",
       "      <td>Why does this never happen at my front door......</td>\n",
       "      <td>165.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4981.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2344.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.436377</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>749981277374128128</td>\n",
       "      <td>2016-07-04 15:00:45+00:00</td>\n",
       "      <td>This is Atticus. He's quite simply America af....</td>\n",
       "      <td>1776.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Atticus</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>5287.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2575.0</td>\n",
       "      <td>False</td>\n",
       "      <td>bow_tie</td>\n",
       "      <td>0.533941</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>731156023742988288</td>\n",
       "      <td>2016-05-13 16:15:54+00:00</td>\n",
       "      <td>Say hello to this unbelievably well behaved sq...</td>\n",
       "      <td>204.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>this</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3930.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>False</td>\n",
       "      <td>lakeside</td>\n",
       "      <td>0.501767</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>716439118184652801</td>\n",
       "      <td>2016-04-03 01:36:11+00:00</td>\n",
       "      <td>This is Bluebert. He just saw that both #Final...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Bluebert</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2431.0</td>\n",
       "      <td>False</td>\n",
       "      <td>218.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.396495</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>713900603437621249</td>\n",
       "      <td>2016-03-27 01:29:02+00:00</td>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2912.0</td>\n",
       "      <td>False</td>\n",
       "      <td>761.0</td>\n",
       "      <td>False</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.371816</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>710658690886586372</td>\n",
       "      <td>2016-03-18 02:46:49+00:00</td>\n",
       "      <td>Here's a brigade of puppers. All look very pre...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2371.0</td>\n",
       "      <td>False</td>\n",
       "      <td>582.0</td>\n",
       "      <td>False</td>\n",
       "      <td>soft-coated_wheaten_terrier</td>\n",
       "      <td>0.948617</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>709198395643068416</td>\n",
       "      <td>2016-03-14 02:04:08+00:00</td>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2487.0</td>\n",
       "      <td>False</td>\n",
       "      <td>666.0</td>\n",
       "      <td>False</td>\n",
       "      <td>borzoi</td>\n",
       "      <td>0.490783</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>704054845121142784</td>\n",
       "      <td>2016-02-28 21:25:30+00:00</td>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll ...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3011.0</td>\n",
       "      <td>False</td>\n",
       "      <td>945.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Great_Pyrenees</td>\n",
       "      <td>0.667939</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>697463031882764288</td>\n",
       "      <td>2016-02-10 16:51:59+00:00</td>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3512.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1439.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.999885</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>684222868335505415</td>\n",
       "      <td>2016-01-05 04:00:18+00:00</td>\n",
       "      <td>Someone help the girl is being mugged. Several...</td>\n",
       "      <td>121.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3944.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>False</td>\n",
       "      <td>soft-coated_wheaten_terrier</td>\n",
       "      <td>0.791182</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>680494726643068929</td>\n",
       "      <td>2015-12-25 21:06:00+00:00</td>\n",
       "      <td>Here we have uncovered an entire battalion of ...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1765.0</td>\n",
       "      <td>False</td>\n",
       "      <td>503.0</td>\n",
       "      <td>False</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.438627</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>677716515794329600</td>\n",
       "      <td>2015-12-18 05:06:23+00:00</td>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think...</td>\n",
       "      <td>144.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3124.0</td>\n",
       "      <td>False</td>\n",
       "      <td>997.0</td>\n",
       "      <td>False</td>\n",
       "      <td>teddy</td>\n",
       "      <td>0.662908</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>675853064436391936</td>\n",
       "      <td>2015-12-13 01:41:41+00:00</td>\n",
       "      <td>Here we have an entire platoon of puppers. Tot...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1330.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.868367</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>670842764863651840</td>\n",
       "      <td>2015-11-29 05:52:33+00:00</td>\n",
       "      <td>After so many requests... here you go.\\n\\nGood...</td>\n",
       "      <td>420.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>24512.0</td>\n",
       "      <td>False</td>\n",
       "      <td>8719.0</td>\n",
       "      <td>False</td>\n",
       "      <td>microphone</td>\n",
       "      <td>0.096063</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>2015-11-16 16:11:11+00:00</td>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>an</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>141.0</td>\n",
       "      <td>False</td>\n",
       "      <td>63.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Maltese_dog</td>\n",
       "      <td>0.857531</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                 timestamp  \\\n",
       "433   820690176645140481 2017-01-15 17:52:40+00:00   \n",
       "516   810984652412424192 2016-12-19 23:06:23+00:00   \n",
       "695   786709082849828864 2016-10-13 23:23:56+00:00   \n",
       "763   778027034220126208 2016-09-20 00:24:34+00:00   \n",
       "902   758467244762497024 2016-07-28 01:00:57+00:00   \n",
       "979   749981277374128128 2016-07-04 15:00:45+00:00   \n",
       "1120  731156023742988288 2016-05-13 16:15:54+00:00   \n",
       "1202  716439118184652801 2016-04-03 01:36:11+00:00   \n",
       "1228  713900603437621249 2016-03-27 01:29:02+00:00   \n",
       "1254  710658690886586372 2016-03-18 02:46:49+00:00   \n",
       "1274  709198395643068416 2016-03-14 02:04:08+00:00   \n",
       "1351  704054845121142784 2016-02-28 21:25:30+00:00   \n",
       "1433  697463031882764288 2016-02-10 16:51:59+00:00   \n",
       "1635  684222868335505415 2016-01-05 04:00:18+00:00   \n",
       "1712  680494726643068929 2015-12-25 21:06:00+00:00   \n",
       "1779  677716515794329600 2015-12-18 05:06:23+00:00   \n",
       "1843  675853064436391936 2015-12-13 01:41:41+00:00   \n",
       "2074  670842764863651840 2015-11-29 05:52:33+00:00   \n",
       "2335  666287406224695296 2015-11-16 16:11:11+00:00   \n",
       "\n",
       "                                                   text  rating_numerator  \\\n",
       "433   The floofs have been released I repeat the flo...              84.0   \n",
       "516   Meet Sam. She smiles 24/7 &amp; secretly aspir...              24.0   \n",
       "695   This is Logan, the Chow who lived. He solemnly...              75.0   \n",
       "763   This is Sophie. She's a Jubilant Bush Pupper. ...              27.0   \n",
       "902   Why does this never happen at my front door......             165.0   \n",
       "979   This is Atticus. He's quite simply America af....            1776.0   \n",
       "1120  Say hello to this unbelievably well behaved sq...             204.0   \n",
       "1202  This is Bluebert. He just saw that both #Final...              50.0   \n",
       "1228  Happy Saturday here's 9 puppers on a bench. 99...              99.0   \n",
       "1254  Here's a brigade of puppers. All look very pre...              80.0   \n",
       "1274  From left to right:\\nCletus, Jerome, Alejandro...              45.0   \n",
       "1351  Here is a whole flock of puppers.  60/50 I'll ...              60.0   \n",
       "1433  Happy Wednesday here's a bucket of pups. 44/40...              44.0   \n",
       "1635  Someone help the girl is being mugged. Several...             121.0   \n",
       "1712  Here we have uncovered an entire battalion of ...              26.0   \n",
       "1779  IT'S PUPPERGEDDON. Total of 144/120 ...I think...             144.0   \n",
       "1843  Here we have an entire platoon of puppers. Tot...              88.0   \n",
       "2074  After so many requests... here you go.\\n\\nGood...             420.0   \n",
       "2335  This is an Albanian 3 1/2 legged  Episcopalian...               1.0   \n",
       "\n",
       "      rating_denominator      name doggo floofer  pupper puppo  \\\n",
       "433                 70.0      None  None    None    None  None   \n",
       "516                  7.0       Sam  None    None    None  None   \n",
       "695                 10.0     Logan  None    None    None  None   \n",
       "763                 10.0    Sophie  None    None  pupper  None   \n",
       "902                150.0      None  None    None    None  None   \n",
       "979                 10.0   Atticus  None    None    None  None   \n",
       "1120               170.0      this  None    None    None  None   \n",
       "1202                50.0  Bluebert  None    None    None  None   \n",
       "1228                90.0      None  None    None    None  None   \n",
       "1254                80.0      None  None    None    None  None   \n",
       "1274                50.0      None  None    None    None  None   \n",
       "1351                50.0         a  None    None    None  None   \n",
       "1433                40.0      None  None    None    None  None   \n",
       "1635               110.0      None  None    None    None  None   \n",
       "1712                10.0      None  None    None    None  None   \n",
       "1779               120.0      None  None    None    None  None   \n",
       "1843                80.0      None  None    None    None  None   \n",
       "2074                10.0      None  None    None    None  None   \n",
       "2335                 2.0        an  None    None    None  None   \n",
       "\n",
       "      favorite_count favorited  retweet_count retweeted  \\\n",
       "433          12751.0     False         3428.0     False   \n",
       "516           5613.0     False         1540.0     False   \n",
       "695          19150.0     False         6520.0     False   \n",
       "763           6855.0     False         1720.0     False   \n",
       "902           4981.0     False         2344.0     False   \n",
       "979           5287.0     False         2575.0     False   \n",
       "1120          3930.0     False         1317.0     False   \n",
       "1202          2431.0     False          218.0     False   \n",
       "1228          2912.0     False          761.0     False   \n",
       "1254          2371.0     False          582.0     False   \n",
       "1274          2487.0     False          666.0     False   \n",
       "1351          3011.0     False          945.0     False   \n",
       "1433          3512.0     False         1439.0     False   \n",
       "1635          3944.0     False         1428.0     False   \n",
       "1712          1765.0     False          503.0     False   \n",
       "1779          3124.0     False          997.0     False   \n",
       "1843          2720.0     False         1330.0     False   \n",
       "2074         24512.0     False         8719.0     False   \n",
       "2335           141.0     False           63.0     False   \n",
       "\n",
       "                        Dog_Breed  Confidence  Dog_or_not  \n",
       "433   West_Highland_white_terrier    0.872064        True  \n",
       "516              golden_retriever    0.871342        True  \n",
       "695                    Pomeranian    0.467321        True  \n",
       "763                       clumber    0.946718        True  \n",
       "902            Labrador_retriever    0.436377        True  \n",
       "979                       bow_tie    0.533941       False  \n",
       "1120                     lakeside    0.501767       False  \n",
       "1202               Siberian_husky    0.396495        True  \n",
       "1228             golden_retriever    0.371816        True  \n",
       "1254  soft-coated_wheaten_terrier    0.948617        True  \n",
       "1274                       borzoi    0.490783        True  \n",
       "1351               Great_Pyrenees    0.667939        True  \n",
       "1433           Labrador_retriever    0.999885        True  \n",
       "1635  soft-coated_wheaten_terrier    0.791182        True  \n",
       "1712                       kuvasz    0.438627        True  \n",
       "1779                        teddy    0.662908       False  \n",
       "1843           Labrador_retriever    0.868367        True  \n",
       "2074                   microphone    0.096063       False  \n",
       "2335                  Maltese_dog    0.857531        True  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets_clean[(df_tweets_clean['rating_denominator'] < 10) | (df_tweets_clean['rating_numerator'] > 20) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations derived:\n",
    "#### Define:\n",
    "* There are rows where denominator is larger than 10, these are for group of dogs, so to avoid confusion we can drop these\n",
    "* First row with ID 810984652412424192 actually has no rating, so lets drop this \n",
    "* ID 786709082849828864, numerator rating is actually 9.75, so lets correct that.\n",
    "* ID 778027034220126208, this rating is actually 11.27 so lets correct this as well.\n",
    "* ID 749981277374128128, Is an outlier, lets drop this to remove any biases\n",
    "* ID 680494726643068929, rating is actually 11.26, lets correct it.\n",
    "* ID 670842764863651840, is not really a dog, so drop it\n",
    "* ID 666287406224695296 the actual rating is  9/10, so lets correct it.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean = df_tweets_clean.drop(df_tweets_clean[(df_tweets_clean.rating_denominator > 10)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean = df_tweets_clean.drop(df_tweets_clean[(df_tweets_clean.tweet_id == 810984652412424192)].index)\n",
    "df_tweets_clean = df_tweets_clean.drop(df_tweets_clean[(df_tweets_clean.tweet_id == 749981277374128128)].index)\n",
    "df_tweets_clean = df_tweets_clean.drop(df_tweets_clean[(df_tweets_clean.tweet_id == 670842764863651840)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\mishra\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "c:\\users\\mishra\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "c:\\users\\mishra\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "c:\\users\\mishra\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "c:\\users\\mishra\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean['rating_numerator'][df_tweets_clean.tweet_id == 786709082849828864] = 9.75\n",
