wrangle act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrangling and Analyzing Data\n",
    "# 1. Introduction\n",
    "Data preparation is always the hardest part of a data analyst's work flow, in this project, we will use the data wrangling skills to pull real-world data from Twitter, clean it, and do some analysis. We will get the original Twitter data from Twitter user @dog_rates, along with a image prediction dataset, to build our analysis.\n",
    "\n",
    "WeRateDogs is a popular Twitter hash tag, as the name tells, people rate dogs with a denominator of 10 and the numerator is usually higher than 10 to show how lovely the dog is.\n",
    "\n",
    "# 2. Gathering Data\n",
    "1-Twitter archive file: download this file manually by clicking the following link: twitter_archive_enhanced.csv\n",
    "\n",
    "2-The tweet image predictions, i.e., what breed of dog (or other object, animal, etc.) is present in each tweet according to a neural network. This file (image_predictions.tsv) is hosted on Udacity's servers and should be downloaded programmatically using the Requests library and the following URL: https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv\n",
    "\n",
    "3-Twitter API & JSON: Each tweet's retweet count and favorite (\"like\") count at minimum, and any additional data you find interesting. Using the tweet IDs in the WeRateDogs Twitter archive, query the Twitter API for each tweet's JSON data using Python's Tweepy library and store each tweet's entire set of JSON data in a file called tweet_json.txt file. Each tweet's JSON data should be written to its own line. Then read this .txt file line by line into a pandas DataFrame with (at minimum) tweet ID, retweet count, and favorite count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import re\n",
    "import os\n",
    "import tweepy\n",
    "import time\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from IPython.display import HTML, display\n",
    "from functools import reduce\n",
    "%matplotlib inline\n",
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the twitter_archive csv file\n",
    "df_twitter_archive = pd.read_csv('twitter-archive-enhanced.csv')\n",
    "df_twitter_archive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download the tsv file using the Requests library and the given URL\n",
    "url =' https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "response = requests.get(url)\n",
    "with open('image-predictions.tsv', 'wb') as file:\n",
    "        file.write(response.content)\n",
    "df_image_predictions = pd.read_csv('image-predictions.tsv', sep = '\\t')\n",
    "df_image_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the Twitter API with authentication pieces\n",
    "consumer_key = ' '\n",
    "consumer_secret =' '\n",
    "key =' '\n",
    "secret =' '\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(key, secret)\n",
    "api = tweepy.API(auth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "888202515573088257 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "873697596434513921 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "872668790621863937 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "872261713294495745 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "869988702071779329 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "866816280283807744 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "861769973181624320 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "856602993587888130 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "851953902622658560 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "845459076796616705 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "844704788403113984 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "844223788422217728 __ Failed to send request: ('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer'))\n",
      "842892208864923648 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "837366284874571778 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "837012587749474308 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "829374341691346946 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "827228250799742977 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "812747805718642688 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "802247111496568832 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "779123168116150273 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "775096608509886464 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "771004394259247104 __ [{'code': 179, 'message': 'Sorry, you are not authorized to see this status.'}]\n",
      "770743923962707968 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "759566828574212096 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Rate limit reached. Sleeping for: 113\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "754011816964026368 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "680055455951884288 __ [{'code': 144, 'message': 'No status found with that ID.'}]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Rate limit reached. Sleeping for: 422\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2107.7282769680023\n"
     ]
    }
   ],
   "source": [
    "df_list = []\n",
    "error_list = []\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "# Will add each available tweet json to df_list\n",
    "for tweet_id in df_twitter_archive['tweet_id']:\n",
    "    try:\n",
    "        tweet = api.get_status(tweet_id, tweet_mode='extended', wait_on_rate_limit = True,\n",
    "                               wait_on_rate_limit_notify = True)._json \n",
    "        favorites = tweet['favorite_count'] # tweet's favorites\n",
    "        retweets = tweet['retweet_count'] \n",
    "        user_followers = tweet['user']['followers_count'] \n",
    "        user_favourites = tweet['user']['favourites_count'] # user's favorites\n",
    "        date_time = tweet['created_at'] # The date and time of the creation\n",
    "             \n",
    "        df_list.append({'tweet_id': int(tweet_id), 'favorites': int(favorites), 'retweets': int(retweets),\n",
    "                        'user_followers': int(user_followers), 'user_favourites': int(user_favourites),\n",
    "                        'date_time': pd.to_datetime(date_time)})\n",
    "\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(str(tweet_id)+ \" __ \" + str(e)) \n",
    "        error_list.append(tweet_id)\n",
    "        \n",
    "\n",
    "end = time.time()\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lengh of success the result 2330\n",
      "The lengh of error the result 26\n"
     ]
    }
   ],
   "source": [
    "print(\"The lengh of success the result\", len(df_list))\n",
    "print(\"The lengh of error the result\", len(error_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets = pd.DataFrame(df_list, columns = ['tweet_id', 'favorites', 'retweets', 'user_followers',\n",
    "                                               'user_favourites', 'date_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The lengh of success the result 2330\n"
     ]
    }
   ],
   "source": [
    "print(\"The lengh of success the result\", len(df_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweets.to_csv('df_tweets.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Unnamed: 0</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweets</th>\n",
       "      <th>user_followers</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>date_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>35385</td>\n",
       "      <td>7470</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>30622</td>\n",
       "      <td>5547</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>23040</td>\n",
       "      <td>3671</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>38638</td>\n",
       "      <td>7637</td>\n",
       "      <td>8899220</td>\n",
       "      <td>145977</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>36955</td>\n",
       "      <td>8246</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0            tweet_id  favorites  retweets  user_followers  \\\n",
       "0           0  892420643555336193      35385      7470         8898582   \n",
       "1           1  892177421306343426      30622      5547         8898582   \n",
       "2           2  891815181378084864      23040      3671         8898582   \n",
       "3           3  891689557279858688      38638      7637         8899220   \n",
       "4           4  891327558926688256      36955      8246         8898582   \n",
       "\n",
       "   user_favourites                  date_time  \n",
       "0           145976  2017-08-01 16:23:56+00:00  \n",
       "1           145976  2017-08-01 00:17:27+00:00  \n",
       "2           145976  2017-07-31 00:18:03+00:00  \n",
       "3           145977  2017-07-30 15:58:51+00:00  \n",
       "4           145976  2017-07-29 16:00:24+00:00  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets = pd.read_csv('df_tweets.csv')\n",
    "df_tweets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assessing Data\n",
    "In this section the data sources are going to be assessed one by one. \n",
    "The first part of each data source outlines the process of assessing and shows my approach.\n",
    "The second part states observations and issues I have found be fixed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                         0\n",
       "in_reply_to_status_id         2278\n",
       "in_reply_to_user_id           2278\n",
       "timestamp                        0\n",
       "source                           0\n",
       "text                             0\n",
       "retweeted_status_id           2175\n",
       "retweeted_status_user_id      2175\n",
       "retweeted_status_timestamp    2175\n",
       "expanded_urls                   59\n",
       "rating_numerator                 0\n",
       "rating_denominator               0\n",
       "name                             0\n",
       "doggo                            0\n",
       "floofer                          0\n",
       "pupper                           0\n",
       "puppo                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_twitter_archive.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                      2356\n",
       "in_reply_to_status_id           77\n",
       "in_reply_to_user_id             31\n",
       "timestamp                     2356\n",
       "source                           4\n",
       "text                          2356\n",
       "retweeted_status_id            181\n",
