we_rate_dogs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0a343cb6",
   "metadata": {},
   "source": [
    "# Project: Wrangling and Analyzing WeRateDogs® Twitter Archive\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "   <li><a href=\"#intro\">Introduction</a></li> \n",
    "<li><a href=\"#gather\">Data Gathering</a></li>\n",
    "<li><a href=\"#assess\">Assessing Data</a></li>\n",
    "<li><a href=\"#clean\">Cleaning Data</a></li>\n",
    "<li><a href=\"#analysis\">Analyzing and Visualizing Data</a></li>\n",
    "    <li><a href=\"#insights\">Insights</a></li>\n",
    "    <li><a href=\"#wranglereport\">Wrangle Report</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6948cce1",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "The aim of this project is to wrangle and analyze data. The data used in this analysis is the tweet archive of a user, [WeRateDogs®](https://twitter.com/dog_rates), from 15th November 2015 to 1st August 2017. WeRateDogs is a Twitter account that rates dogs with a humorous comment about the dog. The data used is spread across different sources. The data included WeRateDogs twitter archive given by Udacity as a [csv file](https://d17h27t6h515a5.cloudfront.net/topher/2017/August/59a4e958_twitter-archive-enhanced/twitter-archive-enhanced.csv), a tsv file hosted on Udacity servers containing image predictions for each tweet ID and additional data on the tweets which I accessed using the Twitter API.\n",
    "\n",
    "The goal of this project is to gather and clean the data from the three sources as they contain some quality and tidiness issues and combine these into a master pandas DataFrame. I intend to then analyze this master DataFrame and derive insights from the data.\n",
    "\n",
    "In some instances, I viewed the tweets on Twitter using the tweet's ID by appending the ID to https://twitter.com/i/web/status/\n",
    "\n",
    "**NB:** A summarized report of my data gathering, assessing and cleaning efforts can be found at the end of the note. Clicking on `Wrangle Report` in the table to contents skips to that part."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad1eb83e",
   "metadata": {},
   "source": [
    "### Importing modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "32a0eab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "import json\n",
    "import os\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import requests\n",
    "import tweepy\n",
    "from tweepy import OAuthHandler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a382030d",
   "metadata": {},
   "source": [
    "<a id='gather'></a>\n",
    "##  Data Gathering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bd60d71",
   "metadata": {},
   "source": [
    "##### Importing first file (csv file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "822bf49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading csv file into a dataframe\n",
    "df = pd.read_csv('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8fa9f373",
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying first 5 rows of the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8174a50",
   "metadata": {},
   "source": [
    "##### Downloading second file using the Requests library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0d508dff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a folder\n",
    "directory_name = 'tsv_file'\n",
    "if not os.path.exists(directory_name):\n",
    "    os.makedirs(directory_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7eb6eb58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using the Requests library to get the contents of the url\n",
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "response = requests.get(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1dd818d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# writing the contents of the url to a file\n",
    "with open(os.path.join(directory_name,url.split('/')[-1]),mode='wb') as file:\n",
    "    file.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8e9678fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the tsv file into a dataframe\n",
    "df_02 = pd.read_csv('tsv_file/image-predictions.tsv',sep = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "87aeb21d",
   "metadata": {
    "scrolled": false
   },
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying first rows of the dataframe\n",
    "df_02.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9b79033",
   "metadata": {},
   "source": [
    "##### Getting third file from Twitter using the Tweepy library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f8bc166",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3948.1561274\n",
      "29\n"
     ]
    }
   ],
   "source": [
    "# the keys are hidden to comply with Twitter rules\n",
    "consumer_key = 'HIDDEN'\n",
    "consumer_secret = 'HIDDEN'\n",
    "access_token = 'HIDDEN'\n",
    "access_secret = 'HIDDEN'\n",
    "\n",
    "auth = OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True)\n",
    "\n",
    "# assigning tweet Ids in df to a variable\n",
    "tweet_ids = df.tweet_id.values\n",
    "len(tweet_ids)\n",
    "\n",
    "# creating dictionary to which erroes will be appended\n",
    "fails_dict = {}\n",
    "start = timer()\n",
    "# Saving each tweet's returned JSON as a new line in a .txt file\n",
    "with open('tweet_json.txt', 'w') as outfile:\n",
    "    # loop to get each tweet's json data\n",
    "    for tweet_id in tweet_ids:\n",
    "        try:\n",
    "            tweet = api.get_status(tweet_id, tweet_mode='extended')\n",
    "            json.dump(tweet._json, outfile)\n",
    "            outfile.write('\\n')\n",
    "        except Exception as e:\n",
    "            fails_dict[tweet_id] = e\n",
    "            pass\n",
    "end = timer()\n",
    "# printing time it took to run\n",
    "print(end - start)\n",
    "# printing the number of errors\n",
    "print(len(fails_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96148cfc",
   "metadata": {},
   "source": [
    "It took 66 mins to run and 29 tweet IDs were not retrieved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf98433e",
   "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>created_at</th>\n",
       "      <th>id</th>\n",
       "      <th>id_str</th>\n",
       "      <th>full_text</th>\n",
       "      <th>truncated</th>\n",
       "      <th>display_text_range</th>\n",
       "      <th>entities</th>\n",
       "      <th>extended_entities</th>\n",
       "      <th>source</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>...</th>\n",
       "      <th>favorited</th>\n",
       "      <th>retweeted</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>possibly_sensitive_appealable</th>\n",
       "      <th>lang</th>\n",
       "      <th>retweeted_status</th>\n",
       "      <th>quoted_status_id</th>\n",
       "      <th>quoted_status_id_str</th>\n",
       "      <th>quoted_status_permalink</th>\n",
       "      <th>quoted_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tue Aug 01 16:23:56 +0000 2017</td>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 85]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 892420639486877696, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tue Aug 01 00:17:27 +0000 2017</td>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 138]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 892177413194625024, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mon Jul 31 00:18:03 +0000 2017</td>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 121]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 891815175371796480, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sun Jul 30 15:58:51 +0000 2017</td>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 79]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 891689552724799489, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat Jul 29 16:00:24 +0000 2017</td>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 138]</td>\n",
       "      <td>{'hashtags': [{'text': 'BarkWeek', 'indices': ...</td>\n",
       "      <td>{'media': [{'id': 891327551943041024, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       created_at                  id              id_str  \\\n",
       "0  Tue Aug 01 16:23:56 +0000 2017  892420643555336193  892420643555336193   \n",
       "1  Tue Aug 01 00:17:27 +0000 2017  892177421306343426  892177421306343426   \n",
       "2  Mon Jul 31 00:18:03 +0000 2017  891815181378084864  891815181378084864   \n",
       "3  Sun Jul 30 15:58:51 +0000 2017  891689557279858688  891689557279858688   \n",
       "4  Sat Jul 29 16:00:24 +0000 2017  891327558926688256  891327558926688256   \n",
       "\n",
       "                                           full_text  truncated  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...      False   \n",
       "1  This is Tilly. She's just checking pup on you....      False   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...      False   \n",
       "3  This is Darla. She commenced a snooze mid meal...      False   \n",
       "4  This is Franklin. He would like you to stop ca...      False   \n",
       "\n",
       "  display_text_range                                           entities  \\\n",
       "0            [0, 85]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1           [0, 138]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2           [0, 121]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "3            [0, 79]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "4           [0, 138]  {'hashtags': [{'text': 'BarkWeek', 'indices': ...   \n",
       "\n",
       "                                   extended_entities  \\\n",
       "0  {'media': [{'id': 892420639486877696, 'id_str'...   \n",
       "1  {'media': [{'id': 892177413194625024, 'id_str'...   \n",
       "2  {'media': [{'id': 891815175371796480, 'id_str'...   \n",
       "3  {'media': [{'id': 891689552724799489, 'id_str'...   \n",
       "4  {'media': [{'id': 891327551943041024, 'id_str'...   \n",
       "\n",
       "                                              source  in_reply_to_status_id  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...                    NaN   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...                    NaN   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...                    NaN   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...                    NaN   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...                    NaN   \n",
       "\n",
       "   ... favorited  retweeted possibly_sensitive possibly_sensitive_appealable  \\\n",
       "0  ...     False      False              False                         False   \n",
       "1  ...     False      False              False                         False   \n",
       "2  ...     False      False              False                         False   \n",
       "3  ...     False      False              False                         False   \n",
       "4  ...     False      False              False                         False   \n",
       "\n",
       "  lang retweeted_status quoted_status_id quoted_status_id_str  \\\n",
       "0   en              NaN              NaN                  NaN   \n",
       "1   en              NaN              NaN                  NaN   \n",
       "2   en              NaN              NaN                  NaN   \n",
       "3   en              NaN              NaN                  NaN   \n",
       "4   en              NaN              NaN                  NaN   \n",
       "\n",
       "  quoted_status_permalink  quoted_status  \n",
       "0                     NaN            NaN  \n",
       "1                     NaN            NaN  \n",
       "2                     NaN            NaN  \n",
       "3                     NaN            NaN  \n",
       "4                     NaN            NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the json contents stored in the txt file to  a DataFrame\n",
    "with open('tweet_json.txt') as f:\n",
    "    data = [json.loads(line) for line in f ]\n",
    "\n",
    "json_df = pd.DataFrame(data)\n",
    "json_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a9625333",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating new DataFrame using relevant columns from json_df\n",
    "df_03 = json_df[['id','retweet_count','favorite_count']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69ff39d8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1158</th>\n",
       "      <td>718540630683709445</td>\n",
       "      <td>920</td>\n",
       "      <td>2241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>712097430750289920</td>\n",
       "      <td>930</td>\n",
       "      <td>3448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1214</th>\n",
       "      <td>711998809858043904</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>724046343203856385</td>\n",
       "      <td>512</td>\n",
       "      <td>2449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>752917284578922496</td>\n",
       "      <td>1436</td>\n",
       "      <td>6482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790</th>\n",
       "      <td>676590572941893632</td>\n",
       "      <td>110</td>\n",
       "      <td>834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>706291001778950144</td>\n",
       "      <td>434</td>\n",
       "      <td>1565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1579</th>\n",
       "      <td>685663452032069632</td>\n",
       "      <td>1328</td>\n",
       "      <td>2967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>714485234495041536</td>\n",
       "      <td>1007</td>\n",
       "      <td>2444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>704480331685040129</td>\n",
       "      <td>977</td>\n",
       "      <td>3140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id  retweet_count  favorite_count\n",
       "1158  718540630683709445            920            2241\n",
       "1210  712097430750289920            930            3448\n",
       "1214  711998809858043904            120               0\n",
       "1131  724046343203856385            512            2449\n",
       "914   752917284578922496           1436            6482\n",
       "1790  676590572941893632            110             834\n",
       "1294  706291001778950144            434            1565\n",
       "1579  685663452032069632           1328            2967\n",
       "1193  714485234495041536           1007            2444\n",
       "1318  704480331685040129            977            3140"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying a sample of the DataFrame \n",
    "df_03.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "315e3a48",
   "metadata": {},
   "source": [
    "<a id='assess'></a>\n",
    "## Assessing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc7ff01c",
   "metadata": {},
   "source": [
    "##### df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "07b05af3",
   "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>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>2108</th>\n",
       "      <td>670452855871037440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-28 04:03:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This dog can't see its haters. 11/10 https://t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670452855...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>718631497683582976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-04-09 02:47:55 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>We normally don't rate marshmallows but this o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/718631497...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>768970937022709760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-26 00:38:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>This is Kirby. His bowl weighs more than him. ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/768970937...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Kirby</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1784</th>\n",
       "      <td>677662372920729601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-18 01:31:14 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>In honor of the new Star Wars movie. Here's Yo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/677662372...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>776819012571455488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-16 16:24:19 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: Everybody look at this beautifu...</td>\n",
       "      <td>6.798284e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-12-24 00:58:27 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/679828447...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>783466772167098368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-05 00:40:09 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Finn. He likes eavesdropping from fili...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/783466772...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Finn</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\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>984</th>\n",
       "      <td>749317047558017024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-02 19:01:20 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Blu. He's a wild bush Floofer. I wish ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/749317047...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Blu</td>\n",
       "      <td>None</td>\n",
       "      <td>floofer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>839549326359670784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-03-08 18:52:12 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Winston. He knows he's a little too big f...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/839549326...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Winston</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2133</th>\n",
       "      <td>670073503555706880</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-27 02:55:47 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Winston. He wants to be a power drill. Ve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670073503...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Winston</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",
       "2108  670452855871037440                    NaN                  NaN   \n",
       "1184  718631497683582976                    NaN                  NaN   \n",
       "828   768970937022709760                    NaN                  NaN   \n",
       "1784  677662372920729601                    NaN                  NaN   \n",
       "770   776819012571455488                    NaN                  NaN   \n",
       "718   783466772167098368                    NaN                  NaN   \n",
       "0     892420643555336193                    NaN                  NaN   \n",
       "984   749317047558017024                    NaN                  NaN   \n",
       "280   839549326359670784                    NaN                  NaN   \n",
       "2133  670073503555706880                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "2108  2015-11-28 04:03:11 +0000   \n",
       "1184  2016-04-09 02:47:55 +0000   \n",
       "828   2016-08-26 00:38:52 +0000   \n",
       "1784  2015-12-18 01:31:14 +0000   \n",
       "770   2016-09-16 16:24:19 +0000   \n",
       "718   2016-10-05 00:40:09 +0000   \n",
       "0     2017-08-01 16:23:56 +0000   \n",
       "984   2016-07-02 19:01:20 +0000   \n",
       "280   2017-03-08 18:52:12 +0000   \n",
       "2133  2015-11-27 02:55:47 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "2108  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1184  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "828   <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "1784  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "770   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "718   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "0     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "984   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "280   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2133  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "2108  This dog can't see its haters. 11/10 https://t...                  NaN   \n",