    "df_tweets_clean['rating_numerator'][df_tweets_clean.tweet_id == 778027034220126208] = 11.27\n",
    "df_tweets_clean['rating_numerator'][df_tweets_clean.tweet_id == 680494726643068929] = 11.26\n",
    "df_tweets_clean['rating_numerator'][df_tweets_clean.tweet_id == 666287406224695296] = 9\n",
    "df_tweets_clean['rating_denominator'][df_tweets_clean.tweet_id == 666287406224695296] = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>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",
       "      <th>favorite_count</th>\n",
       "      <th>favorited</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>retweeted</th>\n",
       "      <th>Dog_Breed</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>Dog_or_not</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, timestamp, text, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo, favorite_count, favorited, retweet_count, retweeted, Dog_Breed, Confidence, Dog_or_not]\n",
       "Index: []"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets_clean[(df_tweets_clean['rating_denominator'] < 10) | (df_tweets_clean['rating_numerator'] > 20) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### verifying our table for any duplicate data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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",
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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>timestamp</th>\n",
       "      <th>text</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",
       "      <th>favorite_count</th>\n",
       "      <th>favorited</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>retweeted</th>\n",
       "      <th>Dog_Breed</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>Dog_or_not</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>792913359805018113</td>\n",
       "      <td>2016-10-31 02:17:31+00:00</td>\n",
       "      <td>Here is a perfect example of someone who has t...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>15103.0</td>\n",
       "      <td>False</td>\n",
       "      <td>4275.0</td>\n",
       "      <td>False</td>\n",
       "      <td>web_site</td>\n",
       "      <td>0.226716</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>772581559778025472</td>\n",
       "      <td>2016-09-04 23:46:12+00:00</td>\n",
       "      <td>Guys this is getting so out of hand. We only r...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>6775.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1789.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Newfoundland</td>\n",
       "      <td>0.574345</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>747885874273214464</td>\n",
       "      <td>2016-06-28 20:14:22+00:00</td>\n",
       "      <td>This is a mighty rare blue-tailed hammer sherk...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3044.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>False</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.408450</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>747816857231626240</td>\n",
       "      <td>2016-06-28 15:40:07+00:00</td>\n",
       "      <td>Viewer discretion is advised. This is a terrib...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>5001.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.768923</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>746872823977771008</td>\n",
       "      <td>2016-06-26 01:08:52+00:00</td>\n",
       "      <td>This is a carrot. We only rate dogs. Please on...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>6185.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2241.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.540201</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>743222593470234624</td>\n",
       "      <td>2016-06-15 23:24:09+00:00</td>\n",
       "      <td>This is a very rare Great Alaskan Bush Pupper....</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "      <td>6386.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>False</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.350629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>717537687239008257</td>\n",
       "      <td>2016-04-06 02:21:30+00:00</td>\n",
       "      <td>People please. This is a Deadly Mediterranean ...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>5898.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1904.0</td>\n",
       "      <td>False</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.779356</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>715733265223708672</td>\n",
       "      <td>2016-04-01 02:51:22+00:00</td>\n",
       "      <td>This is a taco. We only rate dogs. Please only...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4785.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1744.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Dandie_Dinmont</td>\n",
       "      <td>0.740229</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>704859558691414016</td>\n",
       "      <td>2016-03-02 02:43:09+00:00</td>\n",
       "      <td>Here is a heartbreaking scene of an incredible...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "      <td>2318.0</td>\n",
       "      <td>False</td>\n",
       "      <td>566.0</td>\n",
       "      <td>False</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.284428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>703079050210877440</td>\n",
       "      <td>2016-02-26 04:48:02+00:00</td>\n",
       "      <td>This is a Butternut Cumberfloof. It's not wind...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>7606.0</td>\n",
       "      <td>False</td>\n",
       "      <td>3211.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.778503</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>702539513671897089</td>\n",
       "      <td>2016-02-24 17:04:07+00:00</td>\n",
       "      <td>This is a Wild Tuscan Poofwiggle. Careful not ...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2955.0</td>\n",
       "      <td>False</td>\n",
       "      <td>993.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.714367</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>700864154249383937</td>\n",
       "      <td>2016-02-20 02:06:50+00:00</td>\n",
       "      <td>\"Pupper is a present to world. Here is a bow f...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "      <td>2665.0</td>\n",
       "      <td>False</td>\n",
       "      <td>639.0</td>\n",
       "      <td>False</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.805857</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>692187005137076224</td>\n",
       "      <td>2016-01-27 03:26:56+00:00</td>\n",
       "      <td>This is a rare Arctic Wubberfloof. Unamused by...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>False</td>\n",
       "      <td>849.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.810592</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>679530280114372609</td>\n",
       "      <td>2015-12-23 05:13:38+00:00</td>\n",
       "      <td>Guys this really needs to stop. We've been ove...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4898.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2163.0</td>\n",
       "      <td>False</td>\n",
       "      <td>dalmatian</td>\n",
       "      <td>0.750256</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1785</th>\n",
       "      <td>677644091929329666</td>\n",
       "      <td>2015-12-18 00:18:36+00:00</td>\n",
       "      <td>This is a dog swinging. I really enjoyed it so...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>False</td>\n",
       "      <td>826.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.626236</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>675706639471788032</td>\n",
       "      <td>2015-12-12 15:59:51+00:00</td>\n",
       "      <td>This is a Sizzlin Menorah spaniel from Brookly...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>647.0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>False</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.990300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>675534494439489536</td>\n",
       "      <td>2015-12-12 04:35:48+00:00</td>\n",
       "      <td>Seriously guys?! Only send in dogs. I only rat...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1837.0</td>\n",
       "      <td>False</td>\n",
       "      <td>428.0</td>\n",
       "      <td>False</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.749368</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>675109292475830276</td>\n",
       "      <td>2015-12-11 00:26:12+00:00</td>\n",
       "      <td>C'mon guys. We've been over this. We only rate...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2815.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>False</td>\n",
       "      <td>dalmatian</td>\n",
       "      <td>0.989519</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>675047298674663426</td>\n",
       "      <td>2015-12-10 20:19:52+00:00</td>\n",
       "      <td>This is a fluffy albino Bacardi Columbia mix. ...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1067.0</td>\n",
       "      <td>False</td>\n",
       "      <td>334.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.978007</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>674082852460433408</td>\n",
       "      <td>2015-12-08 04:27:30+00:00</td>\n",
       "      <td>This is a Sagitariot Baklava mix. Loves her ne...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>756.0</td>\n",
       "      <td>False</td>\n",
       "      <td>173.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.666957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>673715861853720576</td>\n",
       "      <td>2015-12-07 04:09:13+00:00</td>\n",
       "      <td>This is a heavily opinionated dog. Loves walls...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3550.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>False</td>\n",
       "      <td>suit</td>\n",
       "      <td>0.404115</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>673636718965334016</td>\n",
       "      <td>2015-12-06 22:54:44+00:00</td>\n",
       "      <td>This is a Lofted Aphrodisiac Terrier named Kip...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>False</td>\n",
       "      <td>364.0</td>\n",
       "      <td>False</td>\n",
       "      <td>wombat</td>\n",
       "      <td>0.880257</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>672604026190569472</td>\n",
       "      <td>2015-12-04 02:31:10+00:00</td>\n",
       "      <td>This is a baby Rand Paul. Curls for days. 11/1...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1108.0</td>\n",
       "      <td>False</td>\n",
       "      <td>402.0</td>\n",
       "      <td>False</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.820158</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>671743150407421952</td>\n",
       "      <td>2015-12-01 17:30:22+00:00</td>\n",
       "      <td>This is a Tuscaloosa Alcatraz named Jacob (Yac...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>744.0</td>\n",
       "      <td>False</td>\n",
       "      <td>235.0</td>\n",
       "      <td>False</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.419427</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>671147085991960577</td>\n",
       "      <td>2015-11-30 02:01:49+00:00</td>\n",
       "      <td>This is a Helvetica Listerine named Rufus. Thi...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>668.0</td>\n",
       "      <td>False</td>\n",
       "      <td>228.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Yorkshire_terrier</td>\n",
       "      <td>0.467202</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>670427002554466305</td>\n",
       "      <td>2015-11-28 02:20:27+00:00</td>\n",
       "      <td>This is a Deciduous Trimester mix named Spork....</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>521.0</td>\n",
       "      <td>False</td>\n",
       "      <td>164.0</td>\n",
       "      <td>False</td>\n",
       "      <td>seat_belt</td>\n",
       "      <td>0.952258</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>670361874861563904</td>\n",
       "      <td>2015-11-27 22:01:40+00:00</td>\n",
       "      <td>This is a Rich Mahogany Seltzer named Cherokee...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>326.0</td>\n",
       "      <td>False</td>\n",
       "      <td>66.0</td>\n",
       "      <td>False</td>\n",
       "      <td>platypus</td>\n",
       "      <td>0.974075</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>670303360680108032</td>\n",
       "      <td>2015-11-27 18:09:09+00:00</td>\n",
       "      <td>This is a Speckled Cauliflower Yosemite named ...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>427.0</td>\n",
       "      <td>False</td>\n",
       "      <td>134.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.380278</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2146</th>\n",
       "      <td>669923323644657664</td>\n",
       "      <td>2015-11-26 16:59:01+00:00</td>\n",
       "      <td>This is a spotted Lipitor Rumpelstiltskin name...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>232.0</td>\n",
       "      <td>False</td>\n",
       "      <td>57.0</td>\n",
       "      <td>False</td>\n",
       "      <td>car_mirror</td>\n",
       "      <td>0.343063</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2153</th>\n",
       "      <td>669661792646373376</td>\n",
       "      <td>2015-11-25 23:39:47+00:00</td>\n",
       "      <td>This is a brave dog. Excellent free climber. T...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>799.0</td>\n",
       "      <td>False</td>\n",
       "      <td>437.0</td>\n",
       "      <td>False</td>\n",
       "      <td>weasel</td>\n",
       "      <td>0.262802</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>669564461267722241</td>\n",
       "      <td>2015-11-25 17:13:02+00:00</td>\n",
       "      <td>This is a Coriander Baton Rouge named Alfredo....</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>383.0</td>\n",
       "      <td>False</td>\n",
       "      <td>124.0</td>\n",
       "      <td>False</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.623685</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>668955713004314625</td>\n",
       "      <td>2015-11-24 00:54:05+00:00</td>\n",
       "      <td>This is a Slovakian Helter Skelter Feta named ...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>277.0</td>\n",
       "      <td>False</td>\n",
       "      <td>72.0</td>\n",
       "      <td>False</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.367492</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2198</th>\n",
       "      <td>668815180734689280</td>\n",
       "      <td>2015-11-23 15:35:39+00:00</td>\n",
       "      <td>This is a wild Toblerone from Papua New Guinea...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>572.0</td>\n",
       "      <td>False</td>\n",
       "      <td>268.0</td>\n",
       "      <td>False</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.461172</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>668614819948453888</td>\n",
       "      <td>2015-11-23 02:19:29+00:00</td>\n",
       "      <td>Here is a horned dog. Much grace. Can jump ove...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>610.0</td>\n",