       "retweeted_status_user_id        25\n",
       "retweeted_status_timestamp     181\n",
       "expanded_urls                 2218\n",
       "rating_numerator                40\n",
       "rating_denominator              18\n",
       "name                           957\n",
       "doggo                            2\n",
       "floofer                          2\n",
       "pupper                           2\n",
       "puppo                            2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2356\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.duplicated(subset=['tweet_id']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['None' 'doggo']\n",
      "['None' 'floofer']\n",
      "['None' 'pupper']\n",
      "['None' 'puppo']\n"
     ]
    }
   ],
   "source": [
    "print(df_twitter_archive['doggo'].unique())\n",
    "print(df_twitter_archive['floofer'].unique())\n",
    "print(df_twitter_archive['pupper'].unique())\n",
    "print(df_twitter_archive['puppo'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>puppo</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>floofer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>puppo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>doggo</td>\n",
       "      <td>floofer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   doggo  floofer  pupper  puppo  tweet_id\n",
       "0   None     None    None   None      1976\n",
       "2   None     None  pupper   None       245\n",
       "4  doggo     None    None   None        83\n",
       "1   None     None    None  puppo        29\n",
       "6  doggo     None  pupper   None        12\n",
       "3   None  floofer    None   None         9\n",
       "5  doggo     None    None  puppo         1\n",
       "7  doggo  floofer    None   None         1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.groupby([\"doggo\",\"floofer\",\"pupper\",\"puppo\"])[\"tweet_id\"].count().to_frame().reset_index().sort_values(by='tweet_id', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  13,   12,   14,    5,   17,   11,   10,  420,  666,    6,   15,\n",
       "        182,  960,    0,   75,    7,   84,    9,   24,    8,    1,   27,\n",
       "          3,    4,  165, 1776,  204,   50,   99,   80,   45,   60,   44,\n",
       "        143,  121,   20,   26,    2,  144,   88])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.rating_numerator.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10,   0,  15,  70,   7,  11, 150, 170,  20,  50,  90,  80,  40,\n",
       "       130, 110,  16, 120,   2])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.rating_denominator.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1776</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>666</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>420</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>182</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>143</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rating_numerator  rating_denominator  tweet_id\n",
       "44              1776                  10         1\n",
       "43               960                   0         1\n",
       "42               666                  10         1\n",
       "41               420                  10         2\n",
       "40               204                 170         1\n",
       "39               182                  10         1\n",
       "38               165                 150         1\n",
       "37               144                 120         1\n",
       "36               143                 130         1\n",
       "35               121                 110         1\n",
       "34                99                  90         1\n",
       "33                88                  80         1\n",
       "32                84                  70         1\n",
       "31                80                  80         1\n",
       "30                75                  10         2\n",
       "29                60                  50         1\n",
       "28                50                  50         1\n",
       "27                45                  50         1\n",
       "26                44                  40         1\n",
       "25                27                  10         1\n",
       "24                26                  10         1\n",
       "23                24                   7         1\n",
       "22                20                  16         1\n",
       "21                17                  10         1\n",
       "20                15                  10         2\n",
       "19                14                  10        54\n",
       "18                13                  10       351\n",
       "17                12                  10       558\n",
       "16                11                  15         1\n",
       "15                11                  10       463\n",
       "14                10                  10       461\n",
       "12                 9                  10       156\n",
       "13                 9                  11         2\n",
       "11                 8                  10       102\n",
       "10                 7                  11         1\n",
       "9                  7                  10        54\n",
       "8                  6                  10        32\n",
       "7                  5                  10        37\n",
       "6                  4                  20         2\n",
       "5                  4                  10        15\n",
       "4                  3                  10        19\n",
       "3                  2                  10         9\n",
       "2                  1                  10         8\n",
       "1                  1                   2         1\n",
       "0                  0                  10         2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.groupby([\"rating_numerator\",\"rating_denominator\"])[\"tweet_id\"].count().to_frame().reset_index().sort_values(by='rating_numerator', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>',\n",
       "       '<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>',\n",
       "       '<a href=\"http://vine.co\" rel=\"nofollow\">Vine - Make a Scene</a>',\n",
       "       '<a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.source.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Phineas\n",
       "1          Tilly\n",
       "2         Archie\n",
       "3          Darla\n",
       "4       Franklin\n",
       "          ...   \n",
       "2351        None\n",
       "2352           a\n",
       "2353           a\n",
       "2354           a\n",
       "2355        None\n",
       "Name: name, Length: 2356, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None              745\n",
       "a                  55\n",
       "Charlie            12\n",
       "Oliver             11\n",
       "Cooper             11\n",
       "                 ... \n",
       "Michelangelope      1\n",
       "Karma               1\n",
       "Rose                1\n",
       "Saydee              1\n",
       "Tito                1\n",
       "Name: name, Length: 957, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.name.value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### There are some values in this column that looks not like a real name: a, an, the, very, and so on. They are all in lower case, so we may check the abnormality by this feature.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a               55\n",
       "the              8\n",
       "an               7\n",
       "very             5\n",
       "one              4\n",
       "just             4\n",
       "quite            4\n",
       "mad              2\n",
       "actually         2\n",
       "getting          2\n",
       "not              2\n",
       "by               1\n",
       "space            1\n",
       "my               1\n",
       "incredibly       1\n",
       "infuriating      1\n",
       "such             1\n",
       "light            1\n",
       "old              1\n",
       "his              1\n",
       "this             1\n",
       "all              1\n",
       "unacceptable     1\n",
       "life             1\n",
       "officially       1\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.loc[(df_twitter_archive.name.str.islower())].name.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([55,  8,  7,  5,  4,  2,  1])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.loc[(df_twitter_archive.name.str.islower())].name.value_counts().unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### The list above proves the hypothesis: lower case strings are not real names of dog.\n",
    "\n",
    "##### name: this column has some missing values and some of the names are not real dog names but articles or adjectives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_image_predictions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id    0\n",
       "jpg_url     0\n",
       "img_num     0\n",
       "p1          0\n",
       "p1_conf     0\n",
       "p1_dog      0\n",
       "p2          0\n",
       "p2_conf     0\n",
       "p2_dog      0\n",
       "p3          0\n",
       "p3_conf     0\n",
       "p3_dog      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_image_predictions.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_image_predictions.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_image_predictions.jpg_url.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2330 entries, 0 to 2329\n",
      "Data columns (total 7 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Unnamed: 0       2330 non-null   int64 \n",
      " 1   tweet_id         2330 non-null   int64 \n",
      " 2   favorites        2330 non-null   int64 \n",
      " 3   retweets         2330 non-null   int64 \n",
      " 4   user_followers   2330 non-null   int64 \n",
      " 5   user_favourites  2330 non-null   int64 \n",
      " 6   date_time        2330 non-null   object\n",
      "dtypes: int64(6), object(1)\n",
      "memory usage: 127.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_tweets.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0         0\n",
       "tweet_id           0\n",
       "favorites          0\n",
       "retweets           0\n",
       "user_followers     0\n",
       "user_favourites    0\n",
       "date_time          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quality Issues:\n",
    "It mainly include issues like completeness, validity, accuracyand consistency\n",
    "\n",
    "### df_twitter_archive:\n",
    "    \n",