       "1184  We normally don't rate marshmallows but this o...                  NaN   \n",
       "828   This is Kirby. His bowl weighs more than him. ...                  NaN   \n",
       "1784  In honor of the new Star Wars movie. Here's Yo...                  NaN   \n",
       "770   RT @dog_rates: Everybody look at this beautifu...         6.798284e+17   \n",
       "718   This is Finn. He likes eavesdropping from fili...                  NaN   \n",
       "0     This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "984   This is Blu. He's a wild bush Floofer. I wish ...                  NaN   \n",
       "280   Meet Winston. He knows he's a little too big f...                  NaN   \n",
       "2133  Meet Winston. He wants to be a power drill. Ve...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "2108                       NaN                        NaN   \n",
       "1184                       NaN                        NaN   \n",
       "828                        NaN                        NaN   \n",
       "1784                       NaN                        NaN   \n",
       "770               4.196984e+09  2015-12-24 00:58:27 +0000   \n",
       "718                        NaN                        NaN   \n",
       "0                          NaN                        NaN   \n",
       "984                        NaN                        NaN   \n",
       "280                        NaN                        NaN   \n",
       "2133                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "2108  https://twitter.com/dog_rates/status/670452855...                11   \n",
       "1184  https://twitter.com/dog_rates/status/718631497...                10   \n",
       "828   https://twitter.com/dog_rates/status/768970937...                12   \n",
       "1784  https://twitter.com/dog_rates/status/677662372...                12   \n",
       "770   https://twitter.com/dog_rates/status/679828447...                13   \n",
       "718   https://twitter.com/dog_rates/status/783466772...                11   \n",
       "0     https://twitter.com/dog_rates/status/892420643...                13   \n",
       "984   https://twitter.com/dog_rates/status/749317047...                12   \n",
       "280   https://twitter.com/dog_rates/status/839549326...                12   \n",
       "2133  https://twitter.com/dog_rates/status/670073503...                10   \n",
       "\n",
       "      rating_denominator     name doggo  floofer  pupper puppo  \n",
       "2108                  10     None  None     None    None  None  \n",
       "1184                  10     None  None     None    None  None  \n",
       "828                   10    Kirby  None     None    None  None  \n",
       "1784                  10     None  None     None    None  None  \n",
       "770                   10     None  None     None  pupper  None  \n",
       "718                   10     Finn  None     None    None  None  \n",
       "0                     10  Phineas  None     None    None  None  \n",
       "984                   10      Blu  None  floofer    None  None  \n",
       "280                   10  Winston  None     None    None  None  \n",
       "2133                  10  Winston  None     None    None  None  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2cb389a7",
   "metadata": {
    "scrolled": true
   },
   "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.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "11b3327f",
   "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>200</th>\n",
       "      <td>854010172552949760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-04-17 16:34:26 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>At first I thought this was a shy doggo, but i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/854010172...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>floofer</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",
       "200  854010172552949760                    NaN                  NaN   \n",
       "\n",
       "                     timestamp  \\\n",
       "200  2017-04-17 16:34:26 +0000   \n",
       "\n",
       "                                                source  \\\n",
       "200  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                  text  retweeted_status_id  \\\n",
       "200  At first I thought this was a shy doggo, but i...                  NaN   \n",
       "\n",
       "     retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "200                       NaN                        NaN   \n",
       "\n",
       "                                         expanded_urls  rating_numerator  \\\n",
       "200  https://twitter.com/dog_rates/status/854010172...                11   \n",
       "\n",
       "     rating_denominator  name  doggo  floofer pupper puppo  \n",
       "200                  10  None  doggo  floofer   None  None  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for double dog stages\n",
    "df[(df['doggo'] != \"None\") & (df['floofer'] != \"None\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77b8f61e",
   "metadata": {},
   "source": [
    "* index 200 - Two dog stages were extracted from the tweet because two dog stages were mentioned but it is only one dog and the stage is `floofer` therefore `doggo` needs to be replaced with `None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "76c0f917",
   "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>460</th>\n",
       "      <td>817777686764523521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-07 16:59:28 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Dido. She's playing the lead role in \"...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/817777686...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Dido</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>808106460588765185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-12 00:29:28 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have Burke (pupper) and Dexter (doggo)...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/808106460...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>802265048156610565</td>\n",
       "      <td>7.331095e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-11-25 21:37:47 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Like doggo, like pupper version 2. Both 11/10 ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/802265048...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>801115127852503040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-22 17:28:25 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Bones. He's being haunted by another d...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/801115127...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Bones</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>785639753186217984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-11 00:34:48 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Pinot. He's a sophisticated doggo. You...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/785639753...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Pinot</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>781308096455073793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-29 01:42:20 +0000</td>\n",
       "      <td>&lt;a href=\"http://vine.co\" rel=\"nofollow\"&gt;Vine -...</td>\n",
       "      <td>Pupper butt 1, Doggo 0. Both 12/10 https://t.c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://vine.co/v/5rgu2Law2ut</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>775898661951791106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-14 03:27:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: Like father (doggo), like son (...</td>\n",
       "      <td>7.331095e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-05-19 01:38:16 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/733109485...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>770093767776997377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-29 03:00:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is just downright precious...</td>\n",
       "      <td>7.410673e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-06-10 00:39:48 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/741067306...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>just</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>759793422261743616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-31 16:50:42 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Maggie &amp;amp; Lila. Maggie is the doggo, L...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/759793422...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Maggie</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>751583847268179968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-09 01:08:47 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Please stop sending it pictures that don't eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/751583847...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>741067306818797568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-10 00:39:48 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is just downright precious af. 12/10 for ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/741067306...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>just</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>733109485275860992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-05-19 01:38:16 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Like father (doggo), like son (pupper). Both 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/733109485...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</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",
       "460   817777686764523521                    NaN                  NaN   \n",
       "531   808106460588765185                    NaN                  NaN   \n",
       "565   802265048156610565           7.331095e+17         4.196984e+09   \n",
       "575   801115127852503040                    NaN                  NaN   \n",
       "705   785639753186217984                    NaN                  NaN   \n",
       "733   781308096455073793                    NaN                  NaN   \n",
       "778   775898661951791106                    NaN                  NaN   \n",
       "822   770093767776997377                    NaN                  NaN   \n",
       "889   759793422261743616                    NaN                  NaN   \n",
       "956   751583847268179968                    NaN                  NaN   \n",
       "1063  741067306818797568                    NaN                  NaN   \n",
       "1113  733109485275860992                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "460   2017-01-07 16:59:28 +0000   \n",
       "531   2016-12-12 00:29:28 +0000   \n",
       "565   2016-11-25 21:37:47 +0000   \n",
       "575   2016-11-22 17:28:25 +0000   \n",
       "705   2016-10-11 00:34:48 +0000   \n",
       "733   2016-09-29 01:42:20 +0000   \n",
       "778   2016-09-14 03:27:11 +0000   \n",
       "822   2016-08-29 03:00:36 +0000   \n",
       "889   2016-07-31 16:50:42 +0000   \n",
       "956   2016-07-09 01:08:47 +0000   \n",
       "1063  2016-06-10 00:39:48 +0000   \n",
       "1113  2016-05-19 01:38:16 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "460   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "531   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "565   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "575   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "705   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "733   <a href=\"http://vine.co\" rel=\"nofollow\">Vine -...   \n",
       "778   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "822   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "889   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "956   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1063  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1113  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "460   This is Dido. She's playing the lead role in \"...                  NaN   \n",
       "531   Here we have Burke (pupper) and Dexter (doggo)...                  NaN   \n",
       "565   Like doggo, like pupper version 2. Both 11/10 ...                  NaN   \n",
       "575   This is Bones. He's being haunted by another d...                  NaN   \n",
       "705   This is Pinot. He's a sophisticated doggo. You...                  NaN   \n",
       "733   Pupper butt 1, Doggo 0. Both 12/10 https://t.c...                  NaN   \n",
       "778   RT @dog_rates: Like father (doggo), like son (...         7.331095e+17   \n",
       "822   RT @dog_rates: This is just downright precious...         7.410673e+17   \n",
       "889   Meet Maggie &amp; Lila. Maggie is the doggo, L...                  NaN   \n",
       "956   Please stop sending it pictures that don't eve...                  NaN   \n",
       "1063  This is just downright precious af. 12/10 for ...                  NaN   \n",
       "1113  Like father (doggo), like son (pupper). Both 1...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "460                        NaN                        NaN   \n",
       "531                        NaN                        NaN   \n",
       "565                        NaN                        NaN   \n",
       "575                        NaN                        NaN   \n",
       "705                        NaN                        NaN   \n",
       "733                        NaN                        NaN   \n",
       "778               4.196984e+09  2016-05-19 01:38:16 +0000   \n",
       "822               4.196984e+09  2016-06-10 00:39:48 +0000   \n",
       "889                        NaN                        NaN   \n",
       "956                        NaN                        NaN   \n",
       "1063                       NaN                        NaN   \n",
       "1113                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "460   https://twitter.com/dog_rates/status/817777686...                13   \n",
       "531   https://twitter.com/dog_rates/status/808106460...                12   \n",
       "565   https://twitter.com/dog_rates/status/802265048...                11   \n",
       "575   https://twitter.com/dog_rates/status/801115127...                12   \n",
       "705   https://twitter.com/dog_rates/status/785639753...                10   \n",
       "733                       https://vine.co/v/5rgu2Law2ut                12   \n",
       "778   https://twitter.com/dog_rates/status/733109485...                12   \n",
       "822   https://twitter.com/dog_rates/status/741067306...                12   \n",
       "889   https://twitter.com/dog_rates/status/759793422...                12   \n",
       "956   https://twitter.com/dog_rates/status/751583847...                 5   \n",
       "1063  https://twitter.com/dog_rates/status/741067306...                12   \n",
       "1113  https://twitter.com/dog_rates/status/733109485...                12   \n",
       "\n",
       "      rating_denominator    name  doggo floofer  pupper puppo  \n",
       "460                   10    Dido  doggo    None  pupper  None  \n",
       "531                   10    None  doggo    None  pupper  None  \n",
       "565                   10    None  doggo    None  pupper  None  \n",
       "575                   10   Bones  doggo    None  pupper  None  \n",
       "705                   10   Pinot  doggo    None  pupper  None  \n",
       "733                   10    None  doggo    None  pupper  None  \n",
       "778                   10    None  doggo    None  pupper  None  \n",
       "822                   10    just  doggo    None  pupper  None  \n",
       "889                   10  Maggie  doggo    None  pupper  None  \n",
       "956                   10    None  doggo    None  pupper  None  \n",
       "1063                  10    just  doggo    None  pupper  None  \n",
       "1113                  10    None  doggo    None  pupper  None  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['doggo'] != \"None\") & (df['pupper'] != \"None\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73120f52",
   "metadata": {},
   "source": [
    "* index 460 - dog stage is actually `pupper` therefore `doggo` has to be replaced with `None`\n",
    "* index 575 - dog is actually a `pupper` therefore `doggo` has to be replaced with `None`\n",
    "* index 705 - even though the animal is not a dog it's given stage was `doggo` not `pupper`\n",
    "* index 956 - even though two dog stages were mentioned, none applies to the dog\n",
    "\n",
    "NB: for indexes that were not mentioned, there were two dogs in the tweet hence the double stages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf5a6393",
   "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>191</th>\n",
       "      <td>855851453814013952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-04-22 18:31:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here's a puppo participating in the #ScienceMa...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/855851453...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "191  855851453814013952                    NaN                  NaN   \n",
       "\n",
       "                     timestamp  \\\n",
       "191  2017-04-22 18:31:02 +0000   \n",
       "\n",
       "                                                source  \\\n",
       "191  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                  text  retweeted_status_id  \\\n",
       "191  Here's a puppo participating in the #ScienceMa...                  NaN   \n",
       "\n",
       "     retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "191                       NaN                        NaN   \n",
       "\n",
       "                                         expanded_urls  rating_numerator  \\\n",
       "191  https://twitter.com/dog_rates/status/855851453...                13   \n",
       "\n",
       "     rating_denominator  name  doggo floofer pupper  puppo  \n",
       "191                  10  None  doggo    None   None  puppo  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['doggo'] != \"None\") & (df['puppo'] != \"None\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "925cd3db",
   "metadata": {},
   "source": [
    "* index 191 - dog stage is `puppo` even though `doggo` was mentioned in the tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "66abe2b4",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['floofer'] != \"None\") & (df['pupper'] != \"None\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9bc5a58",
   "metadata": {},
   "source": [
    "No tweets with both `floofer` and `pupper` stages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f684d435",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['floofer'] != \"None\") & (df['puppo'] != \"None\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9132c8",
   "metadata": {},
   "source": [
    "No tweets with `floofer` and `puppo` stages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "01442e08",
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['pupper'] != \"None\") & (df['puppo'] != \"None\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a65dbaa",
   "metadata": {},
   "source": [
    "No tweets with `pupper` and `puppo` stages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5cdeaada",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2333\n",
       "11        3\n",
       "50        3\n",
       "20        2\n",
       "80        2\n",
       "70        1\n",
       "7         1\n",
       "15        1\n",
       "150       1\n",
       "170       1\n",
       "0         1\n",
       "90        1\n",
       "40        1\n",
       "130       1\n",
       "110       1\n",
       "16        1\n",
       "120       1\n",
       "2         1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the distribution of numbers in the `rating_denominators` column\n",