       "      <td>False</td>\n",
       "      <td>315.0</td>\n",
       "      <td>False</td>\n",
       "      <td>bustard</td>\n",
       "      <td>0.380772</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>668507509523615744</td>\n",
       "      <td>2015-11-22 19:13:05+00:00</td>\n",
       "      <td>This is a Birmingham Quagmire named Chuk. Love...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>321.0</td>\n",
       "      <td>False</td>\n",
       "      <td>106.0</td>\n",
       "      <td>False</td>\n",
       "      <td>basenji</td>\n",
       "      <td>0.055379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>668466899341221888</td>\n",
       "      <td>2015-11-22 16:31:42+00:00</td>\n",
       "      <td>Here is a mother dog caring for her pups. Snaz...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>877.0</td>\n",
       "      <td>False</td>\n",
       "      <td>518.0</td>\n",
       "      <td>False</td>\n",
       "      <td>shopping_basket</td>\n",
       "      <td>0.398361</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>668171859951755264</td>\n",
       "      <td>2015-11-21 20:59:20+00:00</td>\n",
       "      <td>This is a Trans Siberian Kellogg named Alfonso...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>485.0</td>\n",
       "      <td>False</td>\n",
       "      <td>195.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.664834</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>667861340749471744</td>\n",
       "      <td>2015-11-21 00:25:26+00:00</td>\n",
       "      <td>This is a Shotokon Macadamia mix named Cheryl....</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>237.0</td>\n",
       "      <td>False</td>\n",
       "      <td>77.0</td>\n",
       "      <td>False</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.967275</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>667773195014021121</td>\n",
       "      <td>2015-11-20 18:35:10+00:00</td>\n",
       "      <td>This is a rare Hungarian Pinot named Jessiga. ...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>234.0</td>\n",
       "      <td>False</td>\n",
       "      <td>57.0</td>\n",
       "      <td>False</td>\n",
       "      <td>West_Highland_white_terrier</td>\n",
       "      <td>0.360465</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>667538891197542400</td>\n",
       "      <td>2015-11-20 03:04:08+00:00</td>\n",
       "      <td>This is a southwest Coriander named Klint. Hat...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>202.0</td>\n",
       "      <td>False</td>\n",
       "      <td>65.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Yorkshire_terrier</td>\n",
       "      <td>0.618957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>667470559035432960</td>\n",
       "      <td>2015-11-19 22:32:36+00:00</td>\n",
       "      <td>This is a northern Wahoo named Kohl. He runs t...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>251.0</td>\n",
       "      <td>False</td>\n",
       "      <td>97.0</td>\n",
       "      <td>False</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.304175</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>667177989038297088</td>\n",
       "      <td>2015-11-19 03:10:02+00:00</td>\n",
       "      <td>This is a Dasani Kingfisher from Maine. His na...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>188.0</td>\n",
       "      <td>False</td>\n",
       "      <td>55.0</td>\n",
       "      <td>False</td>\n",
       "      <td>vizsla</td>\n",
       "      <td>0.259249</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>666983947667116034</td>\n",
       "      <td>2015-11-18 14:18:59+00:00</td>\n",
       "      <td>This is a curly Ticonderoga named Pepe. No fee...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2511.0</td>\n",
       "      <td>False</td>\n",
       "      <td>969.0</td>\n",
       "      <td>False</td>\n",
       "      <td>swab</td>\n",
       "      <td>0.589446</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2311</th>\n",
       "      <td>666781792255496192</td>\n",
       "      <td>2015-11-18 00:55:42+00:00</td>\n",
       "      <td>This is a purebred Bacardi named Octaviath. Ca...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>377.0</td>\n",
       "      <td>False</td>\n",
       "      <td>186.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.618316</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>666701168228331520</td>\n",
       "      <td>2015-11-17 19:35:19+00:00</td>\n",
       "      <td>This is a golden Buckminsterfullerene named Jo...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>423.0</td>\n",
       "      <td>False</td>\n",
       "      <td>213.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.887707</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>666407126856765440</td>\n",
       "      <td>2015-11-17 00:06:54+00:00</td>\n",
       "      <td>This is a southern Vesuvius bumblegruff. Can d...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>103.0</td>\n",
       "      <td>False</td>\n",
       "      <td>37.0</td>\n",
       "      <td>False</td>\n",
       "      <td>black-and-tan_coonhound</td>\n",
       "      <td>0.529139</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>2015-11-16 16:37:02+00:00</td>\n",
       "      <td>This is a funny dog. Weird toes. Won't come do...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>483.0</td>\n",
       "      <td>False</td>\n",
       "      <td>339.0</td>\n",
       "      <td>False</td>\n",
       "      <td>three-toed_sloth</td>\n",
       "      <td>0.914671</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>2015-11-16 00:55:59+00:00</td>\n",
       "      <td>My oh my. This is a rare blond Canadian terrie...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>282.0</td>\n",
       "      <td>False</td>\n",
       "      <td>134.0</td>\n",
       "      <td>False</td>\n",
       "      <td>shopping_cart</td>\n",
       "      <td>0.962465</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>2015-11-16 00:49:46+00:00</td>\n",
       "      <td>Here is a Siberian heavily armored polar bear ...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>421.0</td>\n",
       "      <td>False</td>\n",
       "      <td>229.0</td>\n",
       "      <td>False</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.692517</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>2015-11-16 00:30:50+00:00</td>\n",
       "      <td>This is a truly beautiful English Wilson Staff...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>129.0</td>\n",
       "      <td>False</td>\n",
       "      <td>56.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Bernese_mountain_dog</td>\n",
       "      <td>0.651137</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>2015-11-16 00:04:52+00:00</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>286.0</td>\n",
       "      <td>False</td>\n",
       "      <td>135.0</td>\n",
       "      <td>False</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>2015-11-15 23:21:54+00:00</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>121.0</td>\n",
       "      <td>False</td>\n",
       "      <td>43.0</td>\n",
       "      <td>False</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>2015-11-15 23:05:30+00:00</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>125.0</td>\n",
       "      <td>False</td>\n",
       "      <td>46.0</td>\n",
       "      <td>False</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                 timestamp  \\\n",
       "649   792913359805018113 2016-10-31 02:17:31+00:00   \n",
       "801   772581559778025472 2016-09-04 23:46:12+00:00   \n",
       "1002  747885874273214464 2016-06-28 20:14:22+00:00   \n",
       "1004  747816857231626240 2016-06-28 15:40:07+00:00   \n",
       "1017  746872823977771008 2016-06-26 01:08:52+00:00   \n",
       "1049  743222593470234624 2016-06-15 23:24:09+00:00   \n",
       "1193  717537687239008257 2016-04-06 02:21:30+00:00   \n",
       "1207  715733265223708672 2016-04-01 02:51:22+00:00   \n",
       "1340  704859558691414016 2016-03-02 02:43:09+00:00   \n",
       "1361  703079050210877440 2016-02-26 04:48:02+00:00   \n",
       "1368  702539513671897089 2016-02-24 17:04:07+00:00   \n",
       "1382  700864154249383937 2016-02-20 02:06:50+00:00   \n",
       "1499  692187005137076224 2016-01-27 03:26:56+00:00   \n",
       "1737  679530280114372609 2015-12-23 05:13:38+00:00   \n",
       "1785  677644091929329666 2015-12-18 00:18:36+00:00   \n",
       "1853  675706639471788032 2015-12-12 15:59:51+00:00   \n",
       "1854  675534494439489536 2015-12-12 04:35:48+00:00   \n",
       "1877  675109292475830276 2015-12-11 00:26:12+00:00   \n",
       "1878  675047298674663426 2015-12-10 20:19:52+00:00   \n",
       "1923  674082852460433408 2015-12-08 04:27:30+00:00   \n",
       "1941  673715861853720576 2015-12-07 04:09:13+00:00   \n",
       "1955  673636718965334016 2015-12-06 22:54:44+00:00   \n",
       "1994  672604026190569472 2015-12-04 02:31:10+00:00   \n",
       "2034  671743150407421952 2015-12-01 17:30:22+00:00   \n",
       "2066  671147085991960577 2015-11-30 02:01:49+00:00   \n",
       "2116  670427002554466305 2015-11-28 02:20:27+00:00   \n",
       "2125  670361874861563904 2015-11-27 22:01:40+00:00   \n",
       "2128  670303360680108032 2015-11-27 18:09:09+00:00   \n",
       "2146  669923323644657664 2015-11-26 16:59:01+00:00   \n",
       "2153  669661792646373376 2015-11-25 23:39:47+00:00   \n",
       "2161  669564461267722241 2015-11-25 17:13:02+00:00   \n",
       "2191  668955713004314625 2015-11-24 00:54:05+00:00   \n",
       "2198  668815180734689280 2015-11-23 15:35:39+00:00   \n",
       "2211  668614819948453888 2015-11-23 02:19:29+00:00   \n",
       "2218  668507509523615744 2015-11-22 19:13:05+00:00   \n",
       "2222  668466899341221888 2015-11-22 16:31:42+00:00   \n",
       "2235  668171859951755264 2015-11-21 20:59:20+00:00   \n",
       "2249  667861340749471744 2015-11-21 00:25:26+00:00   \n",
       "2255  667773195014021121 2015-11-20 18:35:10+00:00   \n",
       "2264  667538891197542400 2015-11-20 03:04:08+00:00   \n",
       "2273  667470559035432960 2015-11-19 22:32:36+00:00   \n",
       "2287  667177989038297088 2015-11-19 03:10:02+00:00   \n",
       "2304  666983947667116034 2015-11-18 14:18:59+00:00   \n",
       "2311  666781792255496192 2015-11-18 00:55:42+00:00   \n",
       "2314  666701168228331520 2015-11-17 19:35:19+00:00   \n",
       "2327  666407126856765440 2015-11-17 00:06:54+00:00   \n",
       "2334  666293911632134144 2015-11-16 16:37:02+00:00   \n",
       "2347  666057090499244032 2015-11-16 00:55:59+00:00   \n",
       "2348  666055525042405380 2015-11-16 00:49:46+00:00   \n",
       "2350  666050758794694657 2015-11-16 00:30:50+00:00   \n",
       "2352  666044226329800704 2015-11-16 00:04:52+00:00   \n",
       "2353  666033412701032449 2015-11-15 23:21:54+00:00   \n",
       "2354  666029285002620928 2015-11-15 23:05:30+00:00   \n",
       "\n",
       "                                                   text  rating_numerator  \\\n",
       "649   Here is a perfect example of someone who has t...              13.0   \n",
       "801   Guys this is getting so out of hand. We only r...              10.0   \n",
       "1002  This is a mighty rare blue-tailed hammer sherk...               8.0   \n",
       "1004  Viewer discretion is advised. This is a terrib...               4.0   \n",
       "1017  This is a carrot. We only rate dogs. Please on...              11.0   \n",
       "1049  This is a very rare Great Alaskan Bush Pupper....              12.0   \n",
       "1193  People please. This is a Deadly Mediterranean ...              11.0   \n",
       "1207  This is a taco. We only rate dogs. Please only...              10.0   \n",
       "1340  Here is a heartbreaking scene of an incredible...              10.0   \n",
       "1361  This is a Butternut Cumberfloof. It's not wind...              11.0   \n",
       "1368  This is a Wild Tuscan Poofwiggle. Careful not ...              12.0   \n",
       "1382  \"Pupper is a present to world. Here is a bow f...              12.0   \n",
       "1499  This is a rare Arctic Wubberfloof. Unamused by...              12.0   \n",
       "1737  Guys this really needs to stop. We've been ove...               7.0   \n",
       "1785  This is a dog swinging. I really enjoyed it so...              11.0   \n",
       "1853  This is a Sizzlin Menorah spaniel from Brookly...              10.0   \n",
       "1854  Seriously guys?! Only send in dogs. I only rat...              11.0   \n",
       "1877  C'mon guys. We've been over this. We only rate...               9.0   \n",
       "1878  This is a fluffy albino Bacardi Columbia mix. ...              11.0   \n",
       "1923  This is a Sagitariot Baklava mix. Loves her ne...              11.0   \n",
       "1941  This is a heavily opinionated dog. Loves walls...               4.0   \n",
       "1955  This is a Lofted Aphrodisiac Terrier named Kip...              10.0   \n",
       "1994  This is a baby Rand Paul. Curls for days. 11/1...              11.0   \n",
       "2034  This is a Tuscaloosa Alcatraz named Jacob (Yac...              11.0   \n",
       "2066  This is a Helvetica Listerine named Rufus. Thi...               9.0   \n",
       "2116  This is a Deciduous Trimester mix named Spork....               9.0   \n",
       "2125  This is a Rich Mahogany Seltzer named Cherokee...               9.0   \n",
       "2128  This is a Speckled Cauliflower Yosemite named ...               9.0   \n",
       "2146  This is a spotted Lipitor Rumpelstiltskin name...              10.0   \n",
       "2153  This is a brave dog. Excellent free climber. T...               5.0   \n",
       "2161  This is a Coriander Baton Rouge named Alfredo....              10.0   \n",
       "2191  This is a Slovakian Helter Skelter Feta named ...              10.0   \n",
       "2198  This is a wild Toblerone from Papua New Guinea...               7.0   \n",
       "2211  Here is a horned dog. Much grace. Can jump ove...               7.0   \n",
       "2218  This is a Birmingham Quagmire named Chuk. Love...              10.0   \n",
       "2222  Here is a mother dog caring for her pups. Snaz...               4.0   \n",
       "2235  This is a Trans Siberian Kellogg named Alfonso...               7.0   \n",
       "2249  This is a Shotokon Macadamia mix named Cheryl....               9.0   \n",
       "2255  This is a rare Hungarian Pinot named Jessiga. ...               8.0   \n",
       "2264  This is a southwest Coriander named Klint. Hat...               9.0   \n",
       "2273  This is a northern Wahoo named Kohl. He runs t...              11.0   \n",
       "2287  This is a Dasani Kingfisher from Maine. His na...               8.0   \n",
       "2304  This is a curly Ticonderoga named Pepe. No fee...              11.0   \n",
       "2311  This is a purebred Bacardi named Octaviath. Ca...              10.0   \n",
       "2314  This is a golden Buckminsterfullerene named Jo...               8.0   \n",
       "2327  This is a southern Vesuvius bumblegruff. Can d...               7.0   \n",
       "2334  This is a funny dog. Weird toes. Won't come do...               3.0   \n",
       "2347  My oh my. This is a rare blond Canadian terrie...               9.0   \n",