    "1-remove tweets that has been replay as its not original.\n",
    "\n",
    "2-remove tweets that has been rtweet as its not original.\n",
    "\n",
    "3-When we back to twetter accaount\"@dog_rates\", there are ratings that are incorrect. I ordered the ratings from low to high and looked at the extremes only for incorrect ratings therefore there are likely more than I missed and will be difficult to find them all programmatically. Examples where things may have gone wrong is the use of decimals, or when two instances of numbers separated by a slash are present in 1 text and I assume the first was chosen. Also, there are ratings with decimals such as 13.5/10, 9.5/10 have been incorrectly extracted as 5/10 (in addition to other numbers with decimals such as 11.26 and 11.27). There are instances of 1/2 and 50/50 which are not ratings such signifying \"half\" which have been considered as ratings. Finally, use of 4/20 and 24/7 has been confused as ratings.\n",
    "For future analysis it could be confusing to interpret unstandardized ratings. It is their gimmick to give dogs a rating of 100% but not all are above 100% so it could be interesting to see what % are below or above 100% and how this changed overtime by calculating a single value for rating.\n",
    "There are many columns in this dataframe making it hard to read, and some will not be needed for analysis.\n",
    "\n",
    "4-Correct numerator bigger than 14. \n",
    "\n",
    "5-Correct denominators not equal 10.\n",
    "\n",
    "6- Combine rating_numerator and rating_denominator columns into one column.\n",
    "\n",
    "7-replace the url from value of source .\n",
    "\n",
    "8-The name column has many invalid values like , a, an, the.\n",
    "\n",
    "### df_image_predictions:\n",
    "1- drop duplicate jpg_url.\n",
    "\n",
    "2-p1,p2 and p3 have inconsisitent capital words.\n",
    "\n",
    "3-p1, p2, and p3 contain underscores instead of spaces in the labels\n",
    "\n",
    "    \n",
    "    \n",
    "# Tidiness:   \n",
    "Tidy data is a standard way of mapping the meaning of a dataset to its structure.\n",
    "A dataset is messy or tidy depending on how rows, columns and tables are matched up with observations, variables and types.\n",
    "In tidy data:\n",
    "\n",
    "1-Merge three data frames.\n",
    "\n",
    "2-There are multiple dog stages columns present e.g. doggo, pupper, etc. They should be merged into 1 column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "##### Merge the df_tweet and df_image_predictions tables to the df_twitter_archive table, both joining on tweet_id.\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>...</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweets</th>\n",
       "      <th>user_followers</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>date_time</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>...</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>35385</td>\n",
       "      <td>7470</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</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>...</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>30622</td>\n",
       "      <td>5547</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</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>...</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>23040</td>\n",
       "      <td>3671</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</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>...</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>38638</td>\n",
       "      <td>7637</td>\n",
       "      <td>8899220</td>\n",
       "      <td>145977</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</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>...</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>36955</td>\n",
       "      <td>8246</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\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  ...  p2_dog  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...  ...   False   \n",
       "1  https://twitter.com/dog_rates/status/892177421...  ...    True   \n",
       "2  https://twitter.com/dog_rates/status/891815181...  ...    True   \n",
       "3  https://twitter.com/dog_rates/status/891689557...  ...    True   \n",
       "4  https://twitter.com/dog_rates/status/891327558...  ...    True   \n",
       "\n",
       "                            p3   p3_conf p3_dog Unnamed: 0 favorites retweets  \\\n",
       "0                       banana  0.076110  False          0     35385     7470   \n",
       "1                     papillon  0.068957   True          1     30622     5547   \n",
       "2                       kelpie  0.031379   True          2     23040     3671   \n",
       "3                      spatula  0.040836  False          3     38638     7637   \n",
       "4  German_short-haired_pointer  0.175219   True          4     36955     8246   \n",
       "\n",
       "  user_followers  user_favourites                  date_time  \n",
       "0        8898582           145976  2017-08-01 16:23:56+00:00  \n",
       "1        8898582           145976  2017-08-01 00:17:27+00:00  \n",
       "2        8898582           145976  2017-07-31 00:18:03+00:00  \n",
       "3        8899220           145977  2017-07-30 15:58:51+00:00  \n",
       "4        8898582           145976  2017-07-29 16:00:24+00:00  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merge the df_twitter_archive, df_image_predictions, and df_tweets on 'tweet_id'\n",
    "dfs = [df_twitter_archive, df_image_predictions, df_tweets]\n",
    "twitter = reduce(lambda left,right: pd.merge(left,right,on='tweet_id'), dfs)\n",
    "twitter.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>...</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweets</th>\n",
       "      <th>user_followers</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>date_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1725</th>\n",
       "      <td>672139350159835138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-02 19:44:43 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This pup has a heart on its ass and that is do...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/672139350...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>chimpanzee</td>\n",
       "      <td>0.115541</td>\n",
       "      <td>False</td>\n",
       "      <td>1992</td>\n",
       "      <td>1638</td>\n",
       "      <td>647</td>\n",
       "      <td>8898576</td>\n",
       "      <td>145976</td>\n",
       "      <td>2015-12-02 19:44:43+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>733828123016450049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-05-21 01:13:53 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Terry. The harder you hug him the fart...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/733828123...</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>Saint_Bernard</td>\n",
       "      <td>0.114640</td>\n",
       "      <td>True</td>\n",
       "      <td>1084</td>\n",
       "      <td>3479</td>\n",
       "      <td>752</td>\n",
       "      <td>8898569</td>\n",
       "      <td>145976</td>\n",
       "      <td>2016-05-21 01:13:53+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>686618349602762752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-11 18:39:05 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Olivia. She just saw an adult wearing ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/686618349...</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>Gordon_setter</td>\n",
       "      <td>0.093582</td>\n",
       "      <td>True</td>\n",
       "      <td>1565</td>\n",
       "      <td>3603</td>\n",
       "      <td>1318</td>\n",
       "      <td>8898574</td>\n",
       "      <td>145976</td>\n",
       "      <td>2016-01-11 18:39:05+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>818627210458333184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-10 01:15:10 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Wafer. He represents every fiber of my be...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/818627210...</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>grocery_store</td>\n",
       "      <td>0.079799</td>\n",
       "      <td>False</td>\n",
       "      <td>434</td>\n",
       "      <td>22237</td>\n",
       "      <td>7458</td>\n",
       "      <td>8898565</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-01-10 01:15:10+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>692828166163931137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-28 21:54:41 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This pupper just descended from heaven. 12/10 ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/692828166...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>white_wolf</td>\n",
       "      <td>0.003278</td>\n",
       "      <td>False</td>\n",
       "      <td>1467</td>\n",
       "      <td>2796</td>\n",
       "      <td>862</td>\n",
       "      <td>8898574</td>\n",
       "      <td>145976</td>\n",
       "      <td>2016-01-28 21:54:41+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "1725  672139350159835138                    NaN                  NaN   \n",
       "899   733828123016450049                    NaN                  NaN   \n",
       "1328  686618349602762752                    NaN                  NaN   \n",
       "349   818627210458333184                    NaN                  NaN   \n",
       "1242  692828166163931137                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "1725  2015-12-02 19:44:43 +0000   \n",
       "899   2016-05-21 01:13:53 +0000   \n",
       "1328  2016-01-11 18:39:05 +0000   \n",
       "349   2017-01-10 01:15:10 +0000   \n",
       "1242  2016-01-28 21:54:41 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "1725  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "899   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1328  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "349   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1242  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "1725  This pup has a heart on its ass and that is do...                  NaN   \n",
       "899   This is Terry. The harder you hug him the fart...                  NaN   \n",
       "1328  This is Olivia. She just saw an adult wearing ...                  NaN   \n",
       "349   Meet Wafer. He represents every fiber of my be...                  NaN   \n",