    "df.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b68c727",
   "metadata": {},
   "source": [
    "The rating denominator is almost always 10 and as the ratings were extracted from the text so I will display the text of tweets where the denominator is not 10 to and identify any problems with the ratings extraction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f257ae2f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…',\n",
       " 'After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ',\n",
       " 'This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 11').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "42fd65b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([784, 1068, 1662], dtype='int64')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 11').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c3c5ca",
   "metadata": {},
   "source": [
    "* index 784 is a retweet of the tweet ID in index 1068 and must be dropped\n",
    "* index 1068 - rating numerator is actually 14 and denominator is 10 \n",
    "* index 1662 - rating numerator is actually 10 and denominator is 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "715a8fe7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq',\n",
       " 'From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK',\n",
       " \"Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa\"]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 50').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ef518fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1202, 1274, 1351], dtype='int64')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 50').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a316ea",
   "metadata": {},
   "source": [
    "* Index 1202 - the actual rating is 11/10 not 50/50 \n",
    "* In the second and third indexes, the ratings were given to a group of dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7a65903f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a',\n",
       " \"Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating\"]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 20').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "498a8ecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1165, 1598], dtype='int64')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 20').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c16270",
   "metadata": {},
   "source": [
    "* Index 1165 - the rating is actually 13/10\n",
    "* Index 1598 - the tweet is a reply and not an actual rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "db7ae6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12\",\n",
       " 'Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 80').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad59b65",
   "metadata": {},
   "source": [
    "For both instances, the ratings were given to a group of dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4cd51560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 70').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb896dc6",
   "metadata": {},
   "source": [
    "Rating was for a group of dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eb3d87c4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 7').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f5899365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([516], dtype='int64')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 7').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e10963",
   "metadata": {},
   "source": [
    "* index 516 - 24/7 is not a rating and there was no given rating in the tweet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f1b87aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@docmisterio account started on 11/15/15']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 15').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e2d1a76f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([342], dtype='int64')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 15').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f51db8",
   "metadata": {},
   "source": [
    "* index 342 - tweet was a reply to another user and not a dog rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c4227c7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 150').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e1b0ce4",
   "metadata": {},
   "source": [
    "rating was for a group of dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e89bf4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 170').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "040b1e91",
   "metadata": {},
   "source": [
    "rating was for a group of dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "508d7d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"@jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho\"]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 0').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "97f45719",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([313], dtype='int64')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 0').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e41c68bc",
   "metadata": {},
   "source": [
    "* index 313 - This is a reply to another user and not an actual dog rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ace0d247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1\"]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 90').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a45443c8",
   "metadata": {},
   "source": [
    "rating is for a group of dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7ba765c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ\"]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 40').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a00953",
   "metadata": {},
   "source": [
    "Rating is for a group of dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6e465d23",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 130').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a63d8cd3",
   "metadata": {},
   "source": [
    "After checking the tweet on Twitter, I realized the initial rating was 121/110 but WeRateDogs changed the rating to 143/130."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a3459225",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 110').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1ee91acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1635], dtype='int64')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 110').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "042a8fe6",
   "metadata": {},
   "source": [
    "* index 1635 - This is the tweet where the initial rating was 121/110 but changed to 143/130"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "27e0ed48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible\"]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 16').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5272c30c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1663], dtype='int64')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 16').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d60c69fa",
   "metadata": {},
   "source": [
    "* index 1663 - After viewing the tweet on Twitter, I discovered that it was not a dog rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0d84ec32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq\"]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 120').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ed877be",
   "metadata": {},
   "source": [
    "Rating was for a group of dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eca50ae3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 2').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b1c722bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2335], dtype='int64')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_denominator == 2').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db5cbb5a",
   "metadata": {},
   "source": [
    "* index 2335 - The actual rating is 9/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e5522930",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      558\n",
       "11      464\n",
       "10      461\n",
       "13      351\n",
       "9       158\n",
       "8       102\n",
       "7        55\n",
       "14       54\n",
       "5        37\n",
       "6        32\n",
       "3        19\n",
       "4        17\n",
       "2         9\n",
       "1         9\n",
       "75        2\n",
       "15        2\n",
       "420       2\n",
       "0         2\n",
       "80        1\n",
       "144       1\n",
       "17        1\n",
       "26        1\n",
       "20        1\n",
       "121       1\n",
       "143       1\n",
       "44        1\n",
       "60        1\n",
       "45        1\n",
       "50        1\n",
       "99        1\n",
       "204       1\n",
       "1776      1\n",
       "165       1\n",
       "666       1\n",
       "27        1\n",
       "182       1\n",
       "24        1\n",
       "960       1\n",
       "84        1\n",
       "88        1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the distribution of numbers in the `rating_numerator` column\n",
    "df.rating_numerator.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cca54d8",
   "metadata": {},
   "source": [
    "Because some of the ratings were given only once, I decided to investigate those ratings to see if they were the correct ratings or had been extracted wrongly. Where there were no issues with the rating, I did not comment but where i discovered an issue with the rating, I commented below noting the index to be used to correct the issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1c419fd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 88').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5f07a0b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 84').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8ef4a588",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"@jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho\"]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 960').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54482245",
   "metadata": {},
   "source": [
    "Tweet is not a rating but issue has already been discovered under `rating_denominator` value counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "eb1a98e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 24').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29b22f12",
   "metadata": {},
   "source": [
    "24/7 is not the rating but issue has already been discovered in `rating_denominator` value counts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "18c71991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@markhoppus 182/10']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 182').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "50587f66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq\"]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 27').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ba981994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([763], dtype='int64')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 27').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41cc351d",
   "metadata": {},
   "source": [
    "* index 763 - correct rating is 11.27/10 not 27/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f848f02a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@s8n You tried very hard to portray this good boy as not so good, but you have ultimately failed. His goodness shines through. 666/10']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 666').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9fbc29a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 165').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ce3c81ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh\"]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 1776').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "46bdf362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 204').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "40397cee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1\"]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 99').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "108a5860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 50').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5871103",
   "metadata": {},
   "source": [
    "Actual rating is 11/10 not 50/50 but issue has already been discovered under rating_denominator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "10e86bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 45').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c374d0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa\"]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 60').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "92ab94aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ\"]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 44').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c4a45fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 143').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e55d3af7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 121').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ba6dc7bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible\"]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 20').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b52701ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 26').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d0cdb876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1712], dtype='int64')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 26').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5969ea27",
   "metadata": {},
   "source": [
    "* index 1712 - correct rating is 11.26/10 not 26/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "720b010d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@roushfenway These are good dogs but 17/10 is an emotional impulse rating. More like 13/10s']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 17').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fcbffa3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([55], dtype='int64')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 17').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb24190",
   "metadata": {},
   "source": [
    "* index 55 - rating is 13/10 not 17/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ff8ba3b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq\"]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 144').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "3e10a6bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12\"]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 80').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "cf789193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag\",\n",
       " \"PUPDATE: can't see any. Even if I could, I couldn't reach them to pet. 0/10 much disappointment https://t.co/c7WXaB2nqX\"]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 0').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "a8866895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@dhmontgomery We also gave snoop dogg a 420/10 but I think that predated your research',\n",
       " 'After so many requests... here you go.\\n\\nGood dogg. 420/10 https://t.co/yfAAo1gdeY']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 420').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "54c86248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([188, 2074], dtype='int64')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 420').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ffb4ba",
   "metadata": {},
   "source": [
    "* index 188 is a reply and not an actual rating but a mention of the rating in index 2074 therefore must be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4cb252a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RT @KibaDva: I collected all the good dogs!! 15/10 @dog_rates #GoodDogs https://t.co/6UCGFczlOI',\n",
       " '@bragg6of8 @Andy_Pace_ we are still looking for the first 15/10']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 15').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8915c15c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"RT @dog_rates: This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wu…\",\n",
       " \"This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS\"]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 75').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e94fd1a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([340, 695], dtype='int64')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 75').index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec32e028",
   "metadata": {},
   "source": [
    "* index 340 and 695 - rating is actually 9.75/10 not 75/10 as extracted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "046218ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"RT @dog_rates: Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/…\",\n",
       " 'After reading the comments I may have overestimated this pup. Downgraded to a 1/10. Please forgive me',\n",
       " \"What kind of person sends in a picture without a dog in it? 1/10 just because that's a nice table https://t.co/RDXCfk8hK0\",\n",
       " \"The millennials have spoken and we've decided to immediately demote to a 1/10. Thank you\",\n",
       " 'After 22 minutes of careful deliberation this dog is being demoted to a 1/10. The longer you look at him the more terrifying he becomes',\n",
       " \"Flamboyant pup here. Probably poisonous. Won't eat kibble. Doesn't bark. Slow af. Petting doesn't look fun. 1/10 https://t.co/jxukeh2BeO\",\n",
       " 'Never seen dog like this. Breathes heavy. Tilts head in a pattern. No bark. Shitty at fetch. Not even cordless. 1/10 https://t.co/i9iSGNn3fx',\n",
       " 'This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv',\n",
       " \"Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX\"]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 1').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bc9daed",
   "metadata": {},
   "source": [
    "In the 8th text, the rating is 9/10 not 1/2 but issue was discovered under rating_denominator vlaue counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5e56d02e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Exotic pup here. Tail long af. Throat looks swollen. Might breathe fire. Exceptionally unfluffy 2/10 would still pet https://t.co/a8SqCaSo2r',\n",
       " \"This is Crystal. She's a shitty fireman. No sense of urgency. People could be dying Crystal. 2/10 just irresponsible https://t.co/rtMtjSl9pz\",\n",
       " \"This is Henry. He's a shit dog. Short pointy ears. Leaves trail of pee. Not fluffy. Doesn't come when called. 2/10 https://t.co/Pu9RhfHDEQ\",\n",
       " \"Scary dog here. Too many legs. Extra tail. Not soft, let alone fluffy. Won't bark. Moves sideways. Has weapon. 2/10 https://t.co/XOPXCSXiUT\",\n",
       " \"This lil pup is Oliver. Hops around. Has wings but doesn't fly (lame). Annoying chirp. Won't catch tennis balls 2/10 https://t.co/DnhUw0aBM2\",\n",
       " \"This is Tedrick. He lives on the edge. Needs someone to hit the gas tho. Other than that he's a baller. 10&amp;2/10 https://t.co/LvP1TTYSCN\",\n",
       " \"Unfamiliar with this breed. Ears pointy af. Won't let go of seashell. Won't eat kibble. Not very fast. Bad dog 2/10 https://t.co/EIn5kElY1S\",\n",
       " \"This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t\",\n",
       " \"This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc\"]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 2').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d30356a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This is Fido. He can tell the weather. Not good at fetch tho. Never comes when called. 4/10 would probably still pet https://t.co/4gOv2Q3iKP',\n",
       " \"Here's another picture without a dog in it. Idk why you guys keep sending these. 4/10 just because that's a neat rug https://t.co/mOmnL19Wsl\",\n",
       " 'Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R',\n",