       "2348  Here is a Siberian heavily armored polar bear ...              10.0   \n",
       "2350  This is a truly beautiful English Wilson Staff...              10.0   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...               6.0   \n",
       "2353  Here is a very happy pup. Big fan of well-main...               9.0   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...               7.0   \n",
       "\n",
       "      rating_denominator name doggo floofer  pupper puppo  favorite_count  \\\n",
       "649                 10.0    a  None    None    None  None         15103.0   \n",
       "801                 10.0    a  None    None    None  None          6775.0   \n",
       "1002                10.0    a  None    None    None  None          3044.0   \n",
       "1004                10.0    a  None    None    None  None          5001.0   \n",
       "1017                10.0    a  None    None    None  None          6185.0   \n",
       "1049                10.0    a  None    None  pupper  None          6386.0   \n",
       "1193                10.0    a  None    None    None  None          5898.0   \n",
       "1207                10.0    a  None    None    None  None          4785.0   \n",
       "1340                10.0    a  None    None  pupper  None          2318.0   \n",
       "1361                10.0    a  None    None    None  None          7606.0   \n",
       "1368                10.0    a  None    None    None  None          2955.0   \n",
       "1382                10.0    a  None    None  pupper  None          2665.0   \n",
       "1499                10.0    a  None    None    None  None          2600.0   \n",
       "1737                10.0    a  None    None    None  None          4898.0   \n",
       "1785                10.0    a  None    None    None  None          1890.0   \n",
       "1853                10.0    a  None    None    None  None           647.0   \n",
       "1854                10.0    a  None    None    None  None          1837.0   \n",
       "1877                10.0    a  None    None    None  None          2815.0   \n",
       "1878                10.0    a  None    None    None  None          1067.0   \n",
       "1923                10.0    a  None    None    None  None           756.0   \n",
       "1941                10.0    a  None    None    None  None          3550.0   \n",
       "1955                10.0    a  None    None    None  None          1100.0   \n",
       "1994                10.0    a  None    None    None  None          1108.0   \n",
       "2034                10.0    a  None    None    None  None           744.0   \n",
       "2066                10.0    a  None    None    None  None           668.0   \n",
       "2116                10.0    a  None    None    None  None           521.0   \n",
       "2125                10.0    a  None    None    None  None           326.0   \n",
       "2128                10.0    a  None    None    None  None           427.0   \n",
       "2146                10.0    a  None    None    None  None           232.0   \n",
       "2153                10.0    a  None    None    None  None           799.0   \n",
       "2161                10.0    a  None    None    None  None           383.0   \n",
       "2191                10.0    a  None    None    None  None           277.0   \n",
       "2198                10.0    a  None    None    None  None           572.0   \n",
       "2211                10.0    a  None    None    None  None           610.0   \n",
       "2218                10.0    a  None    None    None  None           321.0   \n",
       "2222                10.0    a  None    None    None  None           877.0   \n",
       "2235                10.0    a  None    None    None  None           485.0   \n",
       "2249                10.0    a  None    None    None  None           237.0   \n",
       "2255                10.0    a  None    None    None  None           234.0   \n",
       "2264                10.0    a  None    None    None  None           202.0   \n",
       "2273                10.0    a  None    None    None  None           251.0   \n",
       "2287                10.0    a  None    None    None  None           188.0   \n",
       "2304                10.0    a  None    None    None  None          2511.0   \n",
       "2311                10.0    a  None    None    None  None           377.0   \n",
       "2314                10.0    a  None    None    None  None           423.0   \n",
       "2327                10.0    a  None    None    None  None           103.0   \n",
       "2334                10.0    a  None    None    None  None           483.0   \n",
       "2347                10.0    a  None    None    None  None           282.0   \n",
       "2348                10.0    a  None    None    None  None           421.0   \n",
       "2350                10.0    a  None    None    None  None           129.0   \n",
       "2352                10.0    a  None    None    None  None           286.0   \n",
       "2353                10.0    a  None    None    None  None           121.0   \n",
       "2354                10.0    a  None    None    None  None           125.0   \n",
       "\n",
       "     favorited  retweet_count retweeted                    Dog_Breed  \\\n",
       "649      False         4275.0     False                     web_site   \n",
       "801      False         1789.0     False                 Newfoundland   \n",
       "1002     False         1027.0     False                       kuvasz   \n",
       "1004     False         1216.0     False                     Pembroke   \n",
       "1017     False         2241.0     False                     Pembroke   \n",
       "1049     False         1980.0     False                       kuvasz   \n",
       "1193     False         1904.0     False             golden_retriever   \n",
       "1207     False         1744.0     False               Dandie_Dinmont   \n",
       "1340     False          566.0     False                          pug   \n",
       "1361     False         3211.0     False                     Pembroke   \n",
       "1368     False          993.0     False                   Pomeranian   \n",
       "1382     False          639.0     False                       kuvasz   \n",
       "1499     False          849.0     False               Siberian_husky   \n",
       "1737     False         2163.0     False                    dalmatian   \n",
       "1785     False          826.0     False                    Chihuahua   \n",
       "1853     False          100.0     False             English_springer   \n",
       "1854     False          428.0     False                         chow   \n",
       "1877     False         1152.0     False                    dalmatian   \n",
       "1878     False          334.0     False                      Samoyed   \n",
       "1923     False          173.0     False                   Pomeranian   \n",
       "1941     False         2097.0     False                         suit   \n",
       "1955     False          364.0     False                       wombat   \n",
       "1994     False          402.0     False                   toy_poodle   \n",
       "2034     False          235.0     False                   toy_poodle   \n",
       "2066     False          228.0     False            Yorkshire_terrier   \n",
       "2116     False          164.0     False                    seat_belt   \n",
       "2125     False           66.0     False                     platypus   \n",
       "2128     False          134.0     False            Shetland_sheepdog   \n",
       "2146     False           57.0     False                   car_mirror   \n",
       "2153     False          437.0     False                       weasel   \n",
       "2161     False          124.0     False                   toy_poodle   \n",
       "2191     False           72.0     False               cocker_spaniel   \n",
       "2198     False          268.0     False                      redbone   \n",
       "2211     False          315.0     False                      bustard   \n",
       "2218     False          106.0     False                      basenji   \n",
       "2222     False          518.0     False              shopping_basket   \n",
       "2235     False          195.0     False                    Chihuahua   \n",
       "2249     False           77.0     False                     malamute   \n",
       "2255     False           57.0     False  West_Highland_white_terrier   \n",
       "2264     False           65.0     False            Yorkshire_terrier   \n",
       "2273     False           97.0     False                   toy_poodle   \n",
       "2287     False           55.0     False                       vizsla   \n",
       "2304     False          969.0     False                         swab   \n",
       "2311     False          186.0     False            Italian_greyhound   \n",
       "2314     False          213.0     False           Labrador_retriever   \n",
       "2327     False           37.0     False      black-and-tan_coonhound   \n",
       "2334     False          339.0     False             three-toed_sloth   \n",
       "2347     False          134.0     False                shopping_cart   \n",
       "2348     False          229.0     False                         chow   \n",
       "2350     False           56.0     False         Bernese_mountain_dog   \n",
       "2352     False          135.0     False          Rhodesian_ridgeback   \n",
       "2353     False           43.0     False              German_shepherd   \n",
       "2354     False           46.0     False                      redbone   \n",
       "\n",
       "      Confidence  Dog_or_not  \n",
       "649     0.226716       False  \n",
       "801     0.574345        True  \n",
       "1002    0.408450        True  \n",
       "1004    0.768923        True  \n",
       "1017    0.540201        True  \n",
       "1049    0.350629        True  \n",
       "1193    0.779356        True  \n",
       "1207    0.740229        True  \n",
       "1340    0.284428        True  \n",
       "1361    0.778503        True  \n",
       "1368    0.714367        True  \n",
       "1382    0.805857        True  \n",
       "1499    0.810592        True  \n",
       "1737    0.750256        True  \n",
       "1785    0.626236        True  \n",
       "1853    0.990300        True  \n",
       "1854    0.749368        True  \n",
       "1877    0.989519        True  \n",
       "1878    0.978007        True  \n",
       "1923    0.666957        True  \n",
       "1941    0.404115       False  \n",
       "1955    0.880257       False  \n",
       "1994    0.820158        True  \n",
       "2034    0.419427        True  \n",
       "2066    0.467202        True  \n",
       "2116    0.952258       False  \n",
       "2125    0.974075       False  \n",
       "2128    0.380278        True  \n",
       "2146    0.343063       False  \n",
       "2153    0.262802       False  \n",
       "2161    0.623685        True  \n",
       "2191    0.367492        True  \n",
       "2198    0.461172        True  \n",
       "2211    0.380772       False  \n",
       "2218    0.055379        True  \n",
       "2222    0.398361       False  \n",
       "2235    0.664834        True  \n",
       "2249    0.967275        True  \n",
       "2255    0.360465        True  \n",
       "2264    0.618957        True  \n",
       "2273    0.304175        True  \n",
       "2287    0.259249        True  \n",
       "2304    0.589446       False  \n",
       "2311    0.618316        True  \n",
       "2314    0.887707        True  \n",
       "2327    0.529139        True  \n",
       "2334    0.914671       False  \n",
       "2347    0.962465       False  \n",
       "2348    0.692517        True  \n",
       "2350    0.651137        True  \n",
       "2352    0.408143        True  \n",
       "2353    0.596461        True  \n",
       "2354    0.506826        True  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tested using names of the dogs, all the tweets of dog named 'a' are different.So our table has only original tweet now.\n",
    "df_tweets_clean['name'] = df_tweets_clean['name'].str.lower()\n",
    "df_tweets_clean.name.value_counts()\n",
    "df_tweets_clean[df_tweets_clean['name'] == 'a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2042 entries, 58 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id              2042 non-null int64\n",
      "timestamp             2042 non-null datetime64[ns, UTC]\n",
      "text                  2042 non-null object\n",
      "rating_numerator      2042 non-null float64\n",
      "rating_denominator    2042 non-null float64\n",
      "name                  2042 non-null object\n",
      "doggo                 2042 non-null object\n",
      "floofer               2042 non-null object\n",
      "pupper                2042 non-null object\n",
      "puppo                 2042 non-null object\n",
      "favorite_count        2042 non-null float64\n",
      "favorited             2042 non-null object\n",
      "retweet_count         2042 non-null float64\n",
      "retweeted             2042 non-null object\n",
      "Dog_Breed             1910 non-null object\n",
      "Confidence            1910 non-null float64\n",
      "Dog_or_not            2042 non-null bool\n",
      "dtypes: bool(1), datetime64[ns, UTC](1), float64(5), int64(1), object(9)\n",
      "memory usage: 201.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10) Dog stages to be merged into single column\n",
    "#### Define:\n",
    "- merge columns doggo, floofer, pupper, puppo into single column using melt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_tweets_clean.puppo.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean = pd.melt(df_tweets_clean, id_vars=['tweet_id','timestamp','text','rating_numerator','rating_denominator','name','favorite_count','retweet_count','Dog_Breed','Confidence','Dog_or_not'], value_vars=['doggo', 'floofer', 'pupper', 'puppo'],var_name='Dog_stage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dog_stage doesnt appear to be useful so dropping it:\n",
    "df_tweets_clean.drop(columns =[\"Dog_stage\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df_tweets_clean[df_tweets_clean.value != 'None']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping any duplicated tweet_ids:\n",
    "df_tweets_clean.drop_duplicates(\"tweet_id\", keep = 'first', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean =df_tweets_clean.merge(df1, how ='left', on = 'tweet_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping the duplicate columns:\n",
    "df_tweets_clean.drop(columns=['timestamp_y','text_y','rating_numerator_y','rating_denominator_y','name_y','value_x'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean['value_y'].fillna(\"None\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the columns of the dataframe:\n",
    "df_tweets_clean.rename(index=str, columns={\"timestamp_x\": \"timestamp\", \"text_x\": \"text\", \"rating_numerator_x\":\"rating_numerator\",\"rating_denominator_x\":\"rating_denominator\",\"name_x\":\"name\",\"value_y\":\"Dog_Stage\",\"favorite_count_x\":\"favorite_count\",\"retweet_count_x\":\"retweet_count\", \"Dog_Breed_x\":\"Dog_Breed\",\"Confidence_x\":\"Confidence\",\"Dog_or_not_x\":\"Dog_or_not\"},inplace=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean.drop(columns=['favorite_count_y','retweet_count_y','Dog_Breed_y','Confidence_y','Dog_or_not_y'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>Dog_Breed</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>Dog_or_not</th>\n",