       "1242  This pupper just descended from heaven. 12/10 ...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "1725                       NaN                        NaN   \n",
       "899                        NaN                        NaN   \n",
       "1328                       NaN                        NaN   \n",
       "349                        NaN                        NaN   \n",
       "1242                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  ...  p2_dog  \\\n",
       "1725  https://twitter.com/dog_rates/status/672139350...  ...   False   \n",
       "899   https://twitter.com/dog_rates/status/733828123...  ...    True   \n",
       "1328  https://twitter.com/dog_rates/status/686618349...  ...    True   \n",
       "349   https://twitter.com/dog_rates/status/818627210...  ...    True   \n",
       "1242  https://twitter.com/dog_rates/status/692828166...  ...   False   \n",
       "\n",
       "                 p3   p3_conf p3_dog Unnamed: 0 favorites retweets  \\\n",
       "1725     chimpanzee  0.115541  False       1992      1638      647   \n",
       "899   Saint_Bernard  0.114640   True       1084      3479      752   \n",
       "1328  Gordon_setter  0.093582   True       1565      3603     1318   \n",
       "349   grocery_store  0.079799  False        434     22237     7458   \n",
       "1242     white_wolf  0.003278  False       1467      2796      862   \n",
       "\n",
       "     user_followers  user_favourites                  date_time  \n",
       "1725        8898576           145976  2015-12-02 19:44:43+00:00  \n",
       "899         8898569           145976  2016-05-21 01:13:53+00:00  \n",
       "1328        8898574           145976  2016-01-11 18:39:05+00:00  \n",
       "349         8898565           145976  2017-01-10 01:15:10+00:00  \n",
       "1242        8898574           145976  2016-01-28 21:54:41+00:00  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    2, ..., 2327, 2328, 2329])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter['Unnamed: 0'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### After we creat the mearge for the three data frames appear index column(Unnamed: 0), we don't need it ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter.drop('Unnamed: 0',\n",
    "  axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2058 entries, 0 to 2057\n",
      "Data columns (total 33 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2058 non-null   int64  \n",
      " 1   in_reply_to_status_id       23 non-null     float64\n",
      " 2   in_reply_to_user_id         23 non-null     float64\n",
      " 3   timestamp                   2058 non-null   object \n",
      " 4   source                      2058 non-null   object \n",
      " 5   text                        2058 non-null   object \n",
      " 6   retweeted_status_id         72 non-null     float64\n",
      " 7   retweeted_status_user_id    72 non-null     float64\n",
      " 8   retweeted_status_timestamp  72 non-null     object \n",
      " 9   expanded_urls               2058 non-null   object \n",
      " 10  rating_numerator            2058 non-null   int64  \n",
      " 11  rating_denominator          2058 non-null   int64  \n",
      " 12  name                        2058 non-null   object \n",
      " 13  doggo                       2058 non-null   object \n",
      " 14  floofer                     2058 non-null   object \n",
      " 15  pupper                      2058 non-null   object \n",
      " 16  puppo                       2058 non-null   object \n",
      " 17  jpg_url                     2058 non-null   object \n",
      " 18  img_num                     2058 non-null   int64  \n",
      " 19  p1                          2058 non-null   object \n",
      " 20  p1_conf                     2058 non-null   float64\n",
      " 21  p1_dog                      2058 non-null   bool   \n",
      " 22  p2                          2058 non-null   object \n",
      " 23  p2_conf                     2058 non-null   float64\n",
      " 24  p2_dog                      2058 non-null   bool   \n",
      " 25  p3                          2058 non-null   object \n",
      " 26  p3_conf                     2058 non-null   float64\n",
      " 27  p3_dog                      2058 non-null   bool   \n",
      " 28  favorites                   2058 non-null   int64  \n",
      " 29  retweets                    2058 non-null   int64  \n",
      " 30  user_followers              2058 non-null   int64  \n",
      " 31  user_favourites             2058 non-null   int64  \n",
      " 32  date_time                   2058 non-null   object \n",
      "dtypes: bool(3), float64(7), int64(8), object(15)\n",
      "memory usage: 504.5+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(twitter.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                      2058\n",
       "in_reply_to_status_id           22\n",
       "in_reply_to_user_id              1\n",
       "timestamp                     2058\n",
       "source                           3\n",
       "text                          2058\n",
       "retweeted_status_id             72\n",
       "retweeted_status_user_id        13\n",
       "retweeted_status_timestamp      72\n",
       "expanded_urls                 2058\n",
       "rating_numerator                35\n",
       "rating_denominator              15\n",
       "name                           933\n",
       "doggo                            2\n",
       "floofer                          2\n",
       "pupper                           2\n",
       "puppo                            2\n",
       "jpg_url                       1999\n",
       "img_num                          4\n",
       "p1                             378\n",
       "p1_conf                       1996\n",
       "p1_dog                           2\n",
       "p2                             403\n",
       "p2_conf                       1994\n",
       "p2_dog                           2\n",
       "p3                             407\n",
       "p3_conf                       1996\n",
       "p3_dog                           2\n",
       "favorites                     1821\n",
       "retweets                      1571\n",
       "user_followers                  28\n",
       "user_favourites                  2\n",
       "date_time                     2058\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                         0\n",
       "in_reply_to_status_id         2035\n",
       "in_reply_to_user_id           2035\n",
       "timestamp                        0\n",
       "source                           0\n",
       "text                             0\n",
       "retweeted_status_id           1986\n",
       "retweeted_status_user_id      1986\n",
       "retweeted_status_timestamp    1986\n",
       "expanded_urls                    0\n",
       "rating_numerator                 0\n",
       "rating_denominator               0\n",
       "name                             0\n",
       "doggo                            0\n",
       "floofer                          0\n",
       "pupper                           0\n",
       "puppo                            0\n",
       "jpg_url                          0\n",
       "img_num                          0\n",
       "p1                               0\n",
       "p1_conf                          0\n",
       "p1_dog                           0\n",
       "p2                               0\n",
       "p2_conf                          0\n",
       "p2_dog                           0\n",
       "p3                               0\n",
       "p3_conf                          0\n",
       "p3_dog                           0\n",
       "favorites                        0\n",
       "retweets                         0\n",
       "user_followers                   0\n",
       "user_favourites                  0\n",
       "date_time                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are multiple dog stages columns present e.g. doggo, pupper, etc. They should be merged into 1 column\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "#### Create a new column named dog_stage. Extract doggo, floofer, pupper and puppo from the text column using pandas' str.extact method and place them into the dog_stage column. Fill the null values with None.\n",
    "\n",
    "### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter['dog_stage'] = twitter['text'].str.extract('(doggo|floof|pupper|puppo)',expand=True)\n",
    "twitter = twitter.drop(['doggo','floofer','pupper','puppo'], axis=1)\n",
    "twitter['dog_stage'].fillna('None', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>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>...</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweets</th>\n",
       "      <th>user_followers</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>date_time</th>\n",
       "      <th>dog_stage</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>...</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.07611</td>\n",
       "      <td>False</td>\n",
       "      <td>35385</td>\n",
       "      <td>7470</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <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",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  ...  p2_dog      p3  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...  ...   False  banana   \n",
       "\n",
       "   p3_conf p3_dog  favorites retweets  user_followers  user_favourites  \\\n",
       "0  0.07611  False      35385     7470         8898582           145976   \n",
       "\n",
       "                   date_time  dog_stage  \n",
       "0  2017-08-01 16:23:56+00:00       None  \n",
       "\n",
       "[1 rows x 30 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None      1705\n",
       "pupper     230\n",
       "doggo       73\n",
       "puppo       28\n",
       "floof       22\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### remove tweets that has been replay as its not original.\n",
    "\n",
    "### remove tweets that has been rtweet as its not original.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "#### Drop the the columns related to the retweets and the replies: retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, in_reply_to_status_id, in_reply_to_user_id.\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#step 1 : remove rows of reply and retweet tweets\n",
    "twitter=twitter[twitter['in_reply_to_user_id'].isnull()]\n",
    "twitter=twitter[twitter['in_reply_to_status_id'].isnull()]\n",
    "twitter=twitter[twitter['retweeted_status_id'].isnull()]\n",