       " 'Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a',\n",
       " \"This is Benedict. He's a feisty pup. Needs a brushing. Portable af. Looks very angry actually. 4/10 might not pet https://t.co/3oeFfHjv0Z\",\n",
       " \"This is Keurig. He's a rare dog. Laughs like an idiot tho. Head is basically a weapon. Poorly maintained goatee 4/10 https://t.co/xOrUyj7K30\",\n",
       " \"This may be the greatest video I've ever been sent. 4/10 for Charles the puppy, 13/10 overall. (Vid by @stevenxx_) https://t.co/uaJmNgXR2P\",\n",
       " \"Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating\",\n",
       " \"This is Bobby. He doesn't give a damn about personal space. Convinced he called shotgun first. 4/10 not the best dog https://t.co/b8XW69gSaU\",\n",
       " \"This is Alice. She's an idiot. 4/10 https://t.co/VQXdwJfkyS\",\n",
       " 'This is a heavily opinionated dog. Loves walls. Nobody knows how the hair works. Always ready for a kiss. 4/10 https://t.co/dFiaKZ9cDl',\n",
       " \"Two miniature golden retrievers here. Webbed paws. Don't walk very efficiently. Can't catch a tennis ball. 4/10s https://t.co/WzVLdSHJU7\",\n",
       " \"Pink dogs here. Unreasonably long necks. Left guy has only 1 leg. Quite nimble. Don't bark tho 4/10s would still pet https://t.co/QY5uvMmmQk\",\n",
       " \"Unique dog here. Oddly shaped tail. Long pink front legs. I don't think dogs breath underwater sos. 4/10 bad owner https://t.co/0EJXxE9UxW\",\n",
       " \"Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m\",\n",
       " 'These are strange dogs. All have toupees. Long neck for dogs. In a shed of sorts? Work in groups? 4/10 still petable https://t.co/PZxSarAfSN',\n",
       " \"Cool dog. Enjoys couch. Low monotone bark. Very nice kicks. Pisses milk (must be rare). Can't go down stairs. 4/10 https://t.co/vXMKrJC81s\"]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 4').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a11639d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"This is Wesley. He's clearly trespassing. Seems rather h*ckin violent too. Weaponized forehead. 3/10 wouldn't let in https://t.co/pL7wbMRW7M\",\n",
       " \"This is Alexanderson. He's got a weird ass birth mark. Dreadful at fetch. Won't eat kibble. 3/10 wtf @Target https://t.co/FmxOpf2Sgl\",\n",
       " \"What hooligan sent in pictures w/out a dog in them? Churlish af. 3/10 just bc that's a neat fluffy bean bag chair https://t.co/wcwoGOkZvz\",\n",
       " \"This is Elliot. He's blocking the roadway. Downright rude as hell. Doesn't care that you're already late. 3/10 https://t.co/FMUxir5pYu\",\n",
       " \"This is Charl. He's a bully. Chucks that dumbbell around like its nothing. Sharp neck. Exceptionally unfluffy. 3/10 https://t.co/VfLoDZecJ7\",\n",
       " \"Meet Phil. He's big af. Currently destroying this nice family home. Completely uncalled for. 3/10 not a good pupper https://t.co/fShNNhBWYx\",\n",
       " \"This is Hammond. He's a peculiar pup. Loves long walks. Bark barely audible. Too many legs. 3/10 must be rare https://t.co/NOIiRWr5Jf\",\n",
       " \"This is Chuck. He's a neat dog. Very flexible. Trapped in a glass case of emotion. Devastatingly unfluffy 3/10 https://t.co/YqbU9xHV3p\",\n",
       " \"Extremely rare pup here. Very religious. Always praying. Too many legs. Not overwhelmingly fluffy. Won't bark. 3/10 https://t.co/REyE5YKVBb\",\n",
       " \"Meet Patrick. He's an exotic pup. Jumps great distances for a dog. Always gets injured when I toss him a ball. 3/10 https://t.co/Unz1uNrOzo\",\n",
       " \"Herd of wild dogs here. Not sure what they're trying to do. No real goals in life. 3/10 find your purpose puppers https://t.co/t5ih0VrK02\",\n",
       " \"Guys I'm getting real tired of this. We only rate dogs. Please don't send in other things like this Bulbasaur. 3/10 https://t.co/t5rQHl6W8M\",\n",
       " 'Large blue dog here. Cool shades. Flipping us off w both hands. Obviously a preteen. 3/10 for rude blue preteen pup https://t.co/mcPd5AFfhA',\n",
       " \"This is Tommy. He's a cool dog. Hard not to step on. Won't let go of seashell. Not fast by any means. 3/10 https://t.co/0gY6XTOpn3\",\n",
       " \"This is Bernie. He's taking his Halloween costume very seriously. Wants to be baked. 3/10 not a good idea Bernie smh https://t.co/1zBp1moFlX\",\n",
       " 'Fascinating dog here. Loves beach. Oddly long nose for dog. Massive ass paws. Hard to cuddle w. 3/10 would still pet https://t.co/IiSdmhkC5N',\n",
       " 'This dog resembles a baked potato. Bed looks uncomfortable. No tail. Comes with butter tho. 3/10 petting still fun https://t.co/x89NSCEZCq',\n",
       " \"My goodness. Very rare dog here. Large. Tail dangerous. Kinda fat. Only eats leaves. Doesn't come when called 3/10 https://t.co/xYGdBrMS9h\",\n",
       " \"This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0\"]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 3').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f481195c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"This is Jerry. He's doing a distinguished tongue slip. Slightly patronizing tbh. You think you're better than us, Jerry? 6/10 hold me back https://t.co/DkOBbwulw1\",\n",
       " \"This is one of the most reckless puppers I've ever seen. How she got a license in the first place is beyond me. 6/10 https://t.co/z5bAdtn9kd\",\n",
       " \"This is Kyle. He's a heavy drinker and an avid pot user. Just wants to be pupular. 6/10 I can't support this Kyle https://t.co/rRULp7XFnO\",\n",
       " \"This is Charles. He's camera shy. Tail longer than average. Doesn't look overwhelmingly fluffy. 6/10 would still pet https://t.co/rXvcElhoog\",\n",
       " \"This is Chester. He's clearly in charge of the other dogs. Weird ass paws. Not fit for fetch. 6/10 would still pet https://t.co/o2GvskrhHt\",\n",
       " \"This is Chip. He's an Upper West Nile Pantaloon. Extremely deadly. Will rip your throat out. 6/10 might still pet https://t.co/LUFnwzznaV\",\n",
       " \"This is Murphy. He's a mini golden retriever. Missing two legs (tragic). Mouth sharp. Looks rather perturbed. 6/10 https://t.co/ALO02IAKCn\",\n",
       " \"This is Eazy-E. He's colorful af. Must be rare. Submerged in Sprite (rad). Doesn't perform well when not wet. 6/10 https://t.co/UtFI7eUCjE\",\n",
       " 'Meet Brian (pronounced \"Kirk\"). He\\'s not amused by ur churlish tomfoolery. Once u put him down you\\'re done for. 6/10 https://t.co/vityMwPKKi',\n",
       " 'Gang of fearless hoofed puppers here. Straight savages. Elevated for extra terror. Front one has killed before 6/10s https://t.co/jkCb25OWfh',\n",
       " 'Say hello to Moofasa. He must be a powerful dog. Fenced in for your protection. Just got his ear pierced. 6/10 https://t.co/w6fRfQ3RXD',\n",
       " \"Meet Tango. He's a large dog. Doesn't care much for personal space. Owner isn't very accepting. Tongue slip. 6/10 https://t.co/p2T5kGebxe\",\n",
       " 'Contortionist pup here. Inside pentagram. Clearly worships Satan. Known to slowly push fragile stuff off tables 6/10 https://t.co/EX9oR55VMe',\n",
       " \"This is Karl. Karl thinks he's slick. 6/10 sneaky pup https://t.co/Lo4ALwjVh4\",\n",
       " \"This is Donny. He's summoning the demon monster Babadook. 6/10 Donny please no that won't be a good time for anyone https://t.co/kiW6Knb7Gp\",\n",
       " \"Magical floating dog here. Very calm. Always hangs by the pond. Rather moist. Good listener. 6/10 personally I'd pet https://t.co/1euKoOvy49\",\n",
       " 'Marvelous dog here. Rad ears. Not very soft. Large tumor on nose. Has a pet rock. Good w kids. 6/10 overall neat pup https://t.co/g5YkRqP0dg',\n",
       " \"This is Norman. Doesn't bark much. Very docile pup. Up to date on current events. Overall nifty pupper. 6/10 https://t.co/ntxsR98f3U\",\n",
       " 'Very fit horned dog here. Looks powerful. Not phased by wind. Great beard. Big enough to ride? 6/10 would cuddle https://t.co/wwwYO9C9kl',\n",
       " 'Interesting dog here. Very large. Purple. Manifests rainbows. Perfect teeth. No ears. Surprisingly knowledgable 6/10 https://t.co/QVaEMsB9tS',\n",
       " 'Super rare dog here. Spiffy mohawk. Sharp mouth. Shits eggs. Cool chariot wheel in background. 6/10 v confident pup https://t.co/pcx8jm1J1K',\n",
       " \"Meet Phred. He isn't steering, looking at the road, or wearing a seatbelt. Phred is a rolling tornado of danger 6/10 https://t.co/mZD7Bo7HfV\",\n",
       " \"Meet Danny. He's too good to look at the road when he's driving. Absolute menace. 6/10 completely irresponsible https://t.co/I1lMUy1FqH\",\n",
       " 'Sharp dog here. Introverted. Loves purple. Not fun to pet. Hurts to cuddle with. 6/10 still good dog tho https://t.co/Dfv2YaHPMn',\n",
       " 'Special dog here. Pretty big. Neck kinda long for dog. Cool spots. Must be a Dalmatian variant. 6/10 would still pet https://t.co/f8GXeDbFzu',\n",
       " \"This is Billl. He's trying to be a ghost but he's not very good at it. 6/10 c'mon Billl https://t.co/ero0XfdGtY\",\n",
       " \"This is Alfie. He's that one hypocritical gym teacher who made you run laps. Great posture. Cool bench. 6/10 https://t.co/GCJzm3YsfX\",\n",
       " 'Exotic dog here. Long neck. Weird paws. Obsessed with bread. Waddles. Flies sometimes (wow!). Very happy dog. 6/10 https://t.co/rqO4I3nf2N',\n",
       " 'Oh boy what a pup! Sunglasses take this one to the next level. Weirdly folds front legs. Pretty big. 6/10 https://t.co/yECbFrSArM',\n",
       " 'Unique dog here. Very small. Lives in container of Frosted Flakes (?). Short legs. Must be rare 6/10 would still pet https://t.co/XMD9CwjEnM',\n",
       " 'Here we have a well-established sunblockerspaniel. Lost his other flip-flop. 6/10 not very waterproof https://t.co/3RU6x0vHB7',\n",
       " 'This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 6').text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0d89523c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948',\n",
       " 'Who keeps sending in pictures without dogs in them? This needs to stop. 5/10 for the mediocre road https://t.co/ELqelxWMrC',\n",
       " \"Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8\",\n",
       " \"This is Dave. He's a tropical pup. Short lil legs (dachshund mix?) Excels underwater, but refuses to eat kibble 5/10 https://t.co/ZJnCxlIf62\",\n",
       " 'Please only send in dogs. This t-rex is very scary. 5/10 ...might still pet (vid by @helizabethmicha) https://t.co/Vn6w5w8TO2',\n",
       " \"When bae says they can't go out but you see them with someone else that same night. 5/10 &amp; 10/10 for heartbroken pup https://t.co/aenk0KpoWM\",\n",
       " \"Army of water dogs here. None of them know where they're going. Have no real purpose. Aggressive barks. 5/10 for all https://t.co/A88x73TwMN\",\n",
       " 'For those who claim this is a goat, u are wrong. It is not the Greatest Of All Time. The rating of 5/10 should have made that clear. Thank u',\n",
       " \"This is Jerry. He's a neat dog. No legs (tragic). Has more horns than a dog usually does. Bark is unique af. 5/10 https://t.co/85q7xlplsJ\",\n",
       " \"Here we have a basking dino pupper. Looks powerful. Occasionally shits eggs. Doesn't want the holidays to end. 5/10 https://t.co/DnNweb5eTO\",\n",
       " \"This is Jiminy. He's not the brightest dog. Needs to lay off the kibble. 5/10 still petable https://t.co/omln4LOy1x\",\n",
       " \"Unique dog here. Wrinkly as hell. Weird segmented neck. Finger on fire. Doesn't seem to notice. 5/10 might still pet https://t.co/Hy9La4xNX3\",\n",
       " \"I've been told there's a slight possibility he's checking his mirror. We'll bump to 9.5/10. Still a menace\",\n",
       " \"Meet Penelope. She's a bacon frise. Total babe (lol get it like the movie). Doesn't bark tho. 5/10 very average dog https://t.co/SDcQYg0HSZ\",\n",
       " \"This is Juckson. He's totally on his way to a nascar race. 5/10 for Juckson https://t.co/IoLRvF0Kak\",\n",
       " 'Exotic handheld dog here. Appears unathletic. Feet look deadly. Can be thrown a great distance. 5/10 might pet idk https://t.co/Avq4awulqk',\n",
       " 'This is Bubbles. He kinda resembles a fish. Always makes eye contact with u no matter what. Sneaky tongue slip. 5/10 https://t.co/Nrhvc5tLFT',\n",
       " 'Rare shielded battle dog here. Very happy about abundance of lettuce. Painfully slow fetcher. Still petable. 5/10 https://t.co/C3tlKVq7eO',\n",
       " 'This is Steven. He got locked outside. Damn it Steven. 5/10 nice grill tho https://t.co/zf7Sxxjfp3',\n",
       " 'Two gorgeous dogs here. Little waddling dog is a rebel. Refuses to look at camera. Must be a preteen. 5/10 &amp; 8/10 https://t.co/YPfw7oahbD',\n",
       " 'Rare submerged pup here. Holds breath for a long time. Frowning because that spoon ignores him. 5/10 would still pet https://t.co/EJzzNHE8bE',\n",
       " 'This is Earl. Earl is lost. Someone help Earl. He has no tags. Just trying to get home. 5/10 hang in there Earl https://t.co/1ZbfqAVDg6',\n",
       " 'Extraordinary dog here. Looks large. Just a head. No body. Rather intrusive. 5/10 would still pet https://t.co/ufHWUFA9Pu',\n",
       " \"Exotic underwater dog here. Very shy. Wont return tennis balls I toss him. Never been petted. 5/10 I bet he's soft https://t.co/WH7Nzc5IBA\",\n",
       " \"This is Brad. He's a chubby lil pup. Doesn't really need the food he's trying to reach. 5/10 you've had enough Brad https://t.co/vPXKSaNsbE\",\n",
       " 'This is Anthony. He just finished up his masters at Harvard. Unprofessional tattoos. Always looks perturbed. 5/10 https://t.co/iHLo9rGay1',\n",
       " \"This dude slaps your girl's ass what do you do?\\n5/10 https://t.co/6dioUL6gcP\",\n",
       " 'Vibrant dog here. Fabulous tail. Only 2 legs tho. Has wings but can barely fly (lame). Rather elusive. 5/10 okay pup https://t.co/cixC0M3P1e',\n",
       " \"This is Randall. He's from Chernobyl. Built playground himself. Has been stuck up there quite a while. 5/10 good dog https://t.co/pzrvc7wKGd\",\n",
       " 'Awesome dog here. Not sure where it is tho. Spectacular camouflage. Enjoys leaves. Not very soft. 5/10 still petable https://t.co/rOTOteKx4q',\n",
       " \"This is a brave dog. Excellent free climber. Trying to get closer to God. Not very loyal though. Doesn't bark. 5/10 https://t.co/ODnILTr4QM\",\n",
       " 'Two gorgeous pups here. Both have cute fake horns(adorable). Barn in the back looks on fire. 5/10 would pet rly well https://t.co/w5oYFXi0uh',\n",
       " 'Meet Zeek. He is a grey Cumulonimbus. Zeek is hungry. Someone should feed Zeek asap. 5/10 absolutely terrifying https://t.co/fvVNScw8VH',\n",
       " 'Wow. Armored dog here. Ready for battle. Face looks dangerous. Not very loyal. Lil dog on back havin a blast. 5/10 https://t.co/SyMoWrp368',\n",
       " 'RT @dogratingrating: Unoriginal idea. Blatant plagiarism. Curious grammar. -5/10 https://t.co/r7XzeQZWzb',\n",
       " 'This is Josep. He is a Rye Manganese mix. Can drive w eyes closed. Very irresponsible. Menace on the roadways. 5/10 https://t.co/XNGeDwrtYH',\n",
       " 'Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('rating_numerator == 5').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca869606",
   "metadata": {},
   "source": [
    "\n",
    "* Index 45 (first text) correct rating is 13.5/10 and not 5/10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c5bf4234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicates in the DataFrame\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7c4c92b2",
   "metadata": {
    "scrolled": false
   },
   "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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the sum of null values in each column\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "62e7c58a",
   "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>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.356000e+03</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>2356.000000</td>\n",
       "      <td>2356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.427716e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>7.720400e+17</td>\n",
       "      <td>1.241698e+16</td>\n",
       "      <td>13.126486</td>\n",
       "      <td>10.455433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.856705e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>6.236928e+16</td>\n",
       "      <td>9.599254e+16</td>\n",
       "      <td>45.876648</td>\n",
       "      <td>6.745237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>6.661041e+17</td>\n",
       "      <td>7.832140e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783989e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>7.186315e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.196279e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>7.804657e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.993373e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>8.203146e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>7.874618e+17</td>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "count  2.356000e+03           7.800000e+01         7.800000e+01   \n",
       "mean   7.427716e+17           7.455079e+17         2.014171e+16   \n",
       "std    6.856705e+16           7.582492e+16         1.252797e+17   \n",
       "min    6.660209e+17           6.658147e+17         1.185634e+07   \n",
       "25%    6.783989e+17           6.757419e+17         3.086374e+08   \n",
       "50%    7.196279e+17           7.038708e+17         4.196984e+09   \n",
       "75%    7.993373e+17           8.257804e+17         4.196984e+09   \n",
       "max    8.924206e+17           8.862664e+17         8.405479e+17   \n",
       "\n",
       "       retweeted_status_id  retweeted_status_user_id  rating_numerator  \\\n",
       "count         1.810000e+02              1.810000e+02       2356.000000   \n",
       "mean          7.720400e+17              1.241698e+16         13.126486   \n",
       "std           6.236928e+16              9.599254e+16         45.876648   \n",
       "min           6.661041e+17              7.832140e+05          0.000000   \n",
       "25%           7.186315e+17              4.196984e+09         10.000000   \n",
       "50%           7.804657e+17              4.196984e+09         11.000000   \n",
       "75%           8.203146e+17              4.196984e+09         12.000000   \n",
       "max           8.874740e+17              7.874618e+17       1776.000000   \n",
       "\n",
       "       rating_denominator  \n",
       "count         2356.000000  \n",
       "mean            10.455433  \n",
       "std              6.745237  \n",
       "min              0.000000  \n",
       "25%             10.000000  \n",
       "50%             10.000000  \n",
       "75%             10.000000  \n",
       "max            170.000000  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying descriptive statistics of the DataFrame\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc7ebec8",
   "metadata": {},
   "source": [
    "##### df_02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "07277962",
   "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>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>2070</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "2070  891327558926688256  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "2071  891689557279858688  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "2072  891815181378084864  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "2073  892177421306343426  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2074  892420643555336193  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num           p1   p1_conf  p1_dog                  p2   p2_conf  \\\n",
       "2070        2       basset  0.555712    True    English_springer  0.225770   \n",
       "2071        1  paper_towel  0.170278   False  Labrador_retriever  0.168086   \n",
       "2072        1    Chihuahua  0.716012    True            malamute  0.078253   \n",
       "2073        1    Chihuahua  0.323581    True            Pekinese  0.090647   \n",
       "2074        1       orange  0.097049   False               bagel  0.085851   \n",
       "\n",
       "      p2_dog                           p3   p3_conf  p3_dog  \n",
       "2070    True  German_short-haired_pointer  0.175219    True  \n",
       "2071    True                      spatula  0.040836   False  \n",