       "      <th>Dog_Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>880935762899988482</td>\n",
       "      <td>2017-06-30 23:47:07+00:00</td>\n",
       "      <td>This is Louis. He's crossing. It's a big deal....</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>louis</td>\n",
       "      <td>16482.0</td>\n",
       "      <td>2667.0</td>\n",
       "      <td>street_sign</td>\n",
       "      <td>0.251801</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>880872448815771648</td>\n",
       "      <td>2017-06-30 19:35:32+00:00</td>\n",
       "      <td>Ugh not again. We only rate dogs. Please don't...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>none</td>\n",
       "      <td>20570.0</td>\n",
       "      <td>3660.0</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.791416</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>880465832366813184</td>\n",
       "      <td>2017-06-29 16:39:47+00:00</td>\n",
       "      <td>This is Bella. She had her first beach experie...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>bella</td>\n",
       "      <td>27565.0</td>\n",
       "      <td>5986.0</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.913255</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>880221127280381952</td>\n",
       "      <td>2017-06-29 00:27:25+00:00</td>\n",
       "      <td>Meet Jesse. He's a Fetty Woof. His tongue ejec...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>jesse</td>\n",
       "      <td>26114.0</td>\n",
       "      <td>4038.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.238525</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>880095782870896641</td>\n",
       "      <td>2017-06-28 16:09:20+00:00</td>\n",
       "      <td>Please don't send in photos without dogs in th...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>none</td>\n",
       "      <td>26717.0</td>\n",
       "      <td>4157.0</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.120298</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                 timestamp  \\\n",
       "0  880935762899988482 2017-06-30 23:47:07+00:00   \n",
       "1  880872448815771648 2017-06-30 19:35:32+00:00   \n",
       "2  880465832366813184 2017-06-29 16:39:47+00:00   \n",
       "3  880221127280381952 2017-06-29 00:27:25+00:00   \n",
       "4  880095782870896641 2017-06-28 16:09:20+00:00   \n",
       "\n",
       "                                                text  rating_numerator  \\\n",
       "0  This is Louis. He's crossing. It's a big deal....              13.0   \n",
       "1  Ugh not again. We only rate dogs. Please don't...              12.0   \n",
       "2  This is Bella. She had her first beach experie...              12.0   \n",
       "3  Meet Jesse. He's a Fetty Woof. His tongue ejec...              12.0   \n",
       "4  Please don't send in photos without dogs in th...              11.0   \n",
       "\n",
       "   rating_denominator   name  favorite_count  retweet_count  \\\n",
       "0                10.0  louis         16482.0         2667.0   \n",
       "1                10.0   none         20570.0         3660.0   \n",
       "2                10.0  bella         27565.0         5986.0   \n",
       "3                10.0  jesse         26114.0         4038.0   \n",
       "4                10.0   none         26717.0         4157.0   \n",
       "\n",
       "            Dog_Breed  Confidence  Dog_or_not Dog_Stage  \n",
       "0         street_sign    0.251801       False      None  \n",
       "1            Pembroke    0.791416        True      None  \n",
       "2    golden_retriever    0.913255        True      None  \n",
       "3           Chihuahua    0.238525        True      None  \n",
       "4  miniature_pinscher    0.120298        True      None  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2053\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_tweets_clean['name']=='None').value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       1711\n",
       "pupper      231\n",
       "doggo        80\n",
       "puppo        22\n",
       "floofer       9\n",
       "Name: Dog_Stage, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets_clean.Dog_Stage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>Dog_Breed</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>Dog_or_not</th>\n",
       "      <th>Dog_Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, timestamp, text, rating_numerator, rating_denominator, name, favorite_count, retweet_count, Dog_Breed, Confidence, Dog_or_not, Dog_Stage]\n",
       "Index: []"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets_clean[(df_tweets_clean.rating_denominator > 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2053 entries, 0 to 2052\n",
      "Data columns (total 12 columns):\n",
      "tweet_id              2053 non-null int64\n",
      "timestamp             2053 non-null datetime64[ns, UTC]\n",
      "text                  2053 non-null object\n",
      "rating_numerator      2053 non-null float64\n",
      "rating_denominator    2053 non-null float64\n",
      "name                  2053 non-null object\n",
      "favorite_count        2053 non-null float64\n",
      "retweet_count         2053 non-null float64\n",
      "Dog_Breed             1920 non-null object\n",
      "Confidence            1920 non-null float64\n",
      "Dog_or_not            2053 non-null bool\n",
      "Dog_Stage             2053 non-null object\n",
      "dtypes: bool(1), datetime64[ns, UTC](1), float64(5), int64(1), object(4)\n",
      "memory usage: 154.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating backup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean_backup = df_tweets_clean.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2053 entries, 0 to 2052\n",
      "Data columns (total 12 columns):\n",
      "tweet_id              2053 non-null int64\n",
      "timestamp             2053 non-null datetime64[ns, UTC]\n",
      "text                  2053 non-null object\n",
      "rating_numerator      2053 non-null float64\n",
      "rating_denominator    2053 non-null float64\n",
      "name                  2053 non-null object\n",
      "favorite_count        2053 non-null float64\n",
      "retweet_count         2053 non-null float64\n",
      "Dog_Breed             1920 non-null object\n",
      "Confidence            1920 non-null float64\n",
      "Dog_or_not            2053 non-null bool\n",
      "Dog_Stage             2053 non-null object\n",
      "dtypes: bool(1), datetime64[ns, UTC](1), float64(5), int64(1), object(4)\n",
      "memory usage: 154.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean.Dog_or_not = df_tweets_clean.Dog_or_not.astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean = df_tweets_clean_backup.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11) Multiple Dog stages for same tweet_id\n",
    "#### Define:\n",
    "    - Merge the duplicated tweet_ids with different dog stages into 1 and save both dog stages in the Dog_Stage column.\n",
    "    - We have 11 such records."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Storing these records in separate df\n",
    "df1 = df_tweets_clean[df_tweets_clean['text'].duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using groupby and aggregation to combine the values\n",
    "df1 = df1.astype(str).groupby(df1.tweet_id, as_index=False).agg('; '.join)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>Dog_Breed</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>Dog_or_not</th>\n",
       "      <th>Dog_Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>733109485275860992; 733109485275860992</td>\n",
       "      <td>2016-05-19 01:38:16+00:00; 2016-05-19 01:38:16...</td>\n",
       "      <td>Like father (doggo), like son (pupper). Both 1...</td>\n",
       "      <td>12.0; 12.0</td>\n",
       "      <td>10.0; 10.0</td>\n",
       "      <td>none; none</td>\n",
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       "      <td>17452.0; 17452.0</td>\n",
       "      <td>golden_retriever; golden_retriever</td>\n",
       "      <td>0.945523; 0.945523</td>\n",
       "      <td>True; True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>741067306818797568; 741067306818797568</td>\n",
       "      <td>2016-06-10 00:39:48+00:00; 2016-06-10 00:39:48...</td>\n",
       "      <td>This is just downright precious af. 12/10 for ...</td>\n",
       "      <td>12.0; 12.0</td>\n",
       "      <td>10.0; 10.0</td>\n",
       "      <td>just; just</td>\n",
       "      <td>9751.0; 9751.0</td>\n",
       "      <td>3223.0; 3223.0</td>\n",
       "      <td>golden_retriever; golden_retriever</td>\n",
       "      <td>0.843799; 0.843799</td>\n",
       "      <td>True; True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>751583847268179968; 751583847268179968</td>\n",
       "      <td>2016-07-09 01:08:47+00:00; 2016-07-09 01:08:47...</td>\n",
       "      <td>Please stop sending it pictures that don't eve...</td>\n",
       "      <td>5.0; 5.0</td>\n",
       "      <td>10.0; 10.0</td>\n",
       "      <td>none; none</td>\n",
       "      <td>4553.0; 4553.0</td>\n",
       "      <td>1162.0; 1162.0</td>\n",
       "      <td>dalmatian; dalmatian</td>\n",
       "      <td>0.8683040000000001; 0.8683040000000001</td>\n",
       "      <td>True; True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>759793422261743616; 759793422261743616</td>\n",
       "      <td>2016-07-31 16:50:42+00:00; 2016-07-31 16:50:42...</td>\n",
       "      <td>Meet Maggie &amp;amp; Lila. Maggie is the doggo, L...</td>\n",
       "      <td>12.0; 12.0</td>\n",
       "      <td>10.0; 10.0</td>\n",
       "      <td>maggie; maggie</td>\n",
       "      <td>6198.0; 6198.0</td>\n",
       "      <td>2007.0; 2007.0</td>\n",
       "      <td>golden_retriever; golden_retriever</td>\n",
       "      <td>0.985876; 0.985876</td>\n",
       "      <td>True; True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>781308096455073793; 781308096455073793</td>\n",
       "      <td>2016-09-29 01:42:20+00:00; 2016-09-29 01:42:20...</td>\n",
       "      <td>Pupper butt 1, Doggo 0. Both 12/10 https://t.c...</td>\n",
       "      <td>12.0; 12.0</td>\n",
       "      <td>10.0; 10.0</td>\n",
       "      <td>none; none</td>\n",
       "      <td>7473.0; 7473.0</td>\n",
       "      <td>2711.0; 2711.0</td>\n",
       "      <td>nan; nan</td>\n",
       "      <td>nan; nan</td>\n",
       "      <td>True; True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 tweet_id  \\\n",
       "0  733109485275860992; 733109485275860992   \n",
       "1  741067306818797568; 741067306818797568   \n",
       "2  751583847268179968; 751583847268179968   \n",
       "3  759793422261743616; 759793422261743616   \n",
       "4  781308096455073793; 781308096455073793   \n",
       "\n",
       "                                           timestamp  \\\n",
       "0  2016-05-19 01:38:16+00:00; 2016-05-19 01:38:16...   \n",
       "1  2016-06-10 00:39:48+00:00; 2016-06-10 00:39:48...   \n",
       "2  2016-07-09 01:08:47+00:00; 2016-07-09 01:08:47...   \n",
       "3  2016-07-31 16:50:42+00:00; 2016-07-31 16:50:42...   \n",
       "4  2016-09-29 01:42:20+00:00; 2016-09-29 01:42:20...   \n",
       "\n",
       "                                                text rating_numerator  \\\n",
       "0  Like father (doggo), like son (pupper). Both 1...       12.0; 12.0   \n",
       "1  This is just downright precious af. 12/10 for ...       12.0; 12.0   \n",
       "2  Please stop sending it pictures that don't eve...         5.0; 5.0   \n",
       "3  Meet Maggie &amp; Lila. Maggie is the doggo, L...       12.0; 12.0   \n",
       "4  Pupper butt 1, Doggo 0. Both 12/10 https://t.c...       12.0; 12.0   \n",
       "\n",
       "  rating_denominator            name    favorite_count     retweet_count  \\\n",
       "0         10.0; 10.0      none; none  50603.0; 50603.0  17452.0; 17452.0   \n",
       "1         10.0; 10.0      just; just    9751.0; 9751.0    3223.0; 3223.0   \n",
       "2         10.0; 10.0      none; none    4553.0; 4553.0    1162.0; 1162.0   \n",
       "3         10.0; 10.0  maggie; maggie    6198.0; 6198.0    2007.0; 2007.0   \n",
       "4         10.0; 10.0      none; none    7473.0; 7473.0    2711.0; 2711.0   \n",
       "\n",
       "                            Dog_Breed                              Confidence  \\\n",
       "0  golden_retriever; golden_retriever                      0.945523; 0.945523   \n",
       "1  golden_retriever; golden_retriever                      0.843799; 0.843799   \n",
       "2                dalmatian; dalmatian  0.8683040000000001; 0.8683040000000001   \n",
       "3  golden_retriever; golden_retriever                      0.985876; 0.985876   \n",
       "4                            nan; nan                                nan; nan   \n",
       "\n",
       "   Dog_or_not      Dog_Stage  \n",
       "0  True; True  doggo; pupper  \n",
       "1  True; True  doggo; pupper  \n",
       "2  True; True  doggo; pupper  \n",
       "3  True; True  doggo; pupper  \n",
       "4  True; True  doggo; pupper  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping all the duplicated values in the same columns\n",
    "df1['tweet_id'] = df1['tweet_id'].str.split(';').str[0]\n",
    "df1['timestamp'] = df1['timestamp'].str.split(';').str[0]\n",
    "df1['text'] = df1['text'].str.split(';').str[0]\n",
    "df1['rating_numerator'] = df1['rating_numerator'].str.split(';').str[0]\n",
    "df1['rating_denominator'] = df1['rating_denominator'].str.split(';').str[0]\n",
    "df1['favorite_count'] = df1['favorite_count'].str.split(';').str[0]\n",
    "df1['retweet_count'] = df1['retweet_count'].str.split(';').str[0]\n",
    "df1['Dog_Breed'] = df1['Dog_Breed'].str.split(';').str[0]\n",
    "df1['Confidence'] = df1['Confidence'].str.split(';').str[0]\n",
    "df1['Dog_or_not'] = df1['Dog_or_not'].str.split(';').str[0]\n",
    "df1['name'] = df1['name'].str.split(';').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>Dog_Breed</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>Dog_or_not</th>\n",
       "      <th>Dog_Stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>733109485275860992</td>\n",
       "      <td>2016-05-19 01:38:16+00:00</td>\n",
       "      <td>Like father (doggo), like son (pupper). Both 1...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>none</td>\n",
       "      <td>50603.0</td>\n",
       "      <td>17452.0</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.945523</td>\n",
       "      <td>True</td>\n",
       "      <td>doggo; pupper</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>741067306818797568</td>\n",
       "      <td>2016-06-10 00:39:48+00:00</td>\n",
       "      <td>This is just downright precious af. 12/10 for ...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>just</td>\n",
       "      <td>9751.0</td>\n",
       "      <td>3223.0</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.843799</td>\n",
       "      <td>True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>751583847268179968</td>\n",
       "      <td>2016-07-09 01:08:47+00:00</td>\n",
       "      <td>Please stop sending it pictures that don't eve...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>none</td>\n",