    "twitter=twitter[twitter['retweeted_status_user_id'].isnull()]\n",
    "twitter=twitter[twitter['retweeted_status_timestamp'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#step 2: drop 'retweeted_status_id', 'retweeted_status_user_id' and \n",
    "# 'retweeted_status_timestamp' columns\n",
    "twitter = twitter.drop(['retweeted_status_id','retweeted_status_user_id',\n",
    "                        'retweeted_status_timestamp'], axis=1)\n",
    "#Remove in_reply_to_status_id,in_reply_to_user_id\n",
    "twitter = twitter.drop(['in_reply_to_status_id','in_reply_to_user_id'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1963 entries, 0 to 2057\n",
      "Data columns (total 25 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   tweet_id            1963 non-null   int64  \n",
      " 1   timestamp           1963 non-null   object \n",
      " 2   source              1963 non-null   object \n",
      " 3   text                1963 non-null   object \n",
      " 4   expanded_urls       1963 non-null   object \n",
      " 5   rating_numerator    1963 non-null   int64  \n",
      " 6   rating_denominator  1963 non-null   int64  \n",
      " 7   name                1963 non-null   object \n",
      " 8   jpg_url             1963 non-null   object \n",
      " 9   img_num             1963 non-null   int64  \n",
      " 10  p1                  1963 non-null   object \n",
      " 11  p1_conf             1963 non-null   float64\n",
      " 12  p1_dog              1963 non-null   bool   \n",
      " 13  p2                  1963 non-null   object \n",
      " 14  p2_conf             1963 non-null   float64\n",
      " 15  p2_dog              1963 non-null   bool   \n",
      " 16  p3                  1963 non-null   object \n",
      " 17  p3_conf             1963 non-null   float64\n",
      " 18  p3_dog              1963 non-null   bool   \n",
      " 19  favorites           1963 non-null   int64  \n",
      " 20  retweets            1963 non-null   int64  \n",
      " 21  user_followers      1963 non-null   int64  \n",
      " 22  user_favourites     1963 non-null   int64  \n",
      " 23  date_time           1963 non-null   object \n",
      " 24  dog_stage           1963 non-null   object \n",
      "dtypes: bool(3), float64(3), int64(8), object(11)\n",
      "memory usage: 358.5+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  p1,p2 and p3 have inconsisitent capital words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "#### Replace the character '_' with space by using str.extract method and capitalize the first letters.\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to make all string lower case I will use lower() function on p1 ,p2 and p3 \n",
    "\n",
    "twitter['p1']=twitter['p1'].str.lower()\n",
    "twitter['p2']=twitter['p2'].str.lower()\n",
    "twitter['p3']=twitter['p3'].str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>p3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>orange</td>\n",
       "      <td>bagel</td>\n",
       "      <td>banana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chihuahua</td>\n",
       "      <td>pekinese</td>\n",
       "      <td>papillon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chihuahua</td>\n",
       "      <td>malamute</td>\n",
       "      <td>kelpie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>paper_towel</td>\n",
       "      <td>labrador_retriever</td>\n",
       "      <td>spatula</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>basset</td>\n",
       "      <td>english_springer</td>\n",
       "      <td>german_short-haired_pointer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            p1                  p2                           p3\n",
       "0       orange               bagel                       banana\n",
       "1    chihuahua            pekinese                     papillon\n",
       "2    chihuahua            malamute                       kelpie\n",
       "3  paper_towel  labrador_retriever                      spatula\n",
       "4       basset    english_springer  german_short-haired_pointer"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter[['p1', 'p2', 'p3']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "#### drop duplicate jpg_url.\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping duplicates jpg_url\n",
    "twitter = twitter.drop_duplicates(subset=['jpg_url'], keep='last')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(twitter['jpg_url'].duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "\n",
    "#### p1,p2 and p3 have unnessary underscore instead of space\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simply using string.replace function to repalce _ \n",
    "twitter['p1'] = twitter['p1'].str.replace('_', ' ')\n",
    "twitter['p2'] = twitter['p2'].str.replace('_', ' ')\n",
    "twitter['p3'] = twitter['p3'].str.replace('_', ' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter[twitter['p1']=='_'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter[twitter['p2']=='_'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter[twitter['p3']=='_'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>420</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1776</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rating_numerator  rating_denominator  count\n",
       "0                  0                  10      1\n",
       "1                  1                   2      1\n",
       "2                  1                  10      4\n",
       "3                  2                  10      9\n",
       "4                  3                  10     19\n",
       "5                  4                  10     15\n",
       "6                  4                  20      1\n",
       "7                  5                  10     33\n",
       "8                  6                  10     32\n",
       "9                  7                  10     51\n",
       "10                 7                  11      1\n",
       "11                 8                  10     95\n",
       "12                 9                  10    149\n",
       "13                 9                  11      1\n",
       "14                10                  10    416\n",
       "15                11                  10    392\n",
       "16                12                  10    443\n",
       "17                13                  10    249\n",
       "18                14                  10     33\n",
       "19                24                   7      1\n",
       "20                26                  10      1\n",
       "21                27                  10      1\n",
       "22                44                  40      1\n",
       "23                45                  50      1\n",
       "24                50                  50      1\n",
       "25                60                  50      1\n",
       "26                75                  10      1\n",
       "27                80                  80      1\n",
       "28                84                  70      1\n",
       "29                88                  80      1\n",
       "30                99                  90      1\n",
       "31               121                 110      1\n",
       "32               144                 120      1\n",
       "33               165                 150      1\n",
       "34               204                 170      1\n",
       "35               420                  10      1\n",
       "36              1776                  10      1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.groupby(['rating_numerator', 'rating_denominator']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "\n",
    "##### Issue: Some ratings with decimals such as 13.5/10, 9.5/10 have been incorrectly exported as 5/10 (in addition to other numbers with decimals such as 11.26 and 11.27).\n",
    "\n",
    "##### Solution: Find all instances of ratings that contained decimals and replace the numerator values with the correct values.\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter['rating_numerator'][twitter.rating_numerator == 50] = 11\n",
    "twitter['rating_numerator'][twitter.rating_numerator == 27] = 11.27\n",
    "twitter['rating_numerator'][twitter.rating_numerator == 75] = 9.75\n",
    "twitter['rating_numerator'][twitter.rating_numerator == 26] = 11.26"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.00</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.00</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.00</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.00</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.00</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.00</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.00</td>\n",
       "      <td>10</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.00</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.00</td>\n",
       "      <td>10</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.00</td>\n",
       "      <td>10</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9.00</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9.75</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.00</td>\n",
       "      <td>10</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11.00</td>\n",
       "      <td>10</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11.00</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.26</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.27</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12.00</td>\n",
       "      <td>10</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>13.00</td>\n",
       "      <td>10</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14.00</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.00</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>44.00</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>45.00</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>60.00</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>80.00</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>84.00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>88.00</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>99.00</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>121.00</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>144.00</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>165.00</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>204.00</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>420.00</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1776.00</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rating_numerator  rating_denominator  count\n",