       "2072    True                       kelpie  0.031379    True  \n",
       "2073    True                     papillon  0.068957    True  \n",
       "2074   False                       banana  0.076110   False  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying last 5 rows in the DataFrame\n",
    "df_02.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "17168649",
   "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>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>380</th>\n",
       "      <td>673295268553605120</td>\n",
       "      <td>https://pbs.twimg.com/media/CVgGc9hWIAIe1bn.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.889241</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.064683</td>\n",
       "      <td>True</td>\n",
       "      <td>Great_Pyrenees</td>\n",
       "      <td>0.012613</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>854732716440526848</td>\n",
       "      <td>https://pbs.twimg.com/media/C9ye3b3WAAAlTo0.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.695548</td>\n",
       "      <td>True</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.058902</td>\n",
       "      <td>True</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.028411</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>669037058363662336</td>\n",
       "      <td>https://pbs.twimg.com/media/CUjlp51WcAA1vGA.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.803528</td>\n",
       "      <td>True</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.053871</td>\n",
       "      <td>True</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.032257</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>668979806671884288</td>\n",
       "      <td>https://pbs.twimg.com/media/CUixld6WoAArDrJ.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.608537</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish_setter</td>\n",
       "      <td>0.097078</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.076022</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>676430933382295552</td>\n",
       "      <td>https://pbs.twimg.com/media/CWMqV7WUYAEEClG.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.583875</td>\n",
       "      <td>True</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.203671</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.036122</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1818</th>\n",
       "      <td>834167344700198914</td>\n",
       "      <td>https://pbs.twimg.com/media/C5OOxY6WAAAxERz.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>ox</td>\n",
       "      <td>0.991682</td>\n",
       "      <td>False</td>\n",
       "      <td>bison</td>\n",
       "      <td>0.005335</td>\n",
       "      <td>False</td>\n",
       "      <td>water_buffalo</td>\n",
       "      <td>0.001130</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>725786712245440512</td>\n",
       "      <td>https://pbs.twimg.com/media/ChKDKmIWIAIJP_e.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.335761</td>\n",
       "      <td>True</td>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.167173</td>\n",
       "      <td>True</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.145715</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>666268910803644416</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>desktop_computer</td>\n",
       "      <td>0.086502</td>\n",
       "      <td>False</td>\n",
       "      <td>desk</td>\n",
       "      <td>0.085547</td>\n",
       "      <td>False</td>\n",
       "      <td>bookcase</td>\n",
       "      <td>0.079480</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>709225125749587968</td>\n",
       "      <td>https://pbs.twimg.com/media/Cdese-zWEAArIqE.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.271109</td>\n",
       "      <td>True</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.150487</td>\n",
       "      <td>True</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.145578</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>692158366030913536</td>\n",
       "      <td>https://pbs.twimg.com/media/CZsKVxfWQAAXy2u.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.956565</td>\n",
       "      <td>True</td>\n",
       "      <td>swing</td>\n",
       "      <td>0.018907</td>\n",
       "      <td>False</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.013544</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "380   673295268553605120  https://pbs.twimg.com/media/CVgGc9hWIAIe1bn.jpg   \n",
       "1917  854732716440526848  https://pbs.twimg.com/media/C9ye3b3WAAAlTo0.jpg   \n",
       "175   669037058363662336  https://pbs.twimg.com/media/CUjlp51WcAA1vGA.jpg   \n",
       "165   668979806671884288  https://pbs.twimg.com/media/CUixld6WoAArDrJ.jpg   \n",
       "516   676430933382295552  https://pbs.twimg.com/media/CWMqV7WUYAEEClG.jpg   \n",
       "1818  834167344700198914  https://pbs.twimg.com/media/C5OOxY6WAAAxERz.jpg   \n",
       "1117  725786712245440512  https://pbs.twimg.com/media/ChKDKmIWIAIJP_e.jpg   \n",
       "18    666268910803644416  https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg   \n",
       "1009  709225125749587968  https://pbs.twimg.com/media/Cdese-zWEAArIqE.jpg   \n",
       "809   692158366030913536  https://pbs.twimg.com/media/CZsKVxfWQAAXy2u.jpg   \n",
       "\n",
       "      img_num                  p1   p1_conf  p1_dog                  p2  \\\n",
       "380         1    golden_retriever  0.889241    True  Labrador_retriever   \n",
       "1917        1            Pembroke  0.695548    True            Cardigan   \n",
       "175         1           Chihuahua  0.803528    True          Pomeranian   \n",
       "165         1    golden_retriever  0.608537    True        Irish_setter   \n",
       "516         1    golden_retriever  0.583875    True      cocker_spaniel   \n",
       "1818        1                  ox  0.991682   False               bison   \n",
       "1117        1                chow  0.335761    True             Samoyed   \n",
       "18          1    desktop_computer  0.086502   False                desk   \n",
       "1009        1  Labrador_retriever  0.271109    True          Pomeranian   \n",
       "809         1                 pug  0.956565    True               swing   \n",
       "\n",
       "       p2_conf  p2_dog                  p3   p3_conf  p3_dog  \n",
       "380   0.064683    True      Great_Pyrenees  0.012613    True  \n",
       "1917  0.058902    True                chow  0.028411    True  \n",
       "175   0.053871    True                chow  0.032257    True  \n",
       "165   0.097078    True             redbone  0.076022    True  \n",
       "516   0.203671    True  Labrador_retriever  0.036122    True  \n",
       "1818  0.005335   False       water_buffalo  0.001130   False  \n",
       "1117  0.167173    True              kuvasz  0.145715    True  \n",
       "18    0.085547   False            bookcase  0.079480   False  \n",
       "1009  0.150487    True    golden_retriever  0.145578    True  \n",
       "809   0.018907   False          toy_poodle  0.013544    True  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying a sample of 10 rows in the DataFrame\n",
    "df_02.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2b7fd1a6",
   "metadata": {
    "scrolled": true
   },
   "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_02.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b889fefe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for number of duplicated columns in the DataFrame\n",
    "df_02.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "58e95cf4",
   "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>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.384514e+17</td>\n",
       "      <td>1.203855</td>\n",
       "      <td>0.594548</td>\n",
       "      <td>1.345886e-01</td>\n",
       "      <td>6.032417e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.785203e+16</td>\n",
       "      <td>0.561875</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>1.006657e-01</td>\n",
       "      <td>5.090593e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.764835e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364412</td>\n",
       "      <td>5.388625e-02</td>\n",
       "      <td>1.622240e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.119988e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588230</td>\n",
       "      <td>1.181810e-01</td>\n",
       "      <td>4.944380e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.932034e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843855</td>\n",
       "      <td>1.955655e-01</td>\n",
       "      <td>9.180755e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id      img_num      p1_conf       p2_conf       p3_conf\n",
       "count  2.075000e+03  2075.000000  2075.000000  2.075000e+03  2.075000e+03\n",
       "mean   7.384514e+17     1.203855     0.594548  1.345886e-01  6.032417e-02\n",
       "std    6.785203e+16     0.561875     0.271174  1.006657e-01  5.090593e-02\n",
       "min    6.660209e+17     1.000000     0.044333  1.011300e-08  1.740170e-10\n",
       "25%    6.764835e+17     1.000000     0.364412  5.388625e-02  1.622240e-02\n",
       "50%    7.119988e+17     1.000000     0.588230  1.181810e-01  4.944380e-02\n",
       "75%    7.932034e+17     1.000000     0.843855  1.955655e-01  9.180755e-02\n",
       "max    8.924206e+17     4.000000     1.000000  4.880140e-01  2.734190e-01"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying descriptive statistics\n",
    "df_02.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3c8c3b15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5945482636144582"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average confidence level of the p1 algorithm\n",
    "df_02.p1_conf.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "26a23138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13458860950039184"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average confidence level of the p2 algorithm\n",
    "df_02.p2_conf.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "aad9f2d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06032416861810644"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average confidence level of the p1 algorithm\n",
    "df_02.p3_conf.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d0329e9",
   "metadata": {},
   "source": [
    "The average confidence level of the first algorithm (`p1`) is relatively higher than the other two algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "885fd054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     1532\n",
       "False     543\n",
       "Name: p1_dog, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_02.p1_dog.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "17624bfb",
   "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>6</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>box_turtle</td>\n",
       "      <td>0.933012</td>\n",
       "      <td>False</td>\n",
       "      <td>mud_turtle</td>\n",
       "      <td>0.045885</td>\n",
       "      <td>False</td>\n",
       "      <td>terrapin</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>shopping_cart</td>\n",
       "      <td>0.962465</td>\n",
       "      <td>False</td>\n",
       "      <td>shopping_basket</td>\n",
       "      <td>0.014594</td>\n",
       "      <td>False</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.007959</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>hen</td>\n",
       "      <td>0.965932</td>\n",
       "      <td>False</td>\n",
       "      <td>cock</td>\n",
       "      <td>0.033919</td>\n",
       "      <td>False</td>\n",
       "      <td>partridge</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>666268910803644416</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>desktop_computer</td>\n",
       "      <td>0.086502</td>\n",
       "      <td>False</td>\n",
       "      <td>desk</td>\n",
       "      <td>0.085547</td>\n",
       "      <td>False</td>\n",
       "      <td>bookcase</td>\n",
       "      <td>0.079480</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>three-toed_sloth</td>\n",
       "      <td>0.914671</td>\n",
       "      <td>False</td>\n",
       "      <td>otter</td>\n",
       "      <td>0.015250</td>\n",
       "      <td>False</td>\n",
       "      <td>great_grey_owl</td>\n",
       "      <td>0.013207</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>882045870035918850</td>\n",
       "      <td>https://pbs.twimg.com/media/DD2oCl2WAAEI_4a.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>web_site</td>\n",
       "      <td>0.949591</td>\n",
       "      <td>False</td>\n",
       "      <td>dhole</td>\n",
       "      <td>0.017326</td>\n",
       "      <td>False</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.006941</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>886680336477933568</td>\n",
       "      <td>https://pbs.twimg.com/media/DE4fEDzWAAAyHMM.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>convertible</td>\n",
       "      <td>0.738995</td>\n",
       "      <td>False</td>\n",
       "      <td>sports_car</td>\n",
       "      <td>0.139952</td>\n",
       "      <td>False</td>\n",
       "      <td>car_wheel</td>\n",
       "      <td>0.044173</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2052</th>\n",
       "      <td>887517139158093824</td>\n",
       "      <td>https://pbs.twimg.com/ext_tw_video_thumb/88751...</td>\n",
       "      <td>1</td>\n",
       "      <td>limousine</td>\n",
       "      <td>0.130432</td>\n",
       "      <td>False</td>\n",
       "      <td>tow_truck</td>\n",
       "      <td>0.029175</td>\n",
       "      <td>False</td>\n",
       "      <td>shopping_cart</td>\n",
       "      <td>0.026321</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>543 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                            jpg_url  \\\n",
       "6     666051853826850816    https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg   \n",
       "8     666057090499244032    https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg   \n",
       "17    666104133288665088    https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg   \n",
       "18    666268910803644416    https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg   \n",
       "21    666293911632134144    https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg   \n",
       "...                  ...                                                ...   \n",
       "2026  882045870035918850    https://pbs.twimg.com/media/DD2oCl2WAAEI_4a.jpg   \n",
       "2046  886680336477933568    https://pbs.twimg.com/media/DE4fEDzWAAAyHMM.jpg   \n",
       "2052  887517139158093824  https://pbs.twimg.com/ext_tw_video_thumb/88751...   \n",
       "2071  891689557279858688    https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "2074  892420643555336193    https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num                p1   p1_conf  p1_dog                  p2  \\\n",
       "6           1        box_turtle  0.933012   False          mud_turtle   \n",
       "8           1     shopping_cart  0.962465   False     shopping_basket   \n",
       "17          1               hen  0.965932   False                cock   \n",
       "18          1  desktop_computer  0.086502   False                desk   \n",
       "21          1  three-toed_sloth  0.914671   False               otter   \n",
       "...       ...               ...       ...     ...                 ...   \n",
       "2026        1          web_site  0.949591   False               dhole   \n",
       "2046        1       convertible  0.738995   False          sports_car   \n",
       "2052        1         limousine  0.130432   False           tow_truck   \n",
       "2071        1       paper_towel  0.170278   False  Labrador_retriever   \n",
       "2074        1            orange  0.097049   False               bagel   \n",
       "\n",
       "       p2_conf  p2_dog                p3   p3_conf  p3_dog  \n",
       "6     0.045885   False          terrapin  0.017885   False  \n",
       "8     0.014594   False  golden_retriever  0.007959    True  \n",
       "17    0.033919   False         partridge  0.000052   False  \n",
       "18    0.085547   False          bookcase  0.079480   False  \n",
       "21    0.015250   False    great_grey_owl  0.013207   False  \n",
       "...        ...     ...               ...       ...     ...  \n",
       "2026  0.017326   False  golden_retriever  0.006941    True  \n",
       "2046  0.139952   False         car_wheel  0.044173   False  \n",
       "2052  0.029175   False     shopping_cart  0.026321   False  \n",
       "2071  0.168086    True           spatula  0.040836   False  \n",
       "2074  0.085851   False            banana  0.076110   False  \n",
       "\n",
       "[543 rows x 12 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for tweet IDs the p1 algorithm as not being dogs\n",
    "df_02.query('p1_dog == False')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d1563ab",
   "metadata": {},
   "source": [
    "A total of 543 tweets where flagged by the algorithm as not being dogs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c53b1cba",
   "metadata": {},
   "source": [
    "##### df_03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "6ccdcdae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>6962</td>\n",
       "      <td>33652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>5267</td>\n",
       "      <td>29208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>3463</td>\n",
       "      <td>21959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>7189</td>\n",
       "      <td>36763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>7709</td>\n",
       "      <td>35120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id  retweet_count  favorite_count\n",
       "0  892420643555336193           6962           33652\n",
       "1  892177421306343426           5267           29208\n",
       "2  891815181378084864           3463           21959\n",
       "3  891689557279858688           7189           36763\n",
       "4  891327558926688256           7709           35120"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying the first 5 rows\n",
    "df_03.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c496a48d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2327 entries, 0 to 2326\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   id              2327 non-null   int64\n",
      " 1   retweet_count   2327 non-null   int64\n",
      " 2   favorite_count  2327 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 54.7 KB\n"
     ]
    }
   ],
   "source": [
    "df_03.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "0a6d6a93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for number of duplicates\n",
    "df_03.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "16aa854c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.327000e+03</td>\n",
       "      <td>2327.000000</td>\n",
       "      <td>2327.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.417930e+17</td>\n",
       "      <td>2456.647185</td>\n",
       "      <td>7019.900731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.820795e+16</td>\n",
       "      <td>4159.638809</td>\n",
       "      <td>10907.925731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.781394e+17</td>\n",
       "      <td>492.500000</td>\n",
       "      <td>1218.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.178418e+17</td>\n",
       "      <td>1143.000000</td>\n",
       "      <td>3033.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.986547e+17</td>\n",
       "      <td>2843.500000</td>\n",
       "      <td>8554.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>70243.000000</td>\n",
       "      <td>144106.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  retweet_count  favorite_count\n",
       "count  2.327000e+03    2327.000000     2327.000000\n",
       "mean   7.417930e+17    2456.647185     7019.900731\n",
       "std    6.820795e+16    4159.638809    10907.925731\n",
       "min    6.660209e+17       1.000000        0.000000\n",