       "      <td>4553.0</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>dalmatian</td>\n",
       "      <td>0.8683040000000001</td>\n",
       "      <td>True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>759793422261743616</td>\n",
       "      <td>2016-07-31 16:50:42+00:00</td>\n",
       "      <td>Meet Maggie &amp;amp</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>maggie</td>\n",
       "      <td>6198.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.985876</td>\n",
       "      <td>True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>781308096455073793</td>\n",
       "      <td>2016-09-29 01:42:20+00:00</td>\n",
       "      <td>Pupper butt 1, Doggo 0. Both 12/10 https://t.c...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>none</td>\n",
       "      <td>7473.0</td>\n",
       "      <td>2711.0</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>785639753186217984</td>\n",
       "      <td>2016-10-11 00:34:48+00:00</td>\n",
       "      <td>This is Pinot. He's a sophisticated doggo. You...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>pinot</td>\n",
       "      <td>8220.0</td>\n",
       "      <td>2374.0</td>\n",
       "      <td>porcupine</td>\n",
       "      <td>0.978042</td>\n",
       "      <td>False</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>801115127852503040</td>\n",
       "      <td>2016-11-22 17:28:25+00:00</td>\n",
       "      <td>This is Bones. He's being haunted by another d...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>bones</td>\n",
       "      <td>8506.0</td>\n",
       "      <td>2248.0</td>\n",
       "      <td>dalmatian</td>\n",
       "      <td>0.823356</td>\n",
       "      <td>True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>808106460588765185</td>\n",
       "      <td>2016-12-12 00:29:28+00:00</td>\n",
       "      <td>Here we have Burke (pupper) and Dexter (doggo)...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>none</td>\n",
       "      <td>9220.0</td>\n",
       "      <td>2357.0</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.426183</td>\n",
       "      <td>True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>817777686764523521</td>\n",
       "      <td>2017-01-07 16:59:28+00:00</td>\n",
       "      <td>This is Dido. She's playing the lead role in \"...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>dido</td>\n",
       "      <td>11219.0</td>\n",
       "      <td>2858.0</td>\n",
       "      <td>curly-coated_retriever</td>\n",
       "      <td>0.733256</td>\n",
       "      <td>True</td>\n",
       "      <td>doggo; pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>854010172552949760</td>\n",
       "      <td>2017-04-17 16:34:26+00:00</td>\n",
       "      <td>At first I thought this was a shy doggo, but i...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>none</td>\n",
       "      <td>16262.0</td>\n",
       "      <td>3189.0</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.354733</td>\n",
       "      <td>True</td>\n",
       "      <td>doggo; floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>855851453814013952</td>\n",
       "      <td>2017-04-22 18:31:02+00:00</td>\n",
       "      <td>Here's a puppo participating in the #ScienceMa...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>none</td>\n",
       "      <td>45548.0</td>\n",
       "      <td>17930.0</td>\n",
       "      <td>flat-coated_retriever</td>\n",
       "      <td>0.321676</td>\n",
       "      <td>True</td>\n",
       "      <td>doggo; puppo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id                  timestamp  \\\n",
       "0   733109485275860992  2016-05-19 01:38:16+00:00   \n",
       "1   741067306818797568  2016-06-10 00:39:48+00:00   \n",
       "2   751583847268179968  2016-07-09 01:08:47+00:00   \n",
       "3   759793422261743616  2016-07-31 16:50:42+00:00   \n",
       "4   781308096455073793  2016-09-29 01:42:20+00:00   \n",
       "5   785639753186217984  2016-10-11 00:34:48+00:00   \n",
       "6   801115127852503040  2016-11-22 17:28:25+00:00   \n",
       "7   808106460588765185  2016-12-12 00:29:28+00:00   \n",
       "8   817777686764523521  2017-01-07 16:59:28+00:00   \n",
       "9   854010172552949760  2017-04-17 16:34:26+00:00   \n",
       "10  855851453814013952  2017-04-22 18:31:02+00:00   \n",
       "\n",
       "                                                 text rating_numerator  \\\n",
       "0   Like father (doggo), like son (pupper). Both 1...             12.0   \n",
       "1   This is just downright precious af. 12/10 for ...             12.0   \n",
       "2   Please stop sending it pictures that don't eve...              5.0   \n",
       "3                                    Meet Maggie &amp             12.0   \n",
       "4   Pupper butt 1, Doggo 0. Both 12/10 https://t.c...             12.0   \n",
       "5   This is Pinot. He's a sophisticated doggo. You...             10.0   \n",
       "6   This is Bones. He's being haunted by another d...             12.0   \n",
       "7   Here we have Burke (pupper) and Dexter (doggo)...             12.0   \n",
       "8   This is Dido. She's playing the lead role in \"...             13.0   \n",
       "9   At first I thought this was a shy doggo, but i...             11.0   \n",
       "10  Here's a puppo participating in the #ScienceMa...             13.0   \n",
       "\n",
       "   rating_denominator    name favorite_count retweet_count  \\\n",
       "0                10.0    none        50603.0       17452.0   \n",
       "1                10.0    just         9751.0        3223.0   \n",
       "2                10.0    none         4553.0        1162.0   \n",
       "3                10.0  maggie         6198.0        2007.0   \n",
       "4                10.0    none         7473.0        2711.0   \n",
       "5                10.0   pinot         8220.0        2374.0   \n",
       "6                10.0   bones         8506.0        2248.0   \n",
       "7                10.0    none         9220.0        2357.0   \n",
       "8                10.0    dido        11219.0        2858.0   \n",
       "9                10.0    none        16262.0        3189.0   \n",
       "10               10.0    none        45548.0       17930.0   \n",
       "\n",
       "                 Dog_Breed          Confidence Dog_or_not       Dog_Stage  \n",
       "0         golden_retriever            0.945523       True   doggo; pupper  \n",
       "1         golden_retriever            0.843799       True   doggo; pupper  \n",
       "2                dalmatian  0.8683040000000001       True   doggo; pupper  \n",
       "3         golden_retriever            0.985876       True   doggo; pupper  \n",
       "4                      nan                 nan       True   doggo; pupper  \n",
       "5                porcupine            0.978042      False   doggo; pupper  \n",
       "6                dalmatian            0.823356       True   doggo; pupper  \n",
       "7         golden_retriever            0.426183       True   doggo; pupper  \n",
       "8   curly-coated_retriever            0.733256       True   doggo; pupper  \n",
       "9         English_springer            0.354733       True  doggo; floofer  \n",
       "10   flat-coated_retriever            0.321676       True    doggo; puppo  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting back to their original datatypes:\n",
    "df1.timestamp = pd.to_datetime(df1.timestamp)\n",
    "df1.tweet_id = pd.to_numeric(df1.tweet_id, errors='coerce')\n",
    "df1.rating_numerator = df1.rating_numerator.astype('float')\n",
    "df1.rating_denominator = df1.rating_denominator.astype('float')\n",
    "df1.favorite_count = df1.favorite_count.astype('float')\n",
    "df1.retweet_count = df1.retweet_count.astype('float')\n",
    "df1.Confidence = df1.Confidence.astype('float')\n",
    "df1.Dog_or_not = df1.Dog_or_not.astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging df1 with original dataframe\n",
    "df_tweets_clean = df_tweets_clean.merge(df1, on ='tweet_id', how ='left', suffixes = ('','_y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2053 entries, 0 to 2052\n",
      "Data columns (total 23 columns):\n",
      "tweet_id                2053 non-null int64\n",
      "timestamp               2053 non-null datetime64[ns, UTC]\n",
      "text                    2053 non-null object\n",
      "rating_numerator        2053 non-null float64\n",
      "rating_denominator      2053 non-null float64\n",
      "name                    2053 non-null object\n",
      "favorite_count          2053 non-null float64\n",
      "retweet_count           2053 non-null float64\n",
      "Dog_Breed               1920 non-null object\n",
      "Confidence              1920 non-null float64\n",
      "Dog_or_not              2053 non-null bool\n",
      "Dog_Stage               2053 non-null object\n",
      "timestamp_y             22 non-null datetime64[ns, UTC]\n",
      "text_y                  22 non-null object\n",
      "rating_numerator_y      22 non-null float64\n",
      "rating_denominator_y    22 non-null float64\n",
      "name_y                  22 non-null object\n",
      "favorite_count_y        22 non-null float64\n",
      "retweet_count_y         22 non-null float64\n",
      "Dog_Breed_y             22 non-null object\n",
      "Confidence_y            20 non-null float64\n",
      "Dog_or_not_y            22 non-null object\n",
      "Dog_Stage_y             22 non-null object\n",
      "dtypes: bool(1), datetime64[ns, UTC](2), float64(10), int64(1), object(9)\n",
      "memory usage: 298.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping unnecesaary columns created during merge:\n",
    "df_tweets_clean = df_tweets_clean.drop(columns=['timestamp_y','text_y', 'rating_numerator_y', 'rating_denominator_y', 'name_y', 'favorite_count_y', 'retweet_count_y', 'Dog_Breed_y','Confidence_y','Dog_or_not_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2053 entries, 0 to 2052\n",
      "Data columns (total 13 columns):\n",
      "tweet_id              2053 non-null int64\n",
      "timestamp             2053 non-null datetime64[ns, UTC]\n",
      "text                  2053 non-null object\n",
      "rating_numerator      2053 non-null float64\n",
      "rating_denominator    2053 non-null float64\n",
      "name                  2053 non-null object\n",
      "favorite_count        2053 non-null float64\n",
      "retweet_count         2053 non-null float64\n",
      "Dog_Breed             1920 non-null object\n",
      "Confidence            1920 non-null float64\n",
      "Dog_or_not            2053 non-null bool\n",
      "Dog_Stage             2053 non-null object\n",
      "Dog_Stage_y           2053 non-null object\n",
      "dtypes: bool(1), datetime64[ns, UTC](1), float64(5), int64(1), object(5)\n",
      "memory usage: 170.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#Combining the two Dog_Stage columns\n",
    "df_tweets_clean['Dog_Stage_y'] = df_tweets_clean['Dog_Stage_y'].fillna(df_tweets_clean['Dog_Stage'])\n",
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping old Dog_Stage column\n",
    "df_tweets_clean = df_tweets_clean.drop(columns=['Dog_Stage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None              1711\n",
       "pupper             222\n",
       "doggo               69\n",
       "puppo               21\n",
       "doggo; pupper       18\n",
       "floofer              8\n",
       "doggo; puppo         2\n",
       "doggo; floofer       2\n",
       "Name: Dog_Stage_y, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets_clean['Dog_Stage_y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2053 entries, 0 to 2052\n",
      "Data columns (total 12 columns):\n",
      "tweet_id              2053 non-null int64\n",
      "timestamp             2053 non-null datetime64[ns, UTC]\n",
      "text                  2053 non-null object\n",
      "rating_numerator      2053 non-null float64\n",
      "rating_denominator    2053 non-null float64\n",
      "name                  2053 non-null object\n",
      "favorite_count        2053 non-null float64\n",
      "retweet_count         2053 non-null float64\n",
      "Dog_Breed             1920 non-null object\n",
      "Confidence            1920 non-null float64\n",
      "Dog_or_not            2053 non-null bool\n",
      "Dog_Stage_y           2053 non-null object\n",
      "dtypes: bool(1), datetime64[ns, UTC](1), float64(5), int64(1), object(4)\n",
      "memory usage: 162.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ANALYSING OUR DATA WHICH IS NOW CLEANED."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1) Observations from the describe function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "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>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>Confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.053000e+03</td>\n",
       "      <td>2053.000000</td>\n",
       "      <td>2053.0</td>\n",
       "      <td>2053.000000</td>\n",
       "      <td>2053.000000</td>\n",
       "      <td>1920.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.336754e+17</td>\n",
       "      <td>10.565163</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7967.521189</td>\n",
       "      <td>2522.213346</td>\n",
       "      <td>0.593981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.390110e+16</td>\n",
       "      <td>2.148656</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11907.276024</td>\n",
       "      <td>4586.546596</td>\n",
       "      <td>0.272532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.044333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.765338e+17</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>0.360456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.087381e+17</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3737.000000</td>\n",
       "      <td>1223.000000</td>\n",
       "      <td>0.587635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.833346e+17</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9879.000000</td>\n",
       "      <td>2786.000000</td>\n",
       "      <td>0.848400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.809358e+17</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>161516.000000</td>\n",
       "      <td>81626.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  rating_numerator  rating_denominator  favorite_count  \\\n",
       "count  2.053000e+03       2053.000000              2053.0     2053.000000   \n",
       "mean   7.336754e+17         10.565163                10.0     7967.521189   \n",
       "std    6.390110e+16          2.148656                 0.0    11907.276024   \n",
       "min    6.660209e+17          0.000000                10.0        0.000000   \n",
       "25%    6.765338e+17         10.000000                10.0     1763.000000   \n",
       "50%    7.087381e+17         11.000000                10.0     3737.000000   \n",
       "75%    7.833346e+17         12.000000                10.0     9879.000000   \n",
       "max    8.809358e+17         15.000000                10.0   161516.000000   \n",
       "\n",
       "       retweet_count   Confidence  \n",
       "count    2053.000000  1920.000000  \n",
       "mean     2522.213346     0.593981  \n",
       "std      4586.546596     0.272532  \n",
       "min        11.000000     0.044333  \n",