       "0               0.00                  10      1\n",
       "1               1.00                   2      1\n",
       "2               1.00                  10      4\n",
       "3               2.00                  10      9\n",
       "4               3.00                  10     19\n",
       "5               4.00                  10     15\n",
       "6               4.00                  20      1\n",
       "7               5.00                  10     33\n",
       "8               6.00                  10     32\n",
       "9               7.00                  10     51\n",
       "10              7.00                  11      1\n",
       "11              8.00                  10     95\n",
       "12              9.00                  10    149\n",
       "13              9.00                  11      1\n",
       "14              9.75                  10      1\n",
       "15             10.00                  10    416\n",
       "16             11.00                  10    392\n",
       "17             11.00                  50      1\n",
       "18             11.26                  10      1\n",
       "19             11.27                  10      1\n",
       "20             12.00                  10    443\n",
       "21             13.00                  10    249\n",
       "22             14.00                  10     33\n",
       "23             24.00                   7      1\n",
       "24             44.00                  40      1\n",
       "25             45.00                  50      1\n",
       "26             60.00                  50      1\n",
       "27             80.00                  80      1\n",
       "28             84.00                  70      1\n",
       "29             88.00                  80      1\n",
       "30             99.00                  90      1\n",
       "31            121.00                 110      1\n",
       "32            144.00                 120      1\n",
       "33            165.00                 150      1\n",
       "34            204.00                 170      1\n",
       "35            420.00                  10      1\n",
       "36           1776.00                  10      1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.groupby(['rating_numerator', 'rating_denominator']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define\n",
    "\n",
    "##### Issue: Some ratings with decimals such as 1776,24,420\n",
    "\n",
    "##### Solution: remove from data frame\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter=twitter[~twitter.rating_numerator.isin([1776,24,420])]\n",
    "#twitter.rating_numerator = twitter.rating_numerator.astype('float') \n",
    "#twitter.rating_denominator = twitter.rating_denominator.astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.00</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.00</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.00</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.00</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.00</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.00</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.00</td>\n",
       "      <td>10</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.00</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.00</td>\n",
       "      <td>10</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.00</td>\n",
       "      <td>10</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9.00</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9.75</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.00</td>\n",
       "      <td>10</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11.00</td>\n",
       "      <td>10</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11.00</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.26</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.27</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>12.00</td>\n",
       "      <td>10</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>13.00</td>\n",
       "      <td>10</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14.00</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>44.00</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>45.00</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>60.00</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>80.00</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>84.00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>88.00</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>99.00</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>121.00</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>144.00</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>165.00</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>204.00</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rating_numerator  rating_denominator  count\n",
       "0               0.00                  10      1\n",
       "1               1.00                   2      1\n",
       "2               1.00                  10      4\n",
       "3               2.00                  10      9\n",
       "4               3.00                  10     19\n",
       "5               4.00                  10     15\n",
       "6               4.00                  20      1\n",
       "7               5.00                  10     33\n",
       "8               6.00                  10     32\n",
       "9               7.00                  10     51\n",
       "10              7.00                  11      1\n",
       "11              8.00                  10     95\n",
       "12              9.00                  10    149\n",
       "13              9.00                  11      1\n",
       "14              9.75                  10      1\n",
       "15             10.00                  10    416\n",
       "16             11.00                  10    392\n",
       "17             11.00                  50      1\n",
       "18             11.26                  10      1\n",
       "19             11.27                  10      1\n",
       "20             12.00                  10    443\n",
       "21             13.00                  10    249\n",
       "22             14.00                  10     33\n",
       "23             44.00                  40      1\n",
       "24             45.00                  50      1\n",
       "25             60.00                  50      1\n",
       "26             80.00                  80      1\n",
       "27             84.00                  70      1\n",
       "28             88.00                  80      1\n",
       "29             99.00                  90      1\n",
       "30            121.00                 110      1\n",
       "31            144.00                 120      1\n",
       "32            165.00                 150      1\n",
       "33            204.00                 170      1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.groupby(['rating_numerator', 'rating_denominator']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "\n",
    "##### Issue: Some ratings with decimals as 99,80,45,60,44,84,204,121,144,88 ,  depande on number of dogs\n",
    "\n",
    "##### Solution: numrate/denumurate\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter['rating_numerator'][twitter.rating_numerator >14]=twitter['rating_numerator']/(twitter['rating_denominator'].div(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.00</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.00</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.00</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.00</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.00</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.00</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.00</td>\n",
       "      <td>10</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.00</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8.00</td>\n",
       "      <td>10</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.00</td>\n",
       "      <td>10</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9.00</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9.00</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9.75</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10.00</td>\n",
       "      <td>10</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.00</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.00</td>\n",
       "      <td>10</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11.00</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11.00</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11.00</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11.00</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11.00</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11.00</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11.26</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11.27</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12.00</td>\n",
       "      <td>10</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>12.00</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>12.00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>12.00</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>12.00</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13.00</td>\n",
       "      <td>10</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>14.00</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rating_numerator  rating_denominator  count\n",
       "0               0.00                  10      1\n",
       "1               1.00                   2      1\n",
       "2               1.00                  10      4\n",
       "3               2.00                  10      9\n",
       "4               3.00                  10     19\n",
       "5               4.00                  10     15\n",
       "6               4.00                  20      1\n",
       "7               5.00                  10     33\n",
       "8               6.00                  10     32\n",
       "9               7.00                  10     51\n",
       "10              7.00                  11      1\n",
       "11              8.00                  10     95\n",
       "12              9.00                  10    149\n",
       "13              9.00                  11      1\n",
       "14              9.00                  50      1\n",
       "15              9.75                  10      1\n",
       "16             10.00                  10    416\n",
       "17             10.00                  80      1\n",