       "25%    6.781394e+17     492.500000     1218.000000\n",
       "50%    7.178418e+17    1143.000000     3033.000000\n",
       "75%    7.986547e+17    2843.500000     8554.500000\n",
       "max    8.924206e+17   70243.000000   144106.000000"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics of the DataFrame\n",
    "df_03.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abbf731a",
   "metadata": {},
   "source": [
    "### QUALITY ISSUES\n",
    "\n",
    "#### df\n",
    "* Dataframe contains retweets\n",
    "* Some tweets have double dog stages for one dog\n",
    "* Missing values in the `in_reply_to_status_id` column\n",
    "* Missing values in the `in_reply_to_user_id` column\n",
    "* HTML tag in the `source` column\n",
    "* Missing values in the `retweeted_status_id` column\n",
    "* Missing values in the `retweeted_status_timestamp` column\n",
    "* Missing values represented as `None` in the `name` column\n",
    "* Missing values in the `expanded_urls` column\n",
    "* Data type of `timestamps` column is string\n",
    "* `rating_denominator` column has values less than or more than 10\n",
    "* irrelevant columns(`in_reply_to_status_id`,`in_reply_to_user_id`, `source`, `retweeted_status_id`,`retweeted_status_user_id`, `retweeted_status_timestamp`, `expanded_urls`, `name`)\n",
    "\n",
    "\n",
    "#### df_02\n",
    "* Some values in the `p1`, `p2_` and `p3` columns are capitalized while others are not\n",
    "* The confidence level of the 2nd(`p2_conf`) and 3rd(`p3_conf`) algorithms are relatively lower than the 1st algorithm(`p1_conf`)\n",
    "* According to the p1 algorithm some tweets are not dogs hence `False` in the `p1_dog` column\n",
    "* `jpg_url` column is irrelevant \n",
    "\n",
    "#### df_03\n",
    "* tweet IDs column is labeled as `id`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5859745",
   "metadata": {},
   "source": [
    "### Tidiness issues\n",
    "* dog stage is one variable but it is spread across multiple columns - `doggo`,`pupper`,`floofer`,`puppo` in the `df` dataframe\n",
    "* information about one observational unit (tweets) is contained in the 3 different dataframes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b6ac46c",
   "metadata": {},
   "source": [
    "<a id='clean'></a>\n",
    "## Cleaning Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3037d647",
   "metadata": {},
   "source": [
    "#### COPYING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f18c52a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a copy of each DataFrame\n",
    "df_clean = copy.deepcopy(df)\n",
    "df_02_clean = copy.deepcopy(df_02)\n",
    "df_03_clean = copy.deepcopy(df_03)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8043ceea",
   "metadata": {},
   "source": [
    "### Issue:\n",
    "* double dog stages instead of one\n",
    "* wrong rating numerator and denominator\n",
    "* tweets with ratings that were not dog ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52bf3b25",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "* Drop index of tweets identified to not be dog ratings\n",
    "* Replace wrong dog stages with `None`\n",
    "* Replace wrong rating numerator and denominator with correct figures obtained from the text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da393ac4",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cef51ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean.drop([784,1598,342,313,516,1635,1663,188],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "23d0438a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# correcting double dog stages that should have been 1\n",
    "df_clean.at[200,'doggo'] = \"None\"\n",
    "df_clean.at[460,'doggo'] = \"None\"\n",
    "df_clean.at[575,'doggo'] = \"None\"\n",
    "df_clean.at[705,'pupper'] = \"None\"\n",
    "df_clean.at[956,'doggo'] = \"None\"\n",
    "df_clean.at[956,'pupper'] = \"None\"\n",
    "df_clean.at[191,'doggo'] = \"None\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "5368b531",
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing the rating_denominator to reflect the correct rating\n",
    "index = [1068,1662,1202,1165,2335]\n",
    "for i in index:\n",
    "    df_clean.at[i,'rating_denominator']=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "48c7cc01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# correcting rating_numerator\n",
    "df_clean.at[1068,'rating_numerator'] = 14\n",
    "df_clean.at[1662,'rating_numerator'] = 10\n",
    "df_clean.at[1202,'rating_numerator'] = 11\n",
    "df_clean.at[1165,'rating_numerator'] = 13\n",
    "df_clean.at[2335,'rating_numerator'] = 9\n",
    "df_clean.at[763, 'rating_numerator'] = 11.27\n",
    "df_clean.at[1712,'rating_numerator'] = 11.26\n",
    "df_clean.at[55,  'rating_numerator'] = 13\n",
    "df_clean.at[340, 'rating_numerator'] = 9.75\n",
    "df_clean.at[695, 'rating_numerator'] = 9.75\n",
    "df_clean.at[45,  'rating_numerator'] = 13.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3affec",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "39940783",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['rating_numerator'].equals(df['rating_numerator'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "46e3851e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['rating_denominator'].equals(df['rating_denominator'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed7ece1e",
   "metadata": {},
   "source": [
    "### Issue :\n",
    "*  `df` DataFrame contains retweets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f18bd8",
   "metadata": {},
   "source": [
    "#### Define\n",
    "* drop rows where `retweeted_status_id` column is not null by filtering for the index of those rows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f12c0e1",
   "metadata": {},
   "source": [
    "Note: Tweet IDs with non null values in `retweeted_status_id`, `retweeted_status_user_id` and `retweeted_status_timestamp` are retweets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63820328",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "706f0eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping rows using index of rows where the `retweeted_status_id` column is not null\n",
    "df_clean.drop(df_clean.index[df_clean['retweeted_status_id'].notnull()], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0048ea20",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "6852737d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2168 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2168 non-null   int64  \n",
      " 1   in_reply_to_status_id       73 non-null     float64\n",
      " 2   in_reply_to_user_id         73 non-null     float64\n",
      " 3   timestamp                   2168 non-null   object \n",
      " 4   source                      2168 non-null   object \n",
      " 5   text                        2168 non-null   object \n",
      " 6   retweeted_status_id         0 non-null      float64\n",
      " 7   retweeted_status_user_id    0 non-null      float64\n",
      " 8   retweeted_status_timestamp  0 non-null      object \n",
      " 9   expanded_urls               2115 non-null   object \n",
      " 10  rating_numerator            2168 non-null   float64\n",
      " 11  rating_denominator          2168 non-null   int64  \n",
      " 12  name                        2168 non-null   object \n",
      " 13  doggo                       2168 non-null   object \n",
      " 14  floofer                     2168 non-null   object \n",
      " 15  pupper                      2168 non-null   object \n",
      " 16  puppo                       2168 non-null   object \n",
      "dtypes: float64(5), int64(2), object(10)\n",
      "memory usage: 304.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93481dfc",
   "metadata": {},
   "source": [
    "0 non-null values in the `retweeted_status_id`,`retweeted_status_user` and `retweeted_status_timestamp` columns confirms that retweets have been dropped"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe4cf7b9",
   "metadata": {},
   "source": [
    "### Issue: \n",
    "* dog stage is one variable but it is spread across multiple columns - `doggo`,`pupper`,`floofer`,`puppo` in the `df` dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3521e0",
   "metadata": {},
   "source": [
    "#### Define\n",
    "* Replace `None` in those columns with empty strings\n",
    "* Combine values in four columns into one column\n",
    "* Replace multiple dog stages with `multiple_stages`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb508d6",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3d42fb1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing `None` in the columns with NaN\n",
    "df_clean.doggo = df_clean.doggo.replace('None','')\n",
    "df_clean.pupper = df_clean.pupper.replace('None','')\n",
    "df_clean.floofer = df_clean.floofer.replace('None','')\n",
    "df_clean.puppo = df_clean.puppo.replace('None','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "9f945945",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating new column by combining string values in four columns\n",
    "df_clean['dog_stage'] = df_clean['doggo'] + df_clean['floofer'] + df_clean['pupper'] + df_clean['puppo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "f392e9ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               1825\n",
       "pupper          226\n",
       "doggo            76\n",
       "puppo            25\n",
       "floofer          10\n",
       "doggopupper       6\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a4f2cc",
   "metadata": {},
   "source": [
    "Empty strings are captured as values and stage names are joined where there were two stages in the tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3124f4fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# index of rows with double dog stages\n",
    "a = df_clean.query('dog_stage == \"doggopupper\"').index\n",
    "b = df_clean.query('dog_stage == \"doggopuppo\"').index\n",
    "c = df_clean.query('dog_stage == \"doggofloofer\"').index\n",
    "# appending the indexes to one variable\n",
    "index = a.append(b).append(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f49842e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# looping through the index and replacing double dog stage names\n",
    "for i in index:\n",
    "    df_clean.at[i,'dog_stage'] = 'multiple_stages'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8e15a7b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   1825\n",
       "pupper              226\n",
       "doggo                76\n",
       "puppo                25\n",
       "floofer              10\n",
       "multiple_stages       6\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "5e72c1e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the four columns that were merged into one\n",
    "df_clean.drop(['doggo','floofer','pupper','puppo'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8172b70",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "eb05f30e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   1825\n",
       "pupper              226\n",
       "doggo                76\n",
       "puppo                25\n",
       "floofer              10\n",
       "multiple_stages       6\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b9d7b7",
   "metadata": {},
   "source": [
    "Empty strings are regarded as values hence why they appear in the value counts however as stated earlier, these may be transformed into NaNs as the analysis progresses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ebd480",
   "metadata": {},
   "source": [
    "### Issue: \n",
    "\n",
    "* data type of `timestamp` column is string instead of datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cbd5ecc",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "* convert dtype of `timestamp` column to datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a85abaf",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "d463a823",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting column dtype to datetime\n",
    "df_clean['timestamp'] = pd.to_datetime(df['timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5819214d",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "b8598665",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2168 entries, 0 to 2355\n",
      "Data columns (total 14 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2168 non-null   int64              \n",
      " 1   in_reply_to_status_id       73 non-null     float64            \n",
      " 2   in_reply_to_user_id         73 non-null     float64            \n",
      " 3   timestamp                   2168 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2168 non-null   object             \n",
      " 5   text                        2168 non-null   object             \n",
      " 6   retweeted_status_id         0 non-null      float64            \n",
      " 7   retweeted_status_user_id    0 non-null      float64            \n",
      " 8   retweeted_status_timestamp  0 non-null      object             \n",
      " 9   expanded_urls               2115 non-null   object             \n",
      " 10  rating_numerator            2168 non-null   float64            \n",
      " 11  rating_denominator          2168 non-null   int64              \n",
      " 12  name                        2168 non-null   object             \n",
      " 13  dog_stage                   2168 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(5), int64(2), object(6)\n",
      "memory usage: 318.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2e52aa2",
   "metadata": {},
   "source": [
    "### Issues:\n",
    "* Missing values in the `in_reply_to_status_id` column\n",
    "* Missing values in the `in_reply_to_user_id` column\n",
    "* HTML tag in the `source` column\n",
    "* Missing values in the `retweeted_status_id` column\n",
    "* Missing values in the `retweeted_status_timestamp` column\n",
    "* Missing values represented as `None` in the `name` column\n",
    "* Missing values in the `expanded_urls` column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddfda000",
   "metadata": {},
   "source": [
    "Note: These issues can be accessed and cleaned individually but as they are not relevant to the analysis, they were just dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c354dece",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "\n",
    "* Drop columns with missing values and html tags"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36656b9e",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "930b51ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the columns listed above\n",
    "columns = ['in_reply_to_status_id', 'in_reply_to_user_id','source',\n",
    "           'retweeted_status_id','retweeted_status_user_id',\n",
    "           'retweeted_status_timestamp','expanded_urls','name']\n",
    "df_clean.drop(columns,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2bc5dd",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9474826f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'text', 'rating_numerator',\n",
       "       'rating_denominator', 'dog_stage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff85d797",
   "metadata": {},
   "source": [
    "### Issue: \n",
    "* Some values in the `p1`, `p2` and `p3` columns are capitalized while others are not"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efa8678",
   "metadata": {},
   "source": [
    "#### Define\n",
    "* Convert all values in the three columns to lowercase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbcceb92",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "2ac97c6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_02_clean['p1'] = df_02_clean.p1.str.lower()\n",
    "df_02_clean['p2'] = df_02_clean.p2.str.lower()\n",
    "df_02_clean['p3'] = df_02_clean.p3.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9288023",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "621a0691",
   "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>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>1176</th>\n",
       "      <td>737678689543020544</td>\n",
       "      <td>https://pbs.twimg.com/media/CjzC2oGWYAAyIfG.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pembroke</td>\n",
       "      <td>0.935307</td>\n",
       "      <td>True</td>\n",
       "      <td>cardigan</td>\n",
       "      <td>0.049874</td>\n",
       "      <td>True</td>\n",
       "      <td>chihuahua</td>\n",
       "      <td>0.011603</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>666437273139982337</td>\n",
       "      <td>https://pbs.twimg.com/media/CT-pKmRWIAAxUWj.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>chihuahua</td>\n",
       "      <td>0.671853</td>\n",
       "      <td>True</td>\n",
       "      <td>beagle</td>\n",
       "      <td>0.124680</td>\n",
       "      <td>True</td>\n",
       "      <td>saluki</td>\n",
       "      <td>0.044094</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>669923323644657664</td>\n",
       "      <td>https://pbs.twimg.com/media/CUwLtPeU8AAfAb2.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>car_mirror</td>\n",
       "      <td>0.343063</td>\n",
       "      <td>False</td>\n",
       "      <td>seat_belt</td>\n",
       "      <td>0.110289</td>\n",
       "      <td>False</td>\n",
       "      <td>wing</td>\n",
       "      <td>0.080148</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>826204788643753985</td>\n",
       "      <td>https://pbs.twimg.com/media/C3dEza1WcAAhlNU.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>labrador_retriever</td>\n",
       "      <td>0.782058</td>\n",
       "      <td>True</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.156581</td>\n",
       "      <td>True</td>\n",
       "      <td>soft-coated_wheaten_terrier</td>\n",
       "      <td>0.007275</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>696877980375769088</td>\n",
       "      <td>https://pbs.twimg.com/media/CavO0uuWEAE96Ed.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>space_heater</td>\n",
       "      <td>0.206876</td>\n",
       "      <td>False</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.123450</td>\n",
       "      <td>False</td>\n",
       "      <td>vacuum</td>\n",
       "      <td>0.119218</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>677687604918272002</td>\n",
       "      <td>https://pbs.twimg.com/media/CWehRdEWIAAySyO.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pembroke</td>\n",
       "      <td>0.573047</td>\n",
       "      <td>True</td>\n",
       "      <td>sunglasses</td>\n",
       "      <td>0.126758</td>\n",
       "      <td>False</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.108047</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>678334497360859136</td>\n",
       "      <td>https://pbs.twimg.com/media/CWntoDVWcAEl3NB.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>norfolk_terrier</td>\n",
       "      <td>0.378643</td>\n",
       "      <td>True</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.095594</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.085309</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>673148804208660480</td>\n",
       "      <td>https://pbs.twimg.com/media/CVeBQwiUsAAqhLw.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>tub</td>\n",
       "      <td>0.873010</td>\n",
       "      <td>False</td>\n",
       "      <td>bathtub</td>\n",
       "      <td>0.091434</td>\n",
       "      <td>False</td>\n",
       "      <td>toilet_seat</td>\n",
       "      <td>0.025456</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>862457590147678208</td>\n",
       "      <td>https://pbs.twimg.com/media/C_gQmaTUMAAPYSS.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>home_theater</td>\n",
       "      <td>0.496348</td>\n",
       "      <td>False</td>\n",
       "      <td>studio_couch</td>\n",
       "      <td>0.167256</td>\n",
       "      <td>False</td>\n",
       "      <td>barber_chair</td>\n",
       "      <td>0.052625</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>719704490224398336</td>\n",
       "      <td>https://pbs.twimg.com/media/CfznaXuUsAAH-py.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>home_theater</td>\n",