       "25%       565.000000     0.360456  \n",
       "50%      1223.000000     0.587635  \n",
       "75%      2786.000000     0.848400  \n",
       "max     81626.000000     1.000000  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets_clean.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " * Maximum rating a Dog has received out of 10 is 15 and minimum is 0 (Could be a dog on carpet case :D )\n",
    " * Mean rating is 10.56/10 , 75% of dogs have received 12 rating.\n",
    " * Mean of fav count of a tweet is approx 7967.5\n",
    " * Mean of retweet count of a tweet is approx 2522.2\n",
    " * Mean confidence about a prediction is 0.594, max is 1 as expected and min id 0.04"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  2) Distribution tweets over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\mishra\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\plotting\\_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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1oGWgpg9rqQYB3mbHcmiUZgAAgEoiJStfz6yLU9TeU6pfzUtvTeiofi0CzY5VLlCaAQAAKjiL1aYPfk7UC5sOqsBi04N9m+q+Pk3l6eZidrRyg9IMAABQge05fl7TvopW7OlM9WxWQ7OHhymoho/ZscodSjMAAEAFdD6nUM9tjNfKnSdU289Tr4xtryGta8vJqXJd/rq0UJoBAAAqEJvN0Ke7Tui5jfHKzLfozp5Beqh/iHw9qH3/BtMDAACoIGJOZWhaVLR+O56uzo0DNDuilUJr+5kdq0KgNAMAAJRzmflFWrTpoFb8dEzVvN31wug2GtG+HksxShGlGQAAoJwyDEOr9p7SvPVxOptdoHFdGum/A5vL39vN7GgVDqUZAACgHDqUnKXIVdH6OSFNber7660JHXVN/apmx6qwKM0AAADlSG6hRUu2HNbyHxLk4+GquRFhuqVzQ7k4sxTDnijNAAAA5YBhGPo65oxmr4nVqYx8je5QX1MHh6q6r4fZ0SoFSjMAAICDSzyXoxmrY/TdgVSF1q6iJbe0U8fGAWbHqlQozQAAAA4qv8iqZduO6NXvjsjN2UnThrbQxG6N5eribHa0SofSDAAA4IC+O5CiGatjlHguV+Ft6mra0BYK9PM0O1alRWkGAABwIKfS8zR7Taw2xpxRk5o++uD2LurRrIbZsSo9SjMAAIADKLLa9PaPR7V4yyHZDEOPDWquO3oGycPVxexoEKUZAADAdD8nnFNkVLQOpWSrf4tAzQhvqQYB3mbHwp9QmgEAAEySlmfRlE/26qvfklS/mpeW39ZR/VsGmh0Ll0BpBgAAKGNWm6EPfk7UcxtOyGJz0n/6NtV91zWVlztLMRwVpRkAAKAM7Tl+XpFR0Yo5lal2dbz0wtjOalLT1+xYKAGlGQAAoAyczynUgq/j9fGvJxTo56GXx7ZTE9d0CnM5QWkGAACwI5vN0Ge7T+jZDfHKzLfojh5BenhAiHw9XBUXl2F2PFwhSjMAAICdxJzKUGRUtPYcT1enxtU0JyJMobX9zI6Ff4DSDAAAUMoy84u0aNNBrfjpmKp5u2vh6DYa2b6enJyczI6Gf4jSDAAAUEoMw9Dqfac0d12czmYX6NYuDfXYwFD5e7uZHQ3/EqUZAACgFBxOyVJkVIx+Sjina+r7a/ltHdWmQVWzY6GUUJoBAAD+hdxCi5Z+e1jLf0iQl5uL5kaE6ZbODeXizFKMioTSDAAA8A8YhqFNscmavSZWSel5GtWhvqYODlUNXw+zo8EOKM0AAABX6fi5XM1YHa2tB1LVPLCKPrvnWnVqHGB2LNgRpRkAAOAK5RdZ9fq2BL363WG5Ojtp2tAWmtCtsdxcnM2OBjujNAMAAFyBbQdTNWNVtI6dy9XQa+oocmhL1fb3NDsWygilGQAA4G+czsjTnLWxWr//jIJq+Oj92zurZ7OaZsdCGbNLaS4qKtLUqVOVlJQkZ2dnzZkzR8HBwcX3v/POO/r8888VEPDH2p9Zs2apSZMm9ogCAADwjxRZbXr7x6NavOWQrDZD/x0Yojt7NZGHq4vZ0WACu5Tmbdu2yWKxaOXKldq+fbteeuklLV26tPj+mJgYPffccwoLC7PH0wMAAPwrvyScU+SqaB1Mzlb/FrU0I7yVGgR4mx0LJrJLaQ4KCpLVapXNZlN2drZcXS9+mpiYGL3xxhtKTU3Vddddp7vvvtseMQAAAK5KalaB5q+P05e/JaleVS+9eVtHDWgZaHYsOAC7lGZvb28lJSVp8ODBOn/+vJYtW3bR/UOHDtXYsWPl6+urBx54QFu3blWfPn3sEQUAAKBEVpuhD39J1PNfH1B+kVUP9Gmq+/s0lZc7SzHwByfDMIzS3un8+fPl7u6uRx99VKdPn9aECRO0Zs0aeXh4yDAMZWdnq0qVKpKkDz/8UOnp6br//vsvu7+9e/fKw8MxThSen58vT0+OlGUOFzCLC5jFxZjHBcziAmbheDOIT83XKz+f1eG0QrWr46X7ulRXfX/3MnluR5uFmRxpFi1atPjLNru80uzn5yc3NzdJkr+/vywWi6xWqyQpOztbw4YN0/r16+Xt7a1ffvlFI0eO/Nv9eXh4XDK8GeLi4hwmi5mYwwXM4gJmcTHmcQGzuIBZOM4M0nML9dzGA1q585Rq+npo6S3tNOyaOnJyKrvLXzvKLByBo8wiLi7uktvtUponTpyop556SmPHjlVRUZGmTJmiLVu2KDc3VzfffLOmTJmi2267Te7u7rr22mvVu3dve8QAAAD4C5vN0Oe7T+rZjfHKyCvS5O5Berh/M1XxdDM7GhyYXUqzj4+PFi9efNn7IyIiFBERYY+nBgAAuKzYU5mKXBWt3Ynn1bFRNc2JCFOLOn5mx0I5wMVNAABAhZeVX6QXNx/Sez8dk7+Xm54fdY1Gtq8vZ+eyW4qB8o3SDAAAKizDMLTm99OauzZWqdkFGtu5oR4b1FxVvcvmQD9UHJRmAABQIR1Oydb0VdHaceScWtfz15u3dVSbBlXNjoVyitIMAAAqlNxCi17+9rDe/CFBXm4umhMRprGdG8qFpRj4FyjNAACgQjAMQ5tjkzVrTayS0vM0sn19PTkkVDV8HeNaDyjfKM0AAKDcO34uVzPXxOjb+BQ1D6yiT+++Vp2DAsyOhQqE0gwAAMqtAotVr29L0CtbD8vV2UlPD2mhid0by83F2exoqGAozQAAoFz6/mCqZqyO0dGzORrauo6mDWuhOv5eZsdCBUVpBgAA5crpjDzNXRundftPK6iGj1ZM7qxeITXNjoUKjtIMAADKhSKrTe9sP6qXvjkkq83QowNCdFfvJvJwdTE7GioBSjMAAHB4vx5N07So/TqYnK2+obU064ZWahDgbXYsVCKUZgAA4LBSswo0f0OcvtyTpHpVvfTG+A4a0DJQTk6ccxlli9IMAAAcjtVm6KNfErXg6wPKL7Lq/j7BeqBPM3m5sxQD5qA0AwAAh7LvRLqmRUVrf1KGugVX1+zhYWpay9fsWKjkKM0AAMAhpOcW6vmvD+ijX4+rpq+HltzSTuHX1GEpBhwCpRkAAJjKZjP0+Z6TenZDvDLyijSpW5CmDGimKp5uZkcDilGaAQCAaeJOZyoyKlq7Es+rQ6NqmjM8TC3r+pkdC/gLSjMAAChzWflFeumbQ3p3xzH5e7lpwahrNKp9fTk7sxQDjonSDAAAyoxhGPruaLbe+XKbUrMLdEvnhnp8UHNV9XY3OxrwtyjNAACgTBxJzdb0VdHafvicwur56Y3bOqptg6pmxwKuCKUZAADYVV6hVS9vPaQ3vk+Qp5uL7utSXY8O7yIXlmKgHKE0AwAAu9kcm6yZq2OUlJ6nEe3r6cnBLXT2ZAKFGeUOpRkAAJS6E2m5mrk6RlviUxQS6KtP7uqqLk2qS5LOmpwN+CcozQAAoNQUWKx68/sELf32sFycnfTUkFBN6h4kNxdns6MB/wqlGQAAlIofDqVqxqoYJZzN0ZDWtRU5rKXq+HuZHQsoFZRmAADwr5zJyNecdbFa9/tpNa7urfcmd1bvkJpmxwJKFaUZAAD8I0VWm97bcUwvbj4oi83QIwNCdFevJvJ0czE7GlDqKM0AAOCq/Xo0TZFR0TqQnKW+obU0M7yVGlb3NjsWYDeUZgAAcMXOZhdo/vp4fbHnpOpV9dIb4ztoQMtAOTlxCjlUbJRmAABQIqvN0Ee/HtfzG+OVV2TVvdcF6z99m8rbnSqByoHvdAAA8Lf2nUhX5Kpo/X4yQ92Cq2v28DA1reVrdiygTFGaAQDAJWXkFmnB1/H66NfjquHrocVj2uqGNnVZioFKidIMAAAuYhiGPt99Us9uiNf53EJN7NZYUwaEyM/TzexogGkozQAAoFj8mUxFRkVr57Hzat+wqlbc3lmt6vqbHQswHaUZAAAou8CilzYf1Ds7jsnP01ULRl6jUR3qy9mZpRiARGkGAKBSMwxD6/af1py1sUrJKtCYTg31+KDmqubjbnY0wKFQmgEAqKQSUrM1fVWMfjx8Vq3q+mnZuA5q17Ca2bEAh0RpBgCgkskrtOqVrYf1xvcJ8nBz1qwbWmlc10ZyYSkGcFmUZgAAKpFvYpM1c02MTp7P04h29fTkkBaqWcXD7FiAw6M0AwBQCZxIy9WsNbH6Ji5ZzWr5auVdXdW1SXWzYwHlBqUZAIAKrMBi1ZvfJ+jlrYfl7OSkJweHanKPILm5OJsdDShXKM0AAFRQPx46q+mropVwNkeDw2orclhL1a3qZXYsoFyiNAMAUMGcycjX3HWxWvv7aTWq7q13J3XSdc1rmR0LKNcozQAAVBAWq03v7jimFzcfVJHN0JT+Ibq7dxN5urmYHQ0o9yjNAABUADuPpSkyKlrxZ7LUp3lNzbyhlRpV9zE7FlBhUJoBACjHzmUXaP6GeH2++6Tq+ntq2bgOGtQqUE5OnHMZKE2UZgAAyiGrzdDHvx7X818fUE6BRff0DtaD/ZrK250/7YA98JMFAEA5s/9khqZF7de+kxnq2iRAc4aHqVlgFbNjARUapRkAgHIiI7dICzcd0Ae/JKq6j4cWj2mrG9rUZSkGUAYozQAAODjDMPTlniQ9sz5O53MLNeHaxnpkYIj8PN3MjgZUGpRmAAAc2IEzWYqMitavx9LUrmFVrbi9s1rV9Tc7FlDpUJoBAHBA2QUWLf7moN7efkx+nq56bmRrje7QQM7OLMUAzEBpBgDAgRiGofX7z2j22hglZxbols4N9PigUFXzcTc7GlCpUZoBAHAQCanZmrE6Rj8cOqtWdf302rgOat+wmtmxAIjSDACA6fKLrHpl62G9vi1BHq7OmhneUuO6NpKri7PZ0QD8f5RmAABMtCUuWTPXxOhEWp4i2tbVU0NbqFYVT7NjAfgflGYAAExw8nyuZq2J1ebYZDWt5auP7+yqa4Ormx0LwGVQmgEAKEOFVkOvbD2spd8ekpOcNHVwqCZ3D5K7K0sxAEdGaQYAoIxsP3xWT6w+qZOZRbq+VW1FhrdUvapeZscCcAUozQAA2FlyZr7mrovTmn2nVKeKq96Z1El9mtcyOxaAq0BpBgDATixWm977KVEvbj6oQqtND/dvpj61i9SGwgyUO5RmAADsYNexNE2Lilb8mSz1Dqmp2cNbqVF1H8XFxZkdDcA/QGkGAKAUncsu0LMb4vXZ7pOq6++pZePaa1Cr2nJy4vLXQHlGaQYAoBTYbIY+3nlcCzYeUE6BRff0DtaD/ZrK250/tUBFwE8yAAD/0v6TGZq2Klr7TqSrS1CA5kaEqVlgFbNjAShFlGYAAP6hjLwivbDpgN7/OVHVfTz00s1tNbxtXZZiABUQpRkAgKtkGIa+3JOk+RvilJZTqAnXNtaUASHy93IzOxoAO6E0AwBwFQ6cyVLkqmj9ejRNbRtU1buTOiusnr/ZsQDYGaUZAIArkFNg0eIth/T2j0fl6+mq+SNa6+aODeTszFIMoDKgNAMA8DcMw9CG6DOavSZWZzLzNaZTAz1+fagCfNzNjgagDFGaAQC4jKNnczR9VbR+OHRWLev46ZVb26tDo2pmxwJgAkozAAD/I7/Iqle3HtaybQnycHXWjPCWGt+1kVxdnM2OBsAklGYAAP7k2/hkzVgdoxNpeYpoW1dPDWmhWn6eZscCYDJKMwAAkk6ez9XsNbHaFJusprV89dGdXdQtuIbZsQA4CEozAKBSK7TYtPzHBC3ZckhOctIT14fq9h5BcndlKQaACyjNAIBKa8fhs4pcFa0jqTka1CpQ08NbqV5VL7NjAXBAlGYAQKWTkpmvuevitHrfKTUM8NY7EzupT2gts2MBcGCUZgBApWGx2rTip0Qt2nxQhVabHurXTPdeFyxPNxezowFwcJRmAEClsDsxTdOiYhR3OlO9Qmpq9g2t1LiGj9mxAJQTlGYAQIWWllOoZzfE6dNdJ1XH31Ov3dpe14fVlpMTl78GcOUozQCACslmM7Ry5wkt+Dpe2fkW3d2riR7s10w+HvzpA3D1+M0BAKhwopMyNC0qWntPpKtzUIDmRoQpJLCK2bEAlGOUZgBAhZGRV6RFmw7o/Z8TFeDjoRdvbqOItvVYigHgX6M0AwDKPcMwFLU3SfPWxSstp0DjuzbSIwOby86brhYAACAASURBVN/LzexoACoIu5TmoqIiTZ06VUlJSXJ2dtacOXMUHBxcfP+3336rV155Ra6urho5cqRuuukme8QAAFQCB5OzFBkVrV+OpqlNg6p6d1InhdXzNzsWgArGLqV527ZtslgsWrlypbZv366XXnpJS5culfRHoZ4/f74+//xzeXl56ZZbblGfPn1Us2ZNe0QBAFRQOQUWLdlySG/9eFQ+Hq6aP6K1bu7YQM7OLMUAUPrsUpqDgoJktVpls9mUnZ0tV9cLT3PkyBE1bNhQ/v5/vArQoUMH7dq1S4MHD7ZHFABABWMYhjZGn9HstbE6nZGvmzs20BODQxXg4252NAAVmF1Ks7e3t5KSkjR48GCdP39ey5YtK74vOztbVapcOILZx8dH2dnZ9ogBAKhgjp3N0YzVMdp2MFUt6vjp5bHt1KFRgNmxAFQCdinN7777rnr06KFHH31Up0+f1oQJE7RmzRp5eHjI19dXOTk5xY/Nycm5qERfSkFBgeLi4uwR9arl5+c7TBYzMYcLmMUFzOJizOOCfzuLAotNn0an67P9GXJ1ke7uVF3hoX5yyU1WXFxyKSa1P74vmMGfMYsLHH0WdinNfn5+cnP744hlf39/WSwWWa1WSVJwcLASExOVnp4ub29v7dq1S7fffvvf7s/Dw0MtWrSwR9SrFhcX5zBZzMQcLmAWFzCLizGPC/7NLLbGp2jGhhgdT8vVDW3qatrQFqrl51nKCcsO3xfM4M+YxQWOMovLFXe7lOaJEyfqqaee0tixY1VUVKQpU6Zoy5Ytys3N1c0336ypU6fq9ttvl2EYGjlypAIDA+0RAwBQjiWl52n2mhh9HZOs4Jo++uiOLurWtIbZsQBUUnYpzT4+Plq8ePFl7+/bt6/69u1rj6cGAJRzhRab3vrxqJZsOSRDhh6/vrnu6NFE7q7OZkcDUIlxcRMAgMPYceSspq+K0eGUbA1sGajp4S1Vv5q32bEAgNIMADBfSma+5q2P06q9p9QgwEtvT+yovqEs3QPgOCjNAADTWKw2vf9zohZtOqgCi00P9m2q+/o0laebi9nRAOAilGYAgCn2HD+vaV9FK/Z0pnqF1NSsG1opqIaP2bEA4JIozQCAMpWWU6jnNsTrk10nVNvPU6/e2l6Dw2rLyYnLXwNwXJRmAECZsNkMfbrrhJ7dGK/sfIvu6tVED/ZrJl8P/hQBcHz8pgIA2N3hcwV6atkO/XY8XZ0bB2hORJia1/77q8ECgCOhNAMA7CYzv0iLNh3Uip+SFODjrkU3tdGN7eqxFANAuUNpBgCUOsMwtGrvKc1dF6dzOQUaFuKnuWO6yt/LzexoAPCPUJoBAKXqUHKWIldF6+eENLVpUFXvTOwk16xTFGYA5RqlGQBQKnIKLFry7SG99cNR+Xi46pkbW2tMpwZydnZSXNwps+MBwL9CaQYA/CuGYejrmDOavSZWpzLyNbpDfU0dHKrqvh5mRwOAUkNpBgD8Y4nncjRjdYy+O5Cq0NpVtOSWdurYOMDsWABQ6ijNAICrll9k1WvfHdFr247I3cVZkcNaasK1jeTq4mx2NACwC0ozAOCqbD2QopmrY5R4Llfhbepq2tAWCvTzNDsWANhViS8JzJ49+6Lbjz/+uN3CAAAc16n0PN3z/m5NemenXJyd9OEdXbT0lnYUZgCVwmVfaf7www/12muvKT09XZs2bZL0x8EeTZs2LbNwAADzFVpsenv7US3+5pAMGXpsUHPd0TNIHq4uZkcDgDJz2dJ866236tZbb9WyZct0zz33lGUmAICD+OnIOUWuitbhlGwNaBmo6cNaqkGAt9mxAKDMlbimedy4cVq/fr0KCwuLt0VERNg1FADAXClZ+XpmXZyi9p5S/WpeemtCR/VrEWh2LAAwTYml+b777lOtWrVUp04dSZKTk5PdQwEAzGGx2vTBz4l6YdNBFVhserBvU93Xp6k83ViKAaByK7E0G4ahhQsXlkUWAICJ9hw/r2lfRSv2dKZ6NquhWTe0UpOavmbHAgCHUGJpbt68ufbt26cWLVoUb3N3d7drKABA2TmfU6jnNsZr5c4Tqu3nqVfGtteQ1rV5ZxEA/qTE0vzrr7/q22+/Lb7t5OSkLVu22DUUAMD+bDZDn+46oec2xisz36I7ewbpof4h8vXgFP4A8L9K/M24evXqssgBAChDMacyNC0qWr8dT1enxtU0JyJMobX9zI4FAA6rxNI8fvz4v7xFt2LFCrsFAgDYT2Z+kRZtOqgVPx1TNW93vTC6jUa0r8dSDAAoQYmledasWZL+OCAwJiZG8fHxdg8FAChdhmFo9b5TmrsuTmezCzSuSyP9d2Bz+Xu7mR0NAMqFEktzkyZNij8ODg7WF198YddAAIDSdTglS5FRMfop4Zza1PfXWxM66pr6Vc2OBQDlSoml+ZNPPin+OCUlRTk5OXYNBAAoHbmFFi3ZcljLf0iQj4er5kaE6ZbODeXizFIMALhaJZbm1NTU4o89PDz00ksv2TUQAODfMQxDX8cka87aWCWl52l0h/qaOjhU1X09zI4GAOVWiaX5gQce0HfffadDhw4pKChI9evXL4tcAIB/4Pi5XM1YHa2tB1IVWruKPrvnWnVqHGB2LAAo90oszS+88IISExPVvn17RUVFadeuXZo6dWpZZAMAXKH8Iqte35agV747LDdnJ00b2kITuzWWq4uz2dEAoEIosTTv3LlTK1eulCRNmDBBN910k91DAQCu3HcHUjRjdYwSz+Vq2DV1NG1oS9X29zQ7FgBUKCWWZovFIpvNJmdnZxmGwbk8AcBBnErP05y1sdoQfUZNavjog9u7qEezGmbHAoAKqcTSPGTIEN1yyy1q06aNfv/9dw0ZMqQscgEALqPIatPbPx7V4i2HZDMMPTaoue7oGSQPVxezowFAhVViaZ48ebJ69OihhIQEjRo1SiEhIWWRCwBwCT8nnFNkVLQOpWSrf4tAzQhvqQYB3mbHAoAK77JHiBQVFWnRokUqKChQSEiIPD09tXr1alkslrLMBwCQlJKVrymf7NWYN35WXpFVy2/rqOUTOlKYAaCMXLY0z58/X/n5+cVrmNu2bav8/Hw9++yzZRYOACo7q83QezuOqd8L27Tu99P6T9+m2jylt/q3DDQ7GgBUKpddnhETE3PR1QCrVq2qp59+WqNHjy6TYABQ2f12/LwiV0UrOilTPZrW0OzhrdSkpq/ZsQCgUrpsafbw+OuVo5ycnOTl5WXXQABQ2Z3PKdSCrw9o5c7jqlXFQy+Pbaehretw9iIAMNFlS3NAQID279+v1q1bF2/bv38/pRkA7MRmM/TZ7hN6dkO8MvMtur17kB4eECJfjxKP2QYA2NllfxNPnTpV9913n+rUqaMGDRro1KlTSkpK0uLFi8syHwBUCrGnMjUtar/2HE9Xp8bVNCciTKG1/cyOBQD4/y5bmmvXrq3PP/9cu3fvVkpKigYNGqS2bdvy9iAAlKKs/CIt2nxQ7+04pmre7lo4uo1Gtq/H71oAcDB/+56fs7OzOnXqVFZZAKDSMAxDq/ed0tx1cTqbXaBbuzTUYwND5e/tZnY0AMAlsFAOAMrY4ZQsRUbF6KeEc7qmvr+W39ZRbRpUNTsWAOBvUJoBoIzkFlq09NvDWv5DgrzcXDQnIkxjOzeUizNLMQDA0ZVYmnfu3Km8vDwZhqE5c+booYceUnh4eFlkA4AKwTAMbYpN1uw1sUpKz9PI9vX15JBQ1fD966k9AQCO6bJXBPw/zz//vBo3bqwVK1bo448/1sqVK8siFwBUCMfP5er293bp7vd3y9fDVZ/efa1euKkNhRkAypkSX2n28PBQ9erV5erqqpo1a6qwsLAscgFAuZZfZNVH+87r0+hjcnV20rShLTShW2O5uZT4WgUAwAGVWJp9fHw0adIkjR07Vh9++KHq1KlTFrkAoNzadjBVM1ZF69i5XA29po4ih7ZUbX9Ps2MBAP6FEkvzwoULdebMGTVt2lQHDx7UwIEDyyIXAJQ7pzPyNGdtrNbvP6OgGj6aN6C2bu3X3uxYAIBScNn3CVNTU3X06FFNnjxZrq6uOnr0qFxcXHTvvfeWZT4AcHhFVpve+P6I+r2wTVviUvTfgSHa+HBPta/rbXY0AEApuewrzfv27dN7772no0ePKjIyUtIfFzvp0aNHmYUDAEf3S8I5Ra6K1sHkbPVvUUszwlupQQBlGQAqmsuW5v79+6t///7atm2bevfuXZaZAMDhpWYVaP76OH35W5LqVfXSm7d11ICWgWbHAgDYSYlrmuvUqaOxY8cqKytL4eHhatasmfr06VMW2QDA4Vhthj76JVELvj6g/CKr7u8TrAf6NJOXu4vZ0QAAdlTiuY/mzZun+fPnq2rVqho1apSWLl1aFrkAwOHsPZGuiFe2K3JVjK6p76+ND/fSY4NCKcwAUAlc0WW0GzVqJCcnJwUEBMjHx8femQDAoaTnFuq5jQe0cudx1fT10NJb2mnYNXXk5MTlrwGgsiixNPv7+2vlypXKy8vTunXr5OfnVxa5AMB0Npuhz/ec1LMb4pWRV6TJ3YP0cP9mquLpZnY0AEAZK7E0P/PMM1q2bJmqVaum6OhozZs3ryxyAYCpYk9lKnJVtHYnnlfHRtU0JyJMLerwogEAVFYllmZfX19169ZNDRs21DXXXCMvL6+yyAUApsjKL9KLmw/pvZ+Oyd/LTc+PukYj29eXszNLMQCgMiuxNC9atEhnzpzRkSNH5ObmpjfeeEOLFi0qi2wAUGYMw9Ca309r7tpYpWYX6JbODfX4oOaq6u1udjQAgAMo8ewZu3fv1oIFC+Tt7a0bb7xRJ0+eLItcAFBmDqdk69blv+jBj39ToJ+nou7rrmdubE1hBgAUK/GVZqvVqoKCAjk5OclqtcrZucSeDQDlQl6hVUu/PaQ3f0iQp5uL5kSEaWznhnJhKQYA4H+UWJonTJigESNGKC0tTaNHj9akSZPKIhcA2NXm2GTNXB2jpPQ8jWhfT08ObqGaVTzMjgUAcFAlluYOHTroo48+UmJiourXr6+AgICyyAUAdnEiLVczV8doS3yKQgJ99cldXdWlSXWzYwEAHFyJpfnJJ59UYWGh+vTpo2rVqlGaAZRLBRar3tiWoJe3Hpars5OeHtJCE7s3lpsLS84AACUrsTS/9dZbys7O1vfff6/HHntM+fn5ioqKKotsAFAqvj+YqhmrY3T0bI6Gtq6jacNaqI4/p88EAFy5EkvzN998ox07dmjfvn2qW7euevToURa5AOBfO52Rp7lr47Ru/2kF1fDRismd1SukptmxAADlUImleeHChfLw8NBdd92lnj17chltAA6vyGrTu9uP6cVvDspqM/TogBDd1buJPFxdzI4GACinSizNGzdu1MmTJ/Xjjz/qgQceUH5+vj799NOyyAYAV+3Xo2mKjIrWgeQs9Q2tpZnhrdSwurfZsQAA5VyJpTkmJkbff/+9tm/fLk9PTw0ePLgscgHAVTmbXaBn1sfpyz1JqlfVS2+M76ABLQPl5MQ5lwEA/16JpfnVV1/VwIED9eqrr7I0A4DDsdoMffRLop7/+oDyiqy677pgPdC3qbzdS/z1BgDAFbvsX5WHH35YL730kl555ZWyzAMAV2zfiXRNi4rW/qQMdQuurtnDw9S0lq/ZsQAAFdBlS3NaWlpZ5gCAK5aeW6jnvz6gj349rpq+HlpySzuFX1OHpRgAALu5bGk+ceKEFi1adMn7HnnkEbsFAoDLsdkMfbHnpOZviFdGXpEmdQvSlAHNVMXTzexoAIAK7rKl2dPTU0FBQWWZBQAuK+50piKjorUr8bw6NKqmOcPD1LIux1kAAMrGZUtzjRo1dOONN5ZlFgD4i+wCi17cfFDv7jgmfy83LRh5jUZ1qC9nZ5ZiAADKzmVLc1hYWFnmAICLGIahtb+f1tx1sUrJKtAtnRvq8UHNVdXb3exoAIBK6LKl+YknnijLHABQ7EhqtmasitGPh88qrJ6fXh/fUW0bVDU7FgCgEuNEpgAcRl6hVa9sPazXvz8iTzcXzR7eSrd2aSQXlmIAAExGaQbgEDbHJmvm6hglpedpRLt6enJIC9Ws4mF2LAAAJFGaAZjsRFquZq2J0TdxKWpWy1cr7+qqrk2qmx0LAICLUJoBmKLAYtWb3ydo6beH5eLspKeGhGpS9yC5uTibHQ0AgL+gNAMocz8cStWMVTFKOJujIa1rK3JYS9Xx9zI7FgAAl0VpBlBmzmTka866WK37/bQaV/fWe5M7q3dITbNjAQBQIruU5i+//FJfffWVJKmgoEBxcXHavn27/Pz+uHrX3LlztWfPHvn4+EiSXn31VVWpUsUeUQA4gCKrTe/tOKYXNx+UxWbokQEhuqtXE3m6uZgdDQCAK2KX0jxixAiNGDFCkjRr1iyNHDmyuDBLUkxMjJYvX66AgAB7PD0AB7LzWJoio6IVfyZLfZrX1KwbwtSwurfZsQAAuCp2XZ6xf/9+HT58WDNmzCjeZrPZlJiYqOnTp+vs2bMaNWqURo0aZc8YAExwNrtAz26I1+e7T6peVS+9Pr6DBrYMlJMT51wGAJQ/di3Nr7/+uu6///6LtuXm5mrcuHGaNGmSrFarbrvtNoWFhSk0NNSeUQCUEavN0Ee/HtfzG+OVV2TVvdcF6z99m8rbnUMoAADll5NhGIY9dpyZmakxY8Zo/fr1F223Wq3Ky8uTr6+vJGnBggUKCQlRRETEZfe1d+9eeXg4xkUO8vPz5enpaXYM0zGHC5jFBdGnMvXGniwdOlegNrU9dV+XGmpY1d3sWKbhe+MCZnEBs2AGf8YsLnCkWbRo0eIv2+z20s/OnTvVrVu3v2w/duyYpkyZoq+++ko2m0179uzRjTfe+Lf78vDwuGR4M8TFxTlMFjMxhwuYhZSRW6TnN8Xrw5/PqkYVDy0e01Y3tKlb6Zdi8L1xAbO4gFkwgz9jFhc4yizi4uIuud1upfno0aOqX79+8e133nlHDRs2VL9+/RQeHq6bbrpJbm5uGj58uJo1a2avGADsyDAMfbEnSfPXx+l8bqFuaOGnOTd3lZ+nm9nRAAAoVXYrzXfcccdFtydNmlT88Z133qk777zTXk8NoAzEn8lUZFS0dh47r/YNq2rF7Z3lnHGKwgwAqJA4MgfAVckusOilzQf1zo5j8vN01XMjW2t0hwZydnZSXMYps+MBAGAXlGYAV8QwDK3bf1pz1sYqJatAYzo11OODmquaT+U90A8AUHlQmgGUKCE1WzNWx+iHQ2fVqq6flo3roHYNq5kdCwCAMkNpBnBZeYVWvfrdYb2+LUEebs6adUMrjevaSC7OlfusGACAyofSDOCSvolN1sw1MTp5Pk83tqunJ4eEqlYVxzh/JgAAZY3SDOAiJ9JyNWtNrL6JS1azWr76+M6uuja4utmxAAAwFaUZgCSpwGLV8h+Oaum3h+QkJz05OFSTewTJzcXZ7GgAAJiO0gxAPx46q+mropVwNkeDw2orclhL1a3qZXYsAAAcBqUZqMSSM/M1Z22s1v5+Wo2qe+vdSZ10XfNaZscCAMDhUJqBSshitendHcf00jeHVGi16eH+zXRP72B5urmYHQ0AAIdEaQYqmV3H0jQtKlrxZ7J0XfOamnVDKzWq7mN2LAAAHBqlGagkzmUXaP6GeH2++6Tq+ntq2bgOGtQqUE5OnHMZAICSUJqBCs5qM7Ry53Et2HhAOQUW3dM7WA/2aypvd378AQC4UvzVBCqw/SczNC1qv/adzFDXJgGaMzxMzQKrmB0LAIByh9IMVEAZuUVauOmAPvglUdV9PLR4TFvd0KYuSzEAAPiHKM1ABWIYhr7ck6Rn1sfpfG6hJlzbWI8MDJGfp5vZ0QAAKNcozUAFceBMliKjovXrsTS1a1hV703urLB6/mbHAgCgQqA0A+VcdoFFi785qLe3H1MVT1c9N7K1RndoIGdnlmIAAFBaKM1AOWUYhtbvP6M5a2N1JjNfYzo10OPXhyrAx93saAAAVDiUZqAcOno2R9NXReuHQ2fVso6fXh3XXu0bVjM7FgAAFRalGShH8ousenXrYS3bliAPV2fNDG+pcV0bydXF2exoAABUaJRmoJzYEpesmWtidCItTxFt6+qpoS1Uq4qn2bEAAKgUKM2Agzt5Plez1sRqc2yymtby1Ud3dlG34BpmxwIAoFKhNAMOqtBi05s/JGjpt4fkJCdNHRyqyd2D5O7KUgwAAMoapRlwQNsPn1XkqmglpObo+la1FRneUvWqepkdCwCASovSDDiQ5Mx8zV0XpzX7TqlRdW+9M6mT+jSvZXYsAAAqPUoz4AAsVpve+ylRL24+qEKrTQ/3b6Z7egfL083F7GgAAECUZsB0uxPT9PRX0Yo/k6XeITU164ZWalzDx+xYAADgTyjNgEnOZRfouY3x+nTXSdXx99Syce01qFVtOTlx