       "18             11.00                  10    392\n",
       "19             11.00                  40      1\n",
       "20             11.00                  50      1\n",
       "21             11.00                  80      1\n",
       "22             11.00                  90      1\n",
       "23             11.00                 110      1\n",
       "24             11.00                 150      1\n",
       "25             11.26                  10      1\n",
       "26             11.27                  10      1\n",
       "27             12.00                  10    443\n",
       "28             12.00                  50      1\n",
       "29             12.00                  70      1\n",
       "30             12.00                 120      1\n",
       "31             12.00                 170      1\n",
       "32             13.00                  10    249\n",
       "33             14.00                  10     33"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.groupby(['rating_numerator', 'rating_denominator']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "\n",
    "#### Correct denominators not equal 10.\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter.rating_denominator=10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.00</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.00</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.00</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.00</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.00</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.00</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.00</td>\n",
       "      <td>10</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.00</td>\n",
       "      <td>10</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.00</td>\n",
       "      <td>10</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9.75</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.00</td>\n",
       "      <td>10</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.00</td>\n",
       "      <td>10</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.26</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11.27</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12.00</td>\n",
       "      <td>10</td>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13.00</td>\n",
       "      <td>10</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14.00</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rating_numerator  rating_denominator  count\n",
       "0               0.00                  10      1\n",
       "1               1.00                  10      5\n",
       "2               2.00                  10      9\n",
       "3               3.00                  10     19\n",
       "4               4.00                  10     16\n",
       "5               5.00                  10     33\n",
       "6               6.00                  10     32\n",
       "7               7.00                  10     52\n",
       "8               8.00                  10     95\n",
       "9               9.00                  10    151\n",
       "10              9.75                  10      1\n",
       "11             10.00                  10    417\n",
       "12             11.00                  10    398\n",
       "13             11.26                  10      1\n",
       "14             11.27                  10      1\n",
       "15             12.00                  10    447\n",
       "16             13.00                  10    249\n",
       "17             14.00                  10     33"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.groupby(['rating_numerator', 'rating_denominator']).size().reset_index(name='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Combine rating_numerator and rating_denominator columns into one column\n",
    "\n",
    "### Define\n",
    "\n",
    "#### Create a rating column by dividing rating_numerator values by rating_denominator values for an easier data analysis.\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter['rating'] = twitter.rating_numerator / twitter.rating_denominator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1960 entries, 0 to 2057\n",
      "Data columns (total 26 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   tweet_id            1960 non-null   int64  \n",
      " 1   timestamp           1960 non-null   object \n",
      " 2   source              1960 non-null   object \n",
      " 3   text                1960 non-null   object \n",
      " 4   expanded_urls       1960 non-null   object \n",
      " 5   rating_numerator    1960 non-null   float64\n",
      " 6   rating_denominator  1960 non-null   int64  \n",
      " 7   name                1960 non-null   object \n",
      " 8   jpg_url             1960 non-null   object \n",
      " 9   img_num             1960 non-null   int64  \n",
      " 10  p1                  1960 non-null   object \n",
      " 11  p1_conf             1960 non-null   float64\n",
      " 12  p1_dog              1960 non-null   bool   \n",
      " 13  p2                  1960 non-null   object \n",
      " 14  p2_conf             1960 non-null   float64\n",
      " 15  p2_dog              1960 non-null   bool   \n",
      " 16  p3                  1960 non-null   object \n",
      " 17  p3_conf             1960 non-null   float64\n",
      " 18  p3_dog              1960 non-null   bool   \n",
      " 19  favorites           1960 non-null   int64  \n",
      " 20  retweets            1960 non-null   int64  \n",
      " 21  user_followers      1960 non-null   int64  \n",
      " 22  user_favourites     1960 non-null   int64  \n",
      " 23  date_time           1960 non-null   object \n",
      " 24  dog_stage           1960 non-null   object \n",
      " 25  rating              1960 non-null   float64\n",
      "dtypes: bool(3), float64(5), int64(7), object(11)\n",
      "memory usage: 373.2+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define\n",
    "\n",
    "#### replace the url from value of source .\n",
    "\n",
    "### code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter.loc[twitter['source'].str.contains('<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>'), 'source'] = 'Twitter for iPhone'\n",
    "twitter.loc[twitter['source'].str.contains('<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>'), 'source'] = 'Twitter Web Client'\n",
    "twitter.loc[twitter['source'].str.contains('<a href=\"http://vine.co\" rel=\"nofollow\">Vine - Make a Scene</a>'), 'source'] = 'Vine - Make a Scene'\n",
    "twitter.loc[twitter['source'].str.contains('<a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>'), 'source'] = 'TweetDeck'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Twitter for iPhone', 'Twitter Web Client', 'TweetDeck'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.source.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The name column has many invalid values like , a, an, the.\n",
    "\n",
    "\n",
    "\n",
    "### Define\n",
    "#### Names are probably extracted after the statements 'This is','Here is', 'hello to', 'Meet'. So that is why we have lots of words in the name column such that; a, an, the, etc. Notice that all of those are lowercase. Find the lowercase names in name cloumn and re-extract the names that come after the statements: 'name is' and 'named'. Replace the rest of the lowercase names with 'None'. Also, change the name 'O' with \"O'Malley\".\n",
    "\n",
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a',\n",
       " 'the',\n",
       " 'an',\n",
       " 'one',\n",
       " 'very',\n",
       " 'just',\n",
       " 'quite',\n",
       " 'getting',\n",
       " 'unacceptable',\n",
       " 'all',\n",
       " 'this',\n",
       " 'his',\n",
       " 'actually',\n",
       " 'officially',\n",
       " 'my',\n",
       " 'space',\n",
       " 'by',\n",
       " 'incredibly',\n",
       " 'infuriating',\n",
       " 'such',\n",
       " 'light',\n",
       " 'not',\n",
       " 'None']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_dog_names = twitter.loc[(twitter.name.str.islower())].name.value_counts().index.tolist()\n",
    "not_dog_names.append('None')\n",
    "not_dog_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in not_dog_names:\n",
    "    twitter.loc[twitter.name == name, 'name'] = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Oliver       10\n",
       "Charlie      10\n",
       "Cooper       10\n",
       "Penny         9\n",
       "Lucy          9\n",
       "             ..\n",
       "Ruffles       1\n",
       "Kathmandu     1\n",
       "Antony        1\n",
       "Kendall       1\n",
       "Edd           1\n",
       "Name: name, Length: 909, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.name.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing Dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save twitter dataframe to a CSV file\n",
    "twitter.to_csv('twitter_archive_master.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing & Visualizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Unnamed: 0</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>...</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweets</th>\n",
       "      <th>user_followers</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>date_time</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "      <td>35385</td>\n",
       "      <td>7470</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "      <td>30622</td>\n",
       "      <td>5547</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "      <td>23040</td>\n",
       "      <td>3671</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "      <td>38638</td>\n",
       "      <td>7637</td>\n",
       "      <td>8899220</td>\n",
       "      <td>145977</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>german short-haired pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "      <td>36955</td>\n",
       "      <td>8246</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0            tweet_id                  timestamp  \\\n",
       "0           0  892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "1           1  892177421306343426  2017-08-01 00:17:27 +0000   \n",
       "2           2  891815181378084864  2017-07-31 00:18:03 +0000   \n",
       "3           3  891689557279858688  2017-07-30 15:58:51 +0000   \n",
       "4           4  891327558926688256  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "               source                                               text  \\\n",