       "      <td>0.059033</td>\n",
       "      <td>False</td>\n",
       "      <td>window_shade</td>\n",
       "      <td>0.038299</td>\n",
       "      <td>False</td>\n",
       "      <td>bathtub</td>\n",
       "      <td>0.035528</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "1176  737678689543020544  https://pbs.twimg.com/media/CjzC2oGWYAAyIfG.jpg   \n",
       "35    666437273139982337  https://pbs.twimg.com/media/CT-pKmRWIAAxUWj.jpg   \n",
       "205   669923323644657664  https://pbs.twimg.com/media/CUwLtPeU8AAfAb2.jpg   \n",
       "1762  826204788643753985  https://pbs.twimg.com/media/C3dEza1WcAAhlNU.jpg   \n",
       "857   696877980375769088  https://pbs.twimg.com/media/CavO0uuWEAE96Ed.jpg   \n",
       "557   677687604918272002  https://pbs.twimg.com/media/CWehRdEWIAAySyO.jpg   \n",
       "566   678334497360859136  https://pbs.twimg.com/media/CWntoDVWcAEl3NB.jpg   \n",
       "376   673148804208660480  https://pbs.twimg.com/media/CVeBQwiUsAAqhLw.jpg   \n",
       "1946  862457590147678208  https://pbs.twimg.com/media/C_gQmaTUMAAPYSS.jpg   \n",
       "1093  719704490224398336  https://pbs.twimg.com/media/CfznaXuUsAAH-py.jpg   \n",
       "\n",
       "      img_num                  p1   p1_conf  p1_dog                p2  \\\n",
       "1176        1            pembroke  0.935307    True          cardigan   \n",
       "35          1           chihuahua  0.671853    True            beagle   \n",
       "205         1          car_mirror  0.343063   False         seat_belt   \n",
       "1762        2  labrador_retriever  0.782058    True  golden_retriever   \n",
       "857         1        space_heater  0.206876   False           spatula   \n",
       "557         1            pembroke  0.573047    True        sunglasses   \n",
       "566         1     norfolk_terrier  0.378643    True  golden_retriever   \n",
       "376         1                 tub  0.873010   False           bathtub   \n",
       "1946        1        home_theater  0.496348   False      studio_couch   \n",
       "1093        1        home_theater  0.059033   False      window_shade   \n",
       "\n",
       "       p2_conf  p2_dog                           p3   p3_conf  p3_dog  \n",
       "1176  0.049874    True                    chihuahua  0.011603    True  \n",
       "35    0.124680    True                       saluki  0.044094    True  \n",
       "205   0.110289   False                         wing  0.080148   False  \n",
       "1762  0.156581    True  soft-coated_wheaten_terrier  0.007275    True  \n",
       "857   0.123450   False                       vacuum  0.119218   False  \n",
       "557   0.126758   False             golden_retriever  0.108047    True  \n",
       "566   0.095594    True                       kelpie  0.085309    True  \n",
       "376   0.091434   False                  toilet_seat  0.025456   False  \n",
       "1946  0.167256   False                 barber_chair  0.052625   False  \n",
       "1093  0.038299   False                      bathtub  0.035528   False  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_02_clean.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c748682",
   "metadata": {},
   "source": [
    "### Issue:\n",
    "* the confidence level of the 2nd(`p2_conf`) and 3rd(`p3_conf`) algorithms are relatively lower than the 1st algorithm(`p1_conf`)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e34a3852",
   "metadata": {},
   "source": [
    "#### Define\n",
    "* drop `p2`,`p2_conf`,`p2_dog` and `p3`,`p3_conf`,`p3_dog`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d27fad4",
   "metadata": {},
   "source": [
    "NB: I dropped these columns because I decided to use the predictions of the first algorithm since its confidence levels were higher than the other two algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76f3000",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "a5a36a9f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_02_clean.drop(['p2','p2_conf','p2_dog','p3','p3_conf','p3_dog'],\n",
    "        axis=1,\n",
    "        inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a99ea9f",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "e5075685",
   "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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0        1  welsh_springer_spaniel  0.465074    True  \n",
       "1        1                 redbone  0.506826    True  \n",
       "2        1         german_shepherd  0.596461    True  \n",
       "3        1     rhodesian_ridgeback  0.408143    True  \n",
       "4        1      miniature_pinscher  0.560311    True  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_02_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3838b3a7",
   "metadata": {},
   "source": [
    "### Issue:\n",
    "* According to the p1 algorithm some tweets are not dogs hence `False` in the `p1_dog` column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1085039",
   "metadata": {},
   "source": [
    "#### Define\n",
    "* drop all rows where `p1_dog` is False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991c1a64",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "83c27762",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_02_clean.drop(df_02_clean[df_02_clean.p1_dog == False].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "b0913fdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    1532\n",
       "Name: p1_dog, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_02_clean.p1_dog.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1228e4",
   "metadata": {},
   "source": [
    "Note: since the tweets marked as not dogs by the algorithm have been dropped leaving only tweets with True in the p1_dog column, the `p1_dog` column can be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "1b7db006",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_02_clean.drop('p1_dog',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c1bf14",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "bf9c6014",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1811</th>\n",
       "      <td>832998151111966721</td>\n",
       "      <td>https://pbs.twimg.com/media/C49nZavUYAEJjGw.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>boxer</td>\n",
       "      <td>0.539036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>674038233588723717</td>\n",
       "      <td>https://pbs.twimg.com/media/CVqqMtiVEAEye_L.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>eskimo_dog</td>\n",
       "      <td>0.358459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>750056684286914561</td>\n",
       "      <td>https://pbs.twimg.com/media/Cmfx2oNW8AAGg4H.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>saluki</td>\n",
       "      <td>0.484428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540</th>\n",
       "      <td>790987426131050500</td>\n",
       "      <td>https://pbs.twimg.com/media/Cvom3ZJXEAE29TD.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.349195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>690015576308211712</td>\n",
       "      <td>https://pbs.twimg.com/media/CZNtgWhWkAAbq3W.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.949609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "1811  832998151111966721  https://pbs.twimg.com/media/C49nZavUYAEJjGw.jpg   \n",
       "419   674038233588723717  https://pbs.twimg.com/media/CVqqMtiVEAEye_L.jpg   \n",
       "1275  750056684286914561  https://pbs.twimg.com/media/Cmfx2oNW8AAGg4H.jpg   \n",
       "1540  790987426131050500  https://pbs.twimg.com/media/Cvom3ZJXEAE29TD.jpg   \n",
       "783   690015576308211712  https://pbs.twimg.com/media/CZNtgWhWkAAbq3W.jpg   \n",
       "\n",
       "      img_num              p1   p1_conf  \n",
       "1811        1           boxer  0.539036  \n",
       "419         1      eskimo_dog  0.358459  \n",
       "1275        1          saluki  0.484428  \n",
       "1540        1  cocker_spaniel  0.349195  \n",
       "783         2        malamute  0.949609  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_02_clean.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04b869e7",
   "metadata": {},
   "source": [
    "#### `jpg_url` column is irrelevant\n",
    "\n",
    "#### Define\n",
    "* drop `jpg_url` column from dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa04496",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "93585060",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_02_clean.drop('jpg_url',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a6388fa",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "022d4189",
   "metadata": {
    "scrolled": false
   },
   "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>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>720415127506415616</td>\n",
       "      <td>1</td>\n",
       "      <td>rottweiler</td>\n",
       "      <td>0.990312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>846042936437604353</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.961110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>685663452032069632</td>\n",
       "      <td>1</td>\n",
       "      <td>chesapeake_bay_retriever</td>\n",
       "      <td>0.171174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>777189768882946048</td>\n",
       "      <td>2</td>\n",
       "      <td>chihuahua</td>\n",
       "      <td>0.988412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>773704687002451968</td>\n",
       "      <td>2</td>\n",
       "      <td>silky_terrier</td>\n",
       "      <td>0.324251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>810284430598270976</td>\n",
       "      <td>1</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.620768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1739</th>\n",
       "      <td>822462944365645825</td>\n",
       "      <td>3</td>\n",
       "      <td>pomeranian</td>\n",
       "      <td>0.960199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>744234799360020481</td>\n",
       "      <td>1</td>\n",
       "      <td>labrador_retriever</td>\n",
       "      <td>0.825333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>672640509974827008</td>\n",
       "      <td>1</td>\n",
       "      <td>chesapeake_bay_retriever</td>\n",
       "      <td>0.420155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>679828447187857408</td>\n",
       "      <td>3</td>\n",
       "      <td>chihuahua</td>\n",
       "      <td>0.346545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  img_num                        p1   p1_conf\n",
       "1099  720415127506415616        1                rottweiler  0.990312\n",
       "1877  846042936437604353        1          golden_retriever  0.961110\n",
       "717   685663452032069632        1  chesapeake_bay_retriever  0.171174\n",
       "1454  777189768882946048        2                 chihuahua  0.988412\n",
       "1435  773704687002451968        2             silky_terrier  0.324251\n",
       "1655  810284430598270976        1                  malamute  0.620768\n",
       "1739  822462944365645825        3                pomeranian  0.960199\n",
       "1221  744234799360020481        1        labrador_retriever  0.825333\n",
       "360   672640509974827008        1  chesapeake_bay_retriever  0.420155\n",
       "602   679828447187857408        3                 chihuahua  0.346545"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_02_clean.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82a2197b",
   "metadata": {},
   "source": [
    "### Issue:\n",
    "* tweet IDs column is labeled as `id`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31bc061e",
   "metadata": {},
   "source": [
    "#### Define\n",
    "* rename `id` column to `tweet_id`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986e7d42",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "81d6e6e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming id column to tweet_id\n",
    "df_03_clean.rename(columns={'id':'tweet_id'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bde5128",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "69cdb173",
   "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>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>6962</td>\n",
       "      <td>33652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>5267</td>\n",
       "      <td>29208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>3463</td>\n",
       "      <td>21959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>7189</td>\n",
       "      <td>36763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>7709</td>\n",
       "      <td>35120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  retweet_count  favorite_count\n",
       "0  892420643555336193           6962           33652\n",
       "1  892177421306343426           5267           29208\n",
       "2  891815181378084864           3463           21959\n",
       "3  891689557279858688           7189           36763\n",
       "4  891327558926688256           7709           35120"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_03_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2031f621",
   "metadata": {},
   "source": [
    "### Issue:\n",
    "* information about one observational unit (tweets) is contained in the 3 different dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ff52e22",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "* merge cleaned dataframes into one based on the observational unit (`tweet_id`)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f01b9a57",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "01d4fe36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging cleaned DataFrames into a master DataFrame\n",
    "master_df =  pd.merge(pd.merge(df_clean,df_02_clean,on='tweet_id'),df_03_clean,on='tweet_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1703df37",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "922e2b32",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1467 entries, 0 to 1466\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            1467 non-null   int64              \n",
      " 1   timestamp           1467 non-null   datetime64[ns, UTC]\n",
      " 2   text                1467 non-null   object             \n",
      " 3   rating_numerator    1467 non-null   float64            \n",
      " 4   rating_denominator  1467 non-null   int64              \n",
      " 5   dog_stage           1467 non-null   object             \n",
      " 6   img_num             1467 non-null   int64              \n",
      " 7   p1                  1467 non-null   object             \n",
      " 8   p1_conf             1467 non-null   float64            \n",
      " 9   retweet_count       1467 non-null   int64              \n",
      " 10  favorite_count      1467 non-null   int64              \n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(5), object(3)\n",
      "memory usage: 137.5+ KB\n"
     ]
    }
   ],
   "source": [
    "master_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "e84477b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   1241\n",
       "pupper              146\n",
       "doggo                47\n",
       "puppo                20\n",
       "floofer               8\n",
       "multiple_stages       5\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce1c7e1",
   "metadata": {},
   "source": [
    "### Storing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "906c19ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing master DataFrame to a csv file\n",
    "master_df.to_csv('twitter_archive_master.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "601dce64",
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df = pd.read_csv('twitter_archive_master.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8926f8",
   "metadata": {},
   "source": [
    "<a id='analysis'></a>\n",
    "## Analyzing and Visualizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d53462ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing empty strings with NANs \n",
    "master_df['dog_stage'] = master_df.dog_stage.replace('',np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "835ba66e",
   "metadata": {},
   "source": [
    "NB: I did this because empty strings are regarded as values but NaNs are not"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d7711d",
   "metadata": {},
   "source": [
    "Even though the rating denominators were not equal, for dogs with stated dog stages, the denominator was always 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "8862e686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    47\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.query('dog_stage == \"doggo\"').rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "c3804592",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    146\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.query('dog_stage == \"pupper\"').rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ba4625f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    8\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.query('dog_stage == \"floofer\"').rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "2292fb4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    20\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.query('dog_stage == \"puppo\"').rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d111d52d",
   "metadata": {},
   "source": [
    "With dog stage analysis, I could therefore use the rating denominators as they were."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3feb1313",
   "metadata": {},
   "source": [
    "##### Dog stage and rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "71aa08c7",
   "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>dog_stage</th>\n",
       "      <th>rating_numerator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>puppo</td>\n",
       "      <td>12.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>doggo</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>floofer</td>\n",
       "      <td>11.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>multiple_stages</td>\n",
       "      <td>11.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pupper</td>\n",
       "      <td>10.974452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dog_stage  rating_numerator\n",
       "4            puppo         12.050000\n",
       "0            doggo         12.000000\n",
       "1          floofer         11.875000\n",
       "2  multiple_stages         11.800000\n",
       "3           pupper         10.974452"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping average rating by stage and sorting results\n",
    "rt_df = master_df.groupby('dog_stage',\n",
    "        as_index=False)['rating_numerator'].mean().sort_values(\n",
    "        'rating_numerator',ascending=False)\n",
    "rt_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "552b889f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the resulting table above in a bar chart\n",
    "colors = ['royalblue','grey','grey','grey','grey']\n",
    "ax = rt_df.plot(\n",
    "             kind='bar',\n",
    "             x='dog_stage',\n",
    "             y='rating_numerator',\n",
    "             figsize=(10,8),\n",
    "             color=colors,\n",
    "             rot=0,\n",
    "             legend=False\n",
    "              )\n",
    "ax.set_xlabel('dog stage')\n",
    "ax.set_ylabel('average ratings');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ecb793f",
   "metadata": {},
   "source": [
    "Dogs in the puppo stage received the highest ratings followed by doggo stage."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b4280b1",
   "metadata": {},
   "source": [
    "##### Dog stage and Favorite count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c17eec95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>puppo</td>\n",
       "      <td>21372.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>doggo</td>\n",
       "      <td>18909.872340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>multiple_stages</td>\n",
       "      <td>15701.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>floofer</td>\n",
       "      <td>11620.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pupper</td>\n",