+WsAABwNpRkoYzaboZU7T+i5jfHKKbDo7t5N9GDfZvLx4McRAABHxV9poAztP5mhaauite9EuroEBWhuRJiaBVYxOxYAACgBpRkoAxl5RXph0wF98HOiAnw89NLNbTW8bV2WYgAAUE5QmgE7MgxDX/2WpGfWxyktp1C3XdtYUwaEyN/LzexoAADgKlCaATs5cCZLkaui9evRNLVtUFXvTuqssHr+ZscCAAD/AKUZKGU5BRYt3nJIb/94VL6erpo/orVu7thAzs4sxQAAoLyiNAOlxDAMbYg+ozlrY3U6I183d2ygJwaHKsDH3exoAADgX6I0A6Xg6NkczVgdo+8PpqplHT+9PLa9OjSqZnYsAABQSijNwL+QX2TV+3vT9Hn0MXm4OmtGeEuN79pIri7OZkcDAACliNIM/ENb41M0fXW0TqTlaXjbunp6SAvV8vM0OxYAALADSjNwlU6ez9XsNbHaFJus4Jo+enZgHY3p287sWAAAwI4ozcAVKrTYtPzHBC3ZckhOctIT14fq9h5BOnLogNnRAACAnVGagSuw48hZRUZF60hqjga1CtT08FaqV9XL7FgAAKCMUJqBv5GSma956+O0au8pNQzw1jsTO6lPaC2zYwEAgDJGaQYuwWK1acVPiXpx80EVWGx6sF8z3XddsDzdXMyOBgAATEBpBv7H7sTzmhYVrbjTmeoVUlOzbmiloBo+ZscCAAAmojQD/19aTqGe2xCvT3adUG0/T712a3tdH1ZbTk5c/hoAgMqO0oxKz2YztHLnCS34Ol7Z+Rbd3auJHuzXTD4e/HgAAIA/0ApQqUUnZWhaVLT2nkhX56AAzY0IU0hgFbNjAQAAB0NpRqWUkVekRZsO6P2fExXg464Xb26jiLb1WIoBAAAuidKMSsUwDEXtTdK8dfFKyynQ+K6N9MjA5vL3cjM7GgAAcGCUZlQaB5OzFBkVrV+OpqlNg6p6d1InhdXzNzsWAAAoByjNqPByCixa8u0hvfXDUfl4uOqZG1trTKcGcnZmKQYAALgylGZUWIZhaGP0Gc1eG6vTGfm6qWN9PXF9qKr7epgdDQAAlDOUZlRIx87maMbqGG07mKrQ2lX08th26tAowOxYAACgnKI0o0LJL7Lqte+O6LVtR+Tu4qzpw1rqtmsbydXF2exoAACgHKM0o8LYeiBFM1bF6Hharm5oU1dPD22hQD9Ps2MBAIAKgNKMci8pPU+z18To65hkNanpo4/u6KJuTWuYHQsAAFQglGaUW4UWm9768aiWbDkkQ4YeG9Rcd/ZsIndXlmIAAIDSRWlGubTjyFlNXxWjwynZGtgyUNPDW6p+NW+zYwEAgAqK0oxyJSUzX/PWx2nV3lNqEOCltyd2VN/QQLNjAQCACo7SjHLBYrXp/Z8TtWjTQRVYbHqwb1Pd16epPP9fe3ceEGW973H8M6yyCKi4kKbhkoKGuZukpenRUo5W5lZ60jxp12653rwpioqZmcfqqOV2reN6PaaZmkunW1GZ5W4goIj7LousA8g8949OFKkNKMMD8n795Twz/J7v98sz+mH4OePqbHZpAACgAiA0o8zbfzpFkzdG68iFNHVs5K/pvZsp0N/L7LIAAEAFQmhGmZWSmavZ2+O0ds8Z1fKppIXPttTjzWrJYuHjrwEAQOkiNKPMsdkMrdt7Rm9uj1O69bpe7FRfrzzWSN7uXK4AAMAcpBCUKdHnril8U7QOnE5V2/uqakafZmpcq7LZZQEAgAqO0IwyIc2ap7/tPKp/fH9SVb3c9Ld+zfVki9psxQAAAGUCoRmmMgxDmw6eV+TWWCVl5mhw+3oa96fG8vVwNbs0AACAAoRmmObYpXSFb4rW7sRkNa/jq+XPt9EDdXzNLgsAAOAGhGaUuqzc63rviwQt/SZRXu4umvlkMw1oU1fOTmzFAAAAZROhGaXGMAztiLmo6ZuP6Pw1q55pVUcTH2+iat7uZpcGAADwhwjNKBWnkjI19dMYfRV/RU1qVdZ7A1uo9X1VzS4LAACgSAjNcChrXr4++Pq4Fn51XG7OTgrvFay/PFRPLs5OZpcGAABQZIRmOMyX8ZcV8WmMTiVlKaz5PZrcM0g1fSqZXRYAAECxEZpR4s6nZmv65iPaHnNR9at7adXwdgpt6G92WQAAALeN0IwSk3vdpv/57oTe++KYbIahCd0ba3jHQLm7OJtdGgAAwB0hNKNE7E5MUvgn0Tp2OUPdgmtqSq9g3VvV0+yyAAAASgShGXfkcrpVsz6L08YD51SnioeW/aW1HguqaXZZAAAAJYrQjNuSbzP00a6TentHvHKu2/SfXRrqPx5tKA83tmIAAIC7D6EZxbb/dIombD2n48m56tjIX9P+3FT1q3ubXRYAAIDDEJpRZCmZuXprR5zW/HhG1TyctWBQSz3xQC1ZLHz8NQAAuLsRmmGXzWbon/vO6M1tcUqzXtdfOwaqx72GWoUEmF0aAABAqSA04w/FnL+m8E+itf90hhIaKgAAFwpJREFUqtrcV0Uz+jRTk1o+io2NNbs0AACAUkNoxk2lWfP0t51H9Y/vT6qKp5vmPtNcT7WszVYMAABQIRGaUYhhGPr00HlFbo3V1YwcPdeunsb/qbF8PV3NLg0AAMA0DgnNGzZs0MaNGyVJOTk5io2N1XfffScfHx9J0rp167R27Vq5uLjopZdeUufOnR1RBoop4XK6wj+J0feJSWpex1fL/tJaIXX8zC4LAADAdA4JzU899ZSeeuopSdK0adP09NNPFwTmK1euaMWKFfr444+Vk5OjQYMGKTQ0VG5ubo4oBUWQlXtd732RoGXfJsrD1VmRfZppYNu6cnZiKwYAAIDk4O0ZP/30kxISEjR16tSCY4cPH1aLFi3k5uYmNzc31a1bV3FxcQoJCXFkKbgJwzC088glTd98ROdSs9W3VR1NfLyJ/L3dzS4NAACgTHFoaF60aJFGjRpV6FhGRoYqV65ccNvLy0sZGRmOLAM3cTopS1M/jdaX8VfUpFZl/XPkQ2pzX1WzywIAACiTHBaa09LSlJiYqPbt2xc67u3trczMzILbmZmZhUL0zfyyL7ossFqtZaaW25Gbb9M/o6/pfw+nysVJerF1VYUF+col+5JiYy8VeZ3yPoeSxCx+xSwKYx6/Yha/YhbM4LeYxa/K+iwcFpr37NmjDh063HA8JCRE77zzjnJycpSbm6vjx4/r/vvv/8O13N3dFRQU5KhSiyU2NrbM1FJcX8VfVsSWGJ1MylKvkABN7hmsWr6Vbmut8jyHksYsfsUsCmMev2IWv2IWzOC3mMWvysosbhXcHRaaT5w4oTp16hTcXr58uerWravHHntMgwcP1qBBg2QYhsaMGSN3d/bQOtL51GzN2HJE26Ivqr6/l1a+0E4PN/I3uywAAIByw2Ghefjw4YVuDx06tODP/fr1U79+/Rx1avxbXr5N//PtCb37xTHl2wxN6N5YwzsGyt3F2ezSAAAAyhU+3OQu9UNiksI3RevopQx1DaqhqWFNdW9VT7PLAgAAKJcIzXeZK+k5mvVZrDYcOKc6VTy0dEhrdQ2uaXZZAAAA5Rqh+S6RbzO0cvcpvb0zXta8fL3cuaFGdW4oDze2YgAAANwpQvNd4MDpFIVvilb0uTQ93NBf03o3VYPq3maXBQAAcNcgNJdjKZm5emtHvNbuOa0ald01f1AL9XwgQBYLH38NAABQkgjN5ZDNZmj9vrOatS1WadbreiE0UKO73S9vd76dAAAAjkDKKmeOnE9T+KZo7TuVotb1qijyyWZqUsvH7LIAAADuaoTmciLdmqe/fX5UH+06qSqebprTN0RPt6wjJye2YgAAADgaobmMMwxDnx46r5lbY3UlI0fPtqurCX9qIl9PV7NLAwAAqDAIzWVYwuUMTdkUrV3Hk/RAbV8tGdJaze/1M7ssAACACofQXAZl5V7X/P9L0JJvEuXh6qwZfZppUNu6cmYrBgAAgCkIzWWIYRj6/MglTdt8ROdSs/V0yzr67yeayN/b3ezSAAAAKjRCcxlxOilLEZtj9H9xl9W4ZmWtG/GQ2gZWNbssAAAAiNBsOmtevhZHJWrBlwlycbJocs8g/aXDfXJ1djK7NAAAAPwbodlEUUevaMqmaJ1MylLPkACF9wxWLd9KZpcFAACA3yE0m+DCtWzN2HJEn/10UYH+XlrxQlt1bFTd7LIAAABwC4TmUpSXb9Py707onX8dU77N0Lhu9+vFR+rL3cXZ7NIAAADwBwjNpeSHxCSFb4rW0UsZeqxJDUX8uanureppdlkAAAAoAkKzg11Jz9GsbbHasP+cavt5aMmQ1uoWXNPssgAAAFAMhGYHybcZWv3DKb21I17WvHyN6txAL3duJA83tmIAAACUN4RmBzh4JlXhn0Trp3PXFNqwmqb9uZka1vA2uywAAADcJkJzCUrNytVbO+K15sfTqu7trr8PbKFeIQGyWPj4awAAgPKM0FwCbDZD6/ef1Zvb4nQtO0/DQgM1umsjVa7kanZpAAAAKAGE5jsUeyFN4Z9Ea++pFLWuV0Uz+jRTUICP2WUBAACgBBGab1O6NU/v/OuYPtx1Ur4erprTN0RPt6wjJye2YgAAANxtCM3FZBiGPj10XpFbjuhKRo4Gtq2r/+reWH6ebmaXBgAAAAchNBfD8SsZev3zizp44YQeqO2rJUNaq/m9fmaXBQAAAAcjNBdBdm6+5n95TIujEuXmZNGM3k01qF09ObMVAwAAoEIgNNvx+ZFLivg0RudSs/VUy9rq29BZHVreZ3ZZAAAAKEWE5ls4k5yliE9j9EXcZd1f01v/+2J7tatfTbGxsWaXBgAAgFJGaP6dnOv5Wvx1ouZ/mSBnJ4smPRGk50Pvk6uzk9mlAQAAwCSE5t/45tgVTdkUoxNXM9XzgQBN7hWkAF8Ps8sCAACAyQjNki5es2rG1iPaeviC7qvmqX8Ma6tO91c3uywAAACUERU6NOfl2/TRrpOa9/lRXbcZGtvtfr3Yqb4quTqbXRoAAADKkAobmn88kazwT6IVfyldXZrUUERYU9Wt5ml2WQAAACiDKlxovpqRo1mfxenj/WdV289Diwe3UrfgmrJYeM9lAAAA3FyFCc35NkOrfzytOdvjlJ2Xr/94tIFe7tJQnm4VZgQAAAC4TRUiMR46k6rwTdE6fPaaOjSopum9m6lhDW+zywIAAEA5cVeH5mtZeXprR5xW/3ha1b3d9d7AFgoLCWArBgAAAIrlrgzNNpuhj/ef1Zvb4pSSlauhHQI1plsjVa7kanZpAAAAKIfuutAcdzFN4Z9Ea8/JFLWqV0UrerdT8D0+ZpcFAACAcuyuCc0ZOdc17/Oj+nDXSflUctFbT4eob6s6cnJiKwYAAADuTLkPzYZhaOtPFzRjyxFdTs/RgDZ19V/dG6uKl5vZpQEAAOAuUa5D8/ErGZq6KUbfJlxV03t89MFzrdSibhWzywIAAMBdplyG5uzcfC34MkGLoo6rkquzpvduqmfb1ZMzWzEAAADgAOUuNP/ryCVFbI7R2ZRsPdWitv77iSBVr+xudlkAAAC4i5Wb0HwmOUvTNsfoX7GX1aiGt9a+2F7t61czuywAAABUAOUiNF9Oz9GTq7+Wk8Wi159ooqGhgXJ1djK7LAAAAFQQ5SI0X7xmVZcmNRTeK1gBvh5mlwMAAIAKplyE5kB/T/XvFmJ2GQAAAKigysUeBz7+GgAAAGYqF6EZAAAAMBOhGQAAALCD0AwAAADYQWgGAAAA7CA0AwAAAHYQmgEAAAA7CM0AAACAHYRmAAAAwA5CMwAAAGAHoRkAAACwg9AMAAAA2EFoBgAAAOwgNAMAAAB2EJoBAAAAOwjNAAAAgB2EZgAAAMAOQjMAAABgB6EZAAAAsIPQDAAAANhBaAYAAADsIDQDAAAAdhCaAQAAADsIzQAAAIAdhGYAAADADkIzAAAAYAehGQAAALCD0AwAAADYYTEMwzC7CHsOHjwod3d3s8sAAADAXS4nJ0cPPvjgDcfLRWgGAAAAzMT2DAAAAMAOQjMAAABgB6EZAAAAsIPQDAAAANhBaAYAAADscDG7gNKSl5en119/XefOnVNubq5eeuklNWzYUBMnTpTFYlGjRo00depUOTn9/HPEqVOnNGrUKG3ZskWSlJWVpYiICJ09e1Z5eXkKDw9XSEhIoXMkJydr/PjxslqtqlGjhmbNmiUPD4+C+wYMGKDNmzeb+vZ5Zs5h1apV2rBhgywWi0aNGqXOnTuXev+/ZeYsIiMjtX//fnl5eUmSFi5cqMqVK5fuAH7DrFmcPHlSb7zxRsFjDh48qAULFqhTp06l1/xNmHltLF68WFu3bpW3t7eGDx9eIZ4nv/jwww919epVjR8/vuBYdna2hg4dqpkzZ6pBgwaOb/gP3OksZs6cqbi4OEnSlStX5OPjo3Xr1hU6x6lTp4q8nhnMnMG8efO0a9cuWSwWTZ48+ZbXUWkxcxYjR45UamqqXF1d5e7urqVLl5Zu879j1iy+/fZbLVmyRJJkGIb27dunLVu2OO7vCqOCWL9+vREZGWkYhmEkJycbjzzyiDFixAhj9+7dhmEYRnh4uLFz507DMAxj48aNxpNPPml06NCh4Ovfe+89Y/HixYZhGEZsbKyxcePGG84xY8YM4+OPPzYMwzAWLVpkLF++3DAMw4iKijJ69+5ttGjRwrBarQ7rsSjMmkNSUpLxxBNPGLm5uUZ6errRqVMnw2azObRXe8y8JgYMGGAkJSU5rLfiMnMWv/jss8+MsWPHlnhvt8OsecTFxRlhYWGG1Wo1rFar0adPHyMrK8uhvdpTGrPIzs42xo0bZ3Tr1s2YM2dOwfHDhw8XrJeQkOCwHovqTmfxi9zcXKNv375GXFzcDffdznqlyawZxMTEGEOGDDFsNptx5swZIywszFEtFpmZ18Pjjz9u+r+hv2XmLH6xZMkSY+7cuSXa1+9VmO0ZPXr00Kuvvlpw29nZWTExMWrbtq0kqVOnTtq1a5ckydfXVytXriz09d9++61cXV31wgsvaOHCherYseMN59i3b1/B8d+u5+TkpOXLl8vPz88hvRWHWXOoWrWqNm3aJFdXV129elU+Pj6yWCyOarNIzJqFzWbTqVOnNGXKFA0YMEDr1693VItFZubzQ/r51ci///3vmjRpUon3djvMmsfx48fVtm1bubu7y93dXfXq1VN8fLyj2iyS0phFTk6O+vTpo5EjRxY6npubqwULFqh+/fol3dZtudNZ/GLlypUKDQ1V48aNb7jvdtYrTWbNIDg4WMuWLZPFYtH58+fl7+9f0q0Vm1mzuHr1qtLS0jRy5EgNHDhQX375ZUm3VmxmPjck6eLFi9q0aZNefvnlEuvpZipMaPby8pK3t7cyMjL0yiuvaPTo0TIMoyC4eXl5KT09XZLUuXNneXp6Fvr6lJQUpaWladmyZerSpYtmz559wzkyMjIKfsX+2/VCQ0NVpUoVR7ZXZGbOwcXFRStXrlT//v3VvXt3R7ZZJGbNIisrS88995zmzJmjpUuXavXq1QW/ljKLmdeFJK1fv149evRQ1apVHdVisZg1j8aNG2vv3r3KyMhQSkqKDhw4oOzsbAd3+8dKYxa+vr56+OGHbzjeqlUrBQQEOKCr23Ons5B+/kFg7dq1euGFF256juKuV9rMnIGLi4vmzZunESNGqFevXo5or1jMmkVeXp6GDRumBQsWaP78+Zo1a5aSkpIc1GXRmHldSNLy5cv1/PPPy83NraRbK6TChGZJunDhgoYMGaLevXsrLCysYG+NJGVmZsrHx+eWX+vn56cuXbpI+vkbHh0drb1792rw4MEaPHiwvvrqK3l7eyszM7NI65nJzDk899xz+uabb7Rnzx7t3r3bQR0WnRmz8PDw0JAhQ+Th4SFvb2+1b9/e9NAsmXtdbN68Wc8884yDOrs9ZsyjQYMGevbZZ/XXv/5Vs2fPVvPmzcvED9yOnkV5ciezkKTvv/9ebdq0KfiBafv27QWziI6OLvZ6ZjBzBmPGjNE333yjZcuW6fTp0yXcWfGZMQt/f38NGDBALi4uqlatmoKCgnTixAnHNFgMZl0XNptNX331lXr27OmArgqrMKH56tWrGjZsmCZMmKC+fftKkoKDg/XDDz9IkqKiotS6detbfn2rVq309ddfS5L27Nmjhg0bqnXr1lqxYoVWrFihRx99VC1btix4TFRUlFq1auXgrorPrDkkJibq5ZdflmEYcnV1lZubW6EngBnMmsXJkyc1aNAg5efnKy8vT/v371fTpk0d3O0fM/P5kZ6ertzc3DL1iqJZ80hOTlZKSorWrFmjSZMm6cKFC2rUqJGDu/1jpTGL8uJOZyFJu3btKvQfXXv06FEwi2bNmhV7vdJm1gy+//57TZs2TZLk7u4uFxcX07f4mTWLXbt2afTo0ZJ+Do/Hjh0zfQuTmc+No0ePKjAwUJUqVXJEa4VYDMMwHH6WMiAyMlLbtm0rdGFNmjRJkZGRysvLU/369RUZGSlnZ+eC+0NDQ/Xdd99JklJTUzV58mRduXJFLi4umj17turUqVPoHFevXtVrr72mzMxMValSRXPnzi30K4guXbpo27Ztpr57hplzmD9/vqKiomSxWNSxY0eH7z2yx8xZLFmyRNu3b5erq6t69+6tgQMHlk7Tt2DmLA4fPqwPPvhACxcuLJ1mi8CseXh4eGjq1KmKiYmRq6urxo0bpzZt2pRO07dQGrP4xYYNG5SYmFjo3TMkafDgwYqIiDD93TPudBaS9OKLL2rMmDEKCgq66TlOnDih8PDwIq9X2syagSRNnz5d8fHxstls6tu3r/r16+egLovGzOth5syZOnTokJycnDR8+HB17drVcY0WgZmz2LZtm/bv318q/yemwoRmAAAA4HZVmO0ZAAAAwO0iNAMAAAB2EJoBAAAAOwjNAAAAgB2EZgAAAMAOQjMAlKBXXnlFixcvLridmZmp7t27m/4BNqGhoTccGzx4sB5//PFCx3bu3KnGjRvr7NmzxT7HLx+Nu2HDBr399tu3VygAlFGEZgAoQREREVqzZo0SEhIkSbNnz1b//v3VpEkTkyu7tdjY2II/b926VbVr176tdd5///2SKgkAyhwXswsAgLtJ1apVFR4ersmTJ2vs2LE6c+aMpk2bpvj4+IIPafDz89Mbb7whT09PTZkyRRcvXlRKSoo6deqk0aNHa+LEiUpNTVVqaqoWLVokX19fSdLFixcVERGhnJwcpaamatSoUeratavCwsLUtm1bxcfHy2KxaOHChfL09FR4eLgSEhJ07733Kjc396b19uzZU1u2bFFQUJDS0tKUk5Mjf39/SVJaWpomTJigjIwM5efn69VXX9VDDz100/OtXLlS165dU0REhEJCQnTo0CENGzZMycnJGjhwoPr371863wAAcBBeaQaAEtalSxcFBgZq4sSJevPNN2WxWBQeHq6pU6dqxYoV6tSpk5YuXaoLFy7owQcf1LJly7RmzRqtWbOmYI327dtr7dq1BYFZkhITEzV06FAtX75c4eHhWrVqlaSft4D07NlTK1euVI0aNRQVFaWoqCjl5ORo3bp1GjdunLKzs29Za1RUlAzD0I4dO9SjR4+C+95//3116NBBq1at0rvvvqtJkybJZrPd9HwvvfSSfH19FRERIUlycXHRsmXLNH/+fH300UcOmDIAlC5eaQYAB+jTp4+sVqtq1qwpSTp+/LimTZsmScrLy1NgYKD8/Pz0008/affu3fL29i70anBgYOANa1avXl3vv/++1q9fL4vFouvXrxfcFxwcLEkKCAhQTk6Ozp07p5CQEEnSPffco4CAgJvW6e7urqCgIB04cECff/655s2bp9WrVxfUHBYWJkmqWbOmvL29lZycfNPz/V5wcLAsFouqV68uq9VajMkBQNnEK80AUAoCAwM1e/ZsrVixQhMmTNAjjzyiDRs2qHLlypo7d66GDRsmq9UqwzAkSRaL5YY13n33XfXu3Vtz5sxRu3btCh57s8fXr19fBw8elCRdunRJly5dumVtvXr10ocffihfX195eXkVHG/QoIH27t1bsEZaWpr8/PxuWd8f1QMA5R2vNANAKYiIiNBrr72m/Px8SdLMmTPVoEEDjR07Vvv27ZOHh4fq1auny5cv33KNHj16aObMmVq0aJECAgKUkpJyy8d27dpV+/bt0zPPPKN77rlHVapUueVjQ0NDNXHiRM2aNavQ8REjRuj111/Xjh07ZLVaNX36dLm43PqfjQYNGmj8+PHq0KHDLR8DAOWVxfjtSwMAAAAAbsD2DAAAAMAOQjMAAABgB6EZAAAAsIPQDAAAANhBaAYAAADsIDQDAAAAdhCaAQAAADsIzQAAAIAd/w+IBPnC0bhIMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Line plot of Time vs Tweets.\n",
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.xlim([datetime.date(2015, 11, 30), datetime.date(2017, 8, 1)]) \n",
    "plt.xlabel('Year and Month')\n",
    "plt.ylabel('Tweets Count')\n",
    "plt.plot(df_tweets_clean.timestamp, df_tweets_clean.tweet_id)\n",
    "plt.title('Line Plot of Time vs Tweets');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that number of tweets have increased alot over time. There is linear relation here between time and tweets which shows that WeRateDogs became immediately popular and keep growing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Histograms of favorites and retweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "plt.xlim(0, 60000)\n",
    "plt.ylabel('Count')\n",
    "plt.hist(df_tweets_clean.favorite_count, alpha=.5, label='# Favorites')\n",
    "plt.hist(df_tweets_clean.retweet_count, alpha=.5, label='# Retweets')\n",
    "plt.title('Distribution of Favorites and Retweets Counts')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maximum tweets have received retweets upto 10k and favourites around 15k ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Popular Dog Breeds on WeRateDogs: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean1 = df_tweets_clean.groupby('Dog_Breed').filter(lambda x: len(x) >= 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['golden_retriever', 'Labrador_retriever', 'Pembroke', 'Chihuahua',\n",
       "       'pug', 'chow', 'Pomeranian', 'toy_poodle', 'Samoyed', 'malamute',\n",
       "       'cocker_spaniel', 'Chesapeake_Bay_retriever', 'miniature_pinscher',\n",
       "       'seat_belt', 'French_bulldog'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs = df_tweets_clean1['Dog_Breed'].value_counts()\n",
    "dogs.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "ax.pie(dogs, autopct='%1.1f%%')\n",
    "ax.legend(dogs.index,\n",
    "          title=\"Breeds of Dogs\",\n",
    "          loc=\"center left\",\n",
    "          bbox_to_anchor=(1, 0, 0.5, 1))\n",
    "\n",
    "# plt.setp(fig, size=8, weight=\"bold\")\n",
    "\n",
    "ax.set_title(\"Most popular Dogs in WeRateDogs: A pie\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation from the pie chart:\n",
    "* The most common dog on WeRateDOgs is Golden Retriever at 18.4%\n",
    "* Second common pet dog is Labrador retriever at 12.2 then Pembroke followed by Chihuahua and so on,.\n",
    "* Yet so, the varieties of breeds of dogs is huge as the most common breed is only at 18.4%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5)To find out if dog stage affects ratings received."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dog_stages = df_tweets_clean.Dog_Stage_y.value_counts()\n",
    "Dog_stages.drop(index = 'None', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Dog_Stage_y</th>\n",
       "      <th>rating_numerator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>doggo</td>\n",
       "      <td>11.797101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>doggo; floofer</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>doggo; pupper</td>\n",
       "      <td>11.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>doggo; puppo</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>floofer</td>\n",
       "      <td>11.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pupper</td>\n",
       "      <td>10.726441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>puppo</td>\n",
       "      <td>11.904762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Dog_Stage_y  rating_numerator\n",
       "1           doggo         11.797101\n",
       "2  doggo; floofer         11.000000\n",
       "3   doggo; pupper         11.111111\n",
       "4    doggo; puppo         13.000000\n",
       "5         floofer         11.750000\n",
       "6          pupper         10.726441\n",
       "7           puppo         11.904762"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stagerate = df_tweets_clean.groupby('Dog_Stage_y').mean().reset_index()\n",
    "stagerate = stagerate[['Dog_Stage_y', 'rating_numerator']]\n",
    "stagerate.drop(index = 0, inplace=True)\n",
    "stagerate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* It seems there is little to no correlation between the two, but it seems doggo and puppo together tend to get more ratings!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('dark')\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "ind = np.arange(len(stagerate))    \n",
    "width = 0.5                   \n",
    "\n",
    "p1 = plt.bar(ind, stagerate.rating_numerator, width)\n",
    "\n",
    "plt.xlabel('Dog Stages')\n",
    "plt.ylabel('Average Rating(s)')\n",
    "plt.title('Effect of Dog Stages on Rating(s)')\n",
    "plt.xticks(ind, stagerate.Dog_Stage_y)\n",
    "plt.yticks(np.arange(0, 1.6, .2));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can conclude that dog stage and ratings are not correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets_clean.to_csv('twitter_archive_master.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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