       "0  Twitter for iPhone  This is Phineas. He's a mystical boy. Only eve...   \n",
       "1  Twitter for iPhone  This is Tilly. She's just checking pup on you....   \n",
       "2  Twitter for iPhone  This is Archie. He is a rare Norwegian Pouncin...   \n",
       "3  Twitter for iPhone  This is Darla. She commenced a snooze mid meal...   \n",
       "4  Twitter for iPhone  This is Franklin. He would like you to stop ca...   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...              13.0   \n",
       "1  https://twitter.com/dog_rates/status/892177421...              13.0   \n",
       "2  https://twitter.com/dog_rates/status/891815181...              12.0   \n",
       "3  https://twitter.com/dog_rates/status/891689557...              13.0   \n",
       "4  https://twitter.com/dog_rates/status/891327558...              12.0   \n",
       "\n",
       "   rating_denominator      name  \\\n",
       "0                  10   Phineas   \n",
       "1                  10     Tilly   \n",
       "2                  10    Archie   \n",
       "3                  10     Darla   \n",
       "4                  10  Franklin   \n",
       "\n",
       "                                           jpg_url  ...  \\\n",
       "0  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg  ...   \n",
       "1  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg  ...   \n",
       "2  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg  ...   \n",
       "3  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg  ...   \n",
       "4  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg  ...   \n",
       "\n",
       "                            p3   p3_conf  p3_dog  favorites retweets  \\\n",
       "0                       banana  0.076110   False      35385     7470   \n",
       "1                     papillon  0.068957    True      30622     5547   \n",
       "2                       kelpie  0.031379    True      23040     3671   \n",
       "3                      spatula  0.040836   False      38638     7637   \n",
       "4  german short-haired pointer  0.175219    True      36955     8246   \n",
       "\n",
       "   user_followers  user_favourites                  date_time  dog_stage  \\\n",
       "0         8898582           145976  2017-08-01 16:23:56+00:00       None   \n",
       "1         8898582           145976  2017-08-01 00:17:27+00:00       None   \n",
       "2         8898582           145976  2017-07-31 00:18:03+00:00       None   \n",
       "3         8899220           145977  2017-07-30 15:58:51+00:00       None   \n",
       "4         8898582           145976  2017-07-29 16:00:24+00:00       None   \n",
       "\n",
       "   rating  \n",
       "0     1.3  \n",
       "1     1.3  \n",
       "2     1.2  \n",
       "3     1.3  \n",
       "4     1.2  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter = pd.read_csv('twitter_archive_master.csv')\n",
    "twitter.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Unnamed: 0</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>...</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweets</th>\n",
       "      <th>user_followers</th>\n",
       "      <th>user_favourites</th>\n",
       "      <th>date_time</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.07611</td>\n",
       "      <td>False</td>\n",
       "      <td>35385</td>\n",
       "      <td>7470</td>\n",
       "      <td>8898582</td>\n",
       "      <td>145976</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0            tweet_id                  timestamp  \\\n",
       "0           0  892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "\n",
       "               source                                               text  \\\n",
       "0  Twitter for iPhone  This is Phineas. He's a mystical boy. Only eve...   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...              13.0   \n",
       "\n",
       "   rating_denominator     name  \\\n",
       "0                  10  Phineas   \n",
       "\n",
       "                                           jpg_url  ...      p3  p3_conf  \\\n",
       "0  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg  ...  banana  0.07611   \n",
       "\n",
       "   p3_dog  favorites retweets  user_followers  user_favourites  \\\n",
       "0   False      35385     7470         8898582           145976   \n",
       "\n",
       "                   date_time  dog_stage  rating  \n",
       "0  2017-08-01 16:23:56+00:00       None     1.3  \n",
       "\n",
       "[1 rows x 27 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which kind of source are people using the most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Twitter for iPhone    1923\n",
       "Twitter Web Client      28\n",
       "TweetDeck                9\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter.source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'source')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twitter.source.value_counts().plot(kind = 'bar', figsize=(5,3))\n",
    "plt.title('twitter source Distribution', fontsize=15)\n",
    "plt.ylabel('Count', fontsize=15)\n",
    "plt.xlabel('source', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### most people using Twitter for iPhone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What was the rating that most dogs got?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00       1\n",
       "1.00       5\n",
       "2.00       9\n",
       "3.00      19\n",
       "4.00      16\n",
       "5.00      33\n",
       "6.00      32\n",
       "7.00      52\n",
       "8.00      95\n",
       "9.00     151\n",
       "9.75       1\n",
       "10.00    417\n",
       "11.00    398\n",
       "11.26      1\n",
       "11.27      1\n",
       "12.00    447\n",
       "13.00    249\n",
       "14.00     33\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter['rating_numerator'].value_counts().sort_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### The rating that most dogs got was 12 over 10 ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many dogs were rated above 10?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1129 dogs were rated above 10.\n"
     ]
    }
   ],
   "source": [
    "print('{} dogs were rated above 10.'.format(round(twitter.rating_numerator[twitter.rating_numerator> 10]).count()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### which breed of dog people love the most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p1\n",
       "golden retriever      1539306\n",
       "labrador retriever     950379\n",
       "pembroke               909140\n",
       "chihuahua              626320\n",
       "samoyed                469120\n",
       "french bulldog         427760\n",
       "Name: favorites, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_favorites = twitter.groupby('p1')['favorites'].sum().sort_values(ascending=False).head(6)\n",
    "dog_favorites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p1\n",
       "golden retriever      432151\n",
       "labrador retriever    300458\n",
       "pembroke              241376\n",
       "chihuahua             193819\n",
       "samoyed               149990\n",
       "french bulldog        104131\n",
       "Name: retweets, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_retweets = twitter.groupby('p1')['retweets'].sum().sort_values(ascending=False).head(6)\n",
    "dog_retweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "\n",
    "dog_favorites.plot(figsize = (10,6), kind='bar', color='green', ax=ax1, width=0.4, position=1, \n",
    "             title='Popular Breeds: Likes vs. Retweets')\n",
    "dog_retweets.plot(figsize = (10,6), kind='bar', color='blue', ax=ax2, width=0.4, position=0)\n",
    "\n",
    "ax1.set_ylabel('Likes')\n",
    "ax2.set_ylabel('Retweets')\n",
    "ax1.set_xticklabels(dog_favorites.index, rotation=30)\n",
    "\n",
    "h1, l1 = ax1.get_legend_handles_labels()\n",
    "h2, l2 = ax2.get_legend_handles_labels()\n",
    "\n",
    "plt.legend(h1+h2, l1+l2, loc=1)\n",
    "#save pic\n",
    "plt.savefig('popular_dogs.png', dpi=100)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the relation between Retweets and Likes?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twitter.plot(kind='scatter',x='favorites',y='retweets', alpha = 0.5,color = 'green')\n",
    "plt.xlabel('Likes')\n",
    "plt.ylabel('Retweets')\n",
    "plt.title('Retweets and Likes Scatter Plot')\n",
    "\n",
    "plt.savefig('Retweets_vs_Likes.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Retweets are positively correlated with Likes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### what is the bar plot of dog stages?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams.update({'figure.autolayout': True})\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "twitter.dog_stage.value_counts().sort_values(ascending=False).plot.bar()\n",
    "plt.title(\"Popular dog stages\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel(\"Dog stages\")\n",
    "plt.ylabel(\"Number of dogs\");\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### There are 1566 dogs with unknown dog stage. It is a very high number and more time should be spent on cleaning dog_stage part. For now, I will exclude the category 'None' and continue to analyze it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Dog Stage')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dog_stages=twitter.groupby('dog_stage').filter(lambda x: len(x) < 250)\n",
    "dog_stages['dog_stage'].value_counts().plot(kind = 'bar')\n",
    "plt.title('Dog Stage Distribution', fontsize=15)\n",
    "plt.ylabel('Count', fontsize=15)\n",
    "plt.xlabel('Dog Stage', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Most of the dogs are pupper."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the 10 most frequent dog names?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twitter['name'].value_counts()[0:10].sort_values(ascending=False).plot(kind = 'bar',color = 'green')\n",
    "plt.ylabel('Number of Dogs')\n",
    "plt.title('Top 10 frequent dog names', size=15)\n",
    "plt.xlabel('Dog Names')\n",
    "plt.savefig('most_frequent_names');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cooper     10\n",
       "Charlie    10\n",
       "Oliver     10\n",
       "Tucker      9\n",
       "Lucy        9\n",
       "Penny       9\n",
       "Winston     8\n",
       "Sadie       8\n",
       "Lola        7\n",
       "Daisy       7\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter['name'].value_counts()[0:10].sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}