       "      <td>6960.452055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dog_stage  favorite_count\n",
       "4            puppo    21372.150000\n",
       "0            doggo    18909.872340\n",
       "2  multiple_stages    15701.400000\n",
       "1          floofer    11620.250000\n",
       "3           pupper     6960.452055"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping mean favorite count by stage\n",
    "fc_df = master_df.groupby('dog_stage',\n",
    "        as_index=False)['favorite_count'].mean(\n",
    "        ).sort_values('favorite_count',ascending=False)\n",
    "fc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c19167db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the resulting table above in a bar chart\n",
    "ax = fc_df.plot(\n",
    "        kind='bar',\n",
    "        x = 'dog_stage',\n",
    "        y = 'favorite_count',\n",
    "        title='Average likes by dog stage',\n",
    "        figsize=(10,8),\n",
    "        legend=False,\n",
    "        rot=0\n",
    "         )\n",
    "ax.set_xlabel('dog stage')\n",
    "ax.set_ylabel('average favorite count');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63799b4f",
   "metadata": {},
   "source": [
    "On the average, tweets with dogs in the `puppo` stage are the most favorited while tweets with dogs in the `pupper` stage are the least favorited "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e06a0a8",
   "metadata": {},
   "source": [
    "##### Dog stage and retweet count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e6a9c84f",
   "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>dog_stage</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>doggo</td>\n",
       "      <td>6395.106383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>puppo</td>\n",
       "      <td>5919.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>multiple_stages</td>\n",
       "      <td>4667.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>floofer</td>\n",
       "      <td>3809.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pupper</td>\n",
       "      <td>2061.027397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dog_stage  retweet_count\n",
       "0            doggo    6395.106383\n",
       "4            puppo    5919.900000\n",
       "2  multiple_stages    4667.400000\n",
       "1          floofer    3809.250000\n",
       "3           pupper    2061.027397"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping mean retweet count by stage\n",
    "rc_df = master_df.groupby('dog_stage',\n",
    "        as_index=False)['retweet_count'].mean(\n",
    "        ).sort_values('retweet_count',ascending=False)\n",
    "rc_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "eb62f8c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the above table in a bar chart\n",
    "ax = rc_df.plot(\n",
    "        kind='bar',\n",
    "        x='dog_stage',\n",
    "        y='retweet_count',\n",
    "        title='Average retweets by dog stage',\n",
    "        figsize=(10,8),\n",
    "        legend=False,\n",
    "        rot=0)\n",
    "ax.set_xlabel('dog stage')\n",
    "ax.set_ylabel('average retweet count');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3510660",
   "metadata": {},
   "source": [
    "Dogs of the `doggo` stage average the most retweets while dogs of the `pupper` stage average the least retweets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "1acdf3ad",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog_stage</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>puppo</th>\n",
       "      <td>21372.150000</td>\n",
       "      <td>5919.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doggo</th>\n",
       "      <td>18909.872340</td>\n",
       "      <td>6395.106383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multiple_stages</th>\n",
       "      <td>15701.400000</td>\n",
       "      <td>4667.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floofer</th>\n",
       "      <td>11620.250000</td>\n",
       "      <td>3809.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pupper</th>\n",
       "      <td>6960.452055</td>\n",
       "      <td>2061.027397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 favorite_count  retweet_count\n",
       "dog_stage                                     \n",
       "puppo              21372.150000    5919.900000\n",
       "doggo              18909.872340    6395.106383\n",
       "multiple_stages    15701.400000    4667.400000\n",
       "floofer            11620.250000    3809.250000\n",
       "pupper              6960.452055    2061.027397"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a pivot table\n",
    "pivot = pd.pivot_table(data=master_df,\n",
    "                       index='dog_stage',\n",
    "                       values=['favorite_count','retweet_count']\n",
    "                      ).sort_values(['favorite_count','retweet_count'],ascending=False)\n",
    "pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3bbd9898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting pivot table on a bar chart\n",
    "ax = pivot.plot(kind='bar',\n",
    "                title = 'Average Retweets and Favorites by Dog stage',\n",
    "                figsize=(10,10),\n",
    "                rot=0)\n",
    "ax.set_xlabel('Dog stage')\n",
    "ax.set_ylabel('Average');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018d03ac",
   "metadata": {},
   "source": [
    "##### Image number and rating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1657973",
   "metadata": {},
   "source": [
    "During data cleaning, I discovered an interesting tweet by WeRateDogs and decided to investigate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "c6e1010f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"@Jack_Septic_Eye I'd need a few more pics to polish a full analysis, but based on the good boy content above I'm leaning towards 12/10\"]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying tweet with the interesting observation\n",
    "df.query('tweet_id == 863427515083354112').text.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8ac5da",
   "metadata": {},
   "source": [
    "As the denominators were not equal, I decided to perform this analysis by converting the rating into decimals and using the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b775ad98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating new column by dividing rating numerator by denominator\n",
    "master_df['rating_decimal'] = master_df['rating_numerator']/master_df['rating_denominator']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "fbb2d943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>rating_decimal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>5267</td>\n",
       "      <td>29208</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>3463</td>\n",
       "      <td>21959</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>7709</td>\n",
       "      <td>35120</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891087950875897856</td>\n",
       "      <td>2017-07-29 00:08:17+00:00</td>\n",
       "      <td>Here we have a majestic great white breaching ...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>chesapeake_bay_retriever</td>\n",
       "      <td>0.425595</td>\n",
       "      <td>2583</td>\n",
       "      <td>17734</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>890971913173991426</td>\n",
       "      <td>2017-07-28 16:27:12+00:00</td>\n",
       "      <td>Meet Jax. He enjoys ice cream so much he gets ...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>appenzeller</td>\n",
       "      <td>0.341703</td>\n",
       "      <td>1645</td>\n",
       "      <td>10322</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                 timestamp  \\\n",
       "0  892177421306343426 2017-08-01 00:17:27+00:00   \n",
       "1  891815181378084864 2017-07-31 00:18:03+00:00   \n",
       "2  891327558926688256 2017-07-29 16:00:24+00:00   \n",
       "3  891087950875897856 2017-07-29 00:08:17+00:00   \n",
       "4  890971913173991426 2017-07-28 16:27:12+00:00   \n",
       "\n",
       "                                                text  rating_numerator  \\\n",
       "0  This is Tilly. She's just checking pup on you....              13.0   \n",
       "1  This is Archie. He is a rare Norwegian Pouncin...              12.0   \n",
       "2  This is Franklin. He would like you to stop ca...              12.0   \n",
       "3  Here we have a majestic great white breaching ...              13.0   \n",
       "4  Meet Jax. He enjoys ice cream so much he gets ...              13.0   \n",
       "\n",
       "   rating_denominator dog_stage  img_num                        p1   p1_conf  \\\n",
       "0                  10       NaN        1                 chihuahua  0.323581   \n",
       "1                  10       NaN        1                 chihuahua  0.716012   \n",
       "2                  10       NaN        2                    basset  0.555712   \n",
       "3                  10       NaN        1  chesapeake_bay_retriever  0.425595   \n",
       "4                  10       NaN        1               appenzeller  0.341703   \n",
       "\n",
       "   retweet_count  favorite_count  rating_decimal  \n",
       "0           5267           29208             1.3  \n",
       "1           3463           21959             1.2  \n",
       "2           7709           35120             1.2  \n",
       "3           2583           17734             1.3  \n",
       "4           1645           10322             1.3  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "16b6a3a0",
   "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>img_num</th>\n",
       "      <th>rating_decimal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.202222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.144000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.079373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   img_num  rating_decimal\n",
       "3        4        1.240000\n",
       "2        3        1.202222\n",
       "1        2        1.144000\n",
       "0        1        1.079373"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping mean rating by image number\n",
    "rd_df = master_df.groupby('img_num',as_index=False)['rating_decimal'].mean(\n",
    "        ).sort_values('rating_decimal',ascending=False)\n",
    "rd_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "78081c56",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing the table above in a bar chart\n",
    "ax  = rd_df.plot(\n",
    "        kind='bar',\n",
    "        x='img_num',\n",
    "        y='rating_decimal',\n",
    "        title='Average ratings by image number',\n",
    "        figsize=(10,8),\n",
    "        color=['royalblue','grey','grey','grey'],\n",
    "        legend=False,\n",
    "        rot=0\n",
    "        )\n",
    "ax.set_xlabel('image number')\n",
    "ax.set_ylabel('average ratings');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a379cc7",
   "metadata": {},
   "source": [
    "I discovered that on the average, tweets with four images received higher ratings. I wanted to investigate this further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "ff2af72b",
   "metadata": {
    "scrolled": true
   },
   "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": [
    "# plotting a scatter graph of the table above\n",
    "scatter_df = master_df.groupby('img_num',as_index=False)['rating_decimal'].mean().copy()\n",
    "scatter_df.plot.scatter(\n",
    "        x='img_num',\n",
    "        y='rating_decimal',\n",
    "        title='relationship between image number and ratings'\n",
    "        );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84bd30af",
   "metadata": {},
   "source": [
    "I discovered that the higher the image number the higher the average ratings. Perhaps because with more images WeRateDogs could 'polish a full analysis'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e53259a",
   "metadata": {},
   "source": [
    "### Dog breed analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0af3ad01",
   "metadata": {},
   "source": [
    "NB: the dog breeds are based on the image prediction algorithm used by Udacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d37d6c32",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting distribution of dog breeds\n",
    "ax = master_df.p1.value_counts().plot.barh(\n",
    "        title='Dog breed distribution',\n",
    "        figsize=(12,12),\n",
    "        rot=0)\n",
    "ax.set_xlabel('dog breed')\n",
    "ax.set_ylabel('frequency');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b07d635",
   "metadata": {},
   "source": [
    "The graph is not clear therefore I decided to plot the top 10 dog breeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b1f8be93",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plottiing top 10 dog breeds by frequency\n",
    "freq = master_df.p1.value_counts().nlargest(10).sort_values(ascending=True)\n",
    "ax = freq.plot.barh(\n",
    "        title='Top 10 dog breeds',\n",
    "        figsize=(12,12),\n",
    "        rot=0)\n",
    "ax.set_xlabel('frequency')\n",
    "ax.set_ylabel('dog breed');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c0dd32",
   "metadata": {},
   "source": [
    "The most occuring dog breed was the golden retriever followed by the labrador retriever."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5969d781",
   "metadata": {},
   "source": [
    "### Text Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "e5c8d8c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "from wordcloud import WordCloud\n",
    "import imageio\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "cb23eb04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# assigning tweets without http tag to variable \n",
    "text = ''.join(master_df.text.str.split(pat='http',expand=True)[0])\n",
    "# creating a mask using a copy of WeRateDogs twitter avi\n",
    "weratedogs_mask = imageio.imread('preview.png')\n",
    "# generating wordcloud\n",
    "wordcloud = WordCloud(\n",
    "                    background_color='white',\n",
    "                    mask=weratedogs_mask\n",
    "                    ).generate(text)\n",
    "# displaying wordcloud\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2767bd",
   "metadata": {},
   "source": [
    "<a id='insights'></a>\n",
    "## Insights:\n",
    "* Dogs of puppo stage received the highest ratings\n",
    "* Tweets with four images averaged the highest ratings\n",
    "* Dogs of puppo stage were the most favorited\n",
    "* Dogs of doggo stage were the most retweeted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe6d3491",
   "metadata": {},
   "source": [
    "### Limitations\n",
    "Only rows and columns relevant to my analysis were cleaned therefore there may be other quality issues in the master dataframe and individual dataframes that were not cleaned because cleaning them would have no impact on the analysis.\n",
    "\n",
    "Using the independent variables (image number and dog stage) in predicting ratinsg will require more in-depth analysis and application of machine learning models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ad9442",
   "metadata": {},
   "source": [
    "<a id='wranglereport'></a>\n",
    "## Wrangle Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb17b96",
   "metadata": {},
   "source": [
    "#### Report on Data Wrangling performed on WeRateDogs twitter archive.\n",
    "\n",
    "##### Gathering\n",
    "I manually downloaded the WeRateDogs twitter archive given by Udacity, moved it into my project directory and imported it into a pandas Dataframe named `df` using the read_csv() function.\n",
    "\n",
    "I programmatically downloaded the second file hosted on Udacity servers using the Requests library. Then I imported it into a DataFrame named `df_02` using the read_csv() function with the appropriate separator (\\t) because it was a tsv file.\n",
    "Using the Tweepy library, I read each tweet’s json data into a file named tweet_json.txt. Then I imported the contents into an initial DataFrame from which I created another DataFrame named `df_03` containing just the columns I needed for my analysis.\n",
    "\n",
    "##### Assessing and Cleaning\n",
    "I assessed the three dataframes visually and programmatically and listed the quality and tidiness issues I discovered. Below is a list of the issues and how I cleaned them.\n",
    "\n",
    "##### Issue:\n",
    "\n",
    "* Double dog stages instead of one\n",
    "* Wrong rating numerator and denominator\n",
    "* Tweets with ratings that were not dog ratings\n",
    "\n",
    "During my assessment, I discovered that double dog stages ahd been extracted from some tweets. While this was correct for some because there was more than one dog in the tweet, for some another dog stage had just been mentioned in the tweet but not as the stage of the dog in the tweet. Using the index of tweets in this category, I replaced the wrong dog stages with ‘None’\n",
    "I also discovered that some rating numerators and denominators had been extracted wrongly therefore I manually replaced them with the correct figures.\n",
    "For tweets with ratings that I discovered were not dogs, I dropped them using the index.\n",
    "\n",
    "##### Issue: \n",
    "\n",
    "* `df` DataFrame contains retweets\n",
    "\n",
    "Tweets that contained non null values in the ‘retweeted_status_id’ column were retweets therefore I dropped all of such tweets. This in turn deleted the non nulls in the ‘retweeted_status_user_id’ and ‘retweeted_status_timestamp’ columns.\n",
    "\n",
    "##### Issue: \n",
    "\n",
    "* row values in multiple columns(`doggo`,`pupper`,`floofer`,`puppo`)\n",
    "\n",
    "These columns belonged in one column because they were dog stages. I replaced ‘None’ in the columns with an empty string and used string concatenation to merge the columns into one. For tweets that had more than one dog stage, I changed it to ‘multiple_stages’.\n",
    "\n",
    "##### Issues: \n",
    "\n",
    "* data type of `timestamp` column is string instead of datetime\n",
    "\n",
    "I used pandas to_datetime() function to convert the timestamp column’s data type to datetime format. \n",
    "\n",
    "##### Issues:\n",
    "\n",
    "* Missing values in the `in_reply_to_status_id` column\n",
    "\n",
    "* Missing values in the `in_reply_to_user_id` column\n",
    "\n",
    "* HTML tag in the `source` column\n",
    "\n",
    "* Missing values in the `retweeted_status_id` column\n",
    "\n",
    "* Missing values in the `retweeted_status_timestamp` column\n",
    "\n",
    "* Missing values represented as `None` in the `name` column\n",
    "\n",
    "* Missing values in the `expanded_urls` column\n",
    "\n",
    "These columns were not relevant to my analysis and visualizations therefore I dropped them.\n",
    "\n",
    "##### Issue:\n",
    "\n",
    "* Some values in the `p1`, `p2` and `p3` columns are capitalized while others are not\n",
    "\n",
    "I used .str.lower() to convert the values in these columns to lowercase.\n",
    "\n",
    "##### Issue: \n",
    "\n",
    "* the confidence level of the second (`p2_conf`) and third (`p3_conf`) algorithms are relatively lower than the 1st algorithm(`p1_conf`)\n",
    "\n",
    "I dropped these columns leaving the first alogorithm.\n",
    "\n",
    "##### Issue: \n",
    "\n",
    "* According to the p1 algorithm some tweets are not dogs hence `False` in the `p1_dog` column\n",
    "\n",
    "I filtered for rows that were false in the p1_dog column and dropped them after which I dropped the p1_dog column because it only contained True.\n",
    "\n",
    "##### Issue:\n",
    "\n",
    "* tweet IDs column is labeled as `id`\n",
    "\n",
    "I renamed the id column to ‘tweet_id’\n",
    "\n",
    "##### Issue:\n",
    "\n",
    "* information about one observational unit (tweets) is contained in the 3 different dataframes\n",
    "\n",
    "After cleaning, I merged the three dataframes into one using the observational unit (`tweet_id`) \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "73daebb8c5e963255a8d4e574cfd4a275e87fbc8721cbb75a3aa7bc07dfe7361"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}