wrangle_act (6).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88634927",
   "metadata": {},
   "source": [
    "# Data wrangling WeRateDogs Project:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8da17cd5",
   "metadata": {},
   "source": [
    "# Introducion/Overview:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e1d2e39",
   "metadata": {},
   "source": [
    "The dataset that we will be wrangling (and analyzing and visualizing) is the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6997dc",
   "metadata": {},
   "source": [
    "# Step 1: Gathering Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6f15424",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install tweepy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14b34e7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import all required packages for this Data wrangling project:\n",
    "\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import tweepy\n",
    "import os\n",
    "import json\n",
    "import time\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9559db1d",
   "metadata": {},
   "source": [
    "# 1-Twitter Archive file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156e5d11",
   "metadata": {},
   "source": [
    "Downloaded this file manually named'twitter-archive-enhanced.csv'provided by Udacity as a link."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0514eff2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read csv as a Pandas DataFrame\n",
    "df_twitter_archive = pd.read_csv('twitter-archive-enhanced.csv')\n",
    "df_twitter_archive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64dd5767",
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d9f1446",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af33aa8d",
   "metadata": {},
   "source": [
    "# 2-The Tweet Image Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7cd265d",
   "metadata": {},
   "source": [
    "This file (image_predictions.tsv) is hosted on Udacity's servers and should be downloaded programmatically using the Requests library and the following URL:\n",
    "https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv\n",
    "\n",
    "This file has dataset about what breed of dog (or other object, animal, etc.) is present in each tweet according to a neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7db150c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#url=\"https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv\"\n",
    "#c=pd.read_csv(url)\n",
    "#To download URL programatically following code will be used in which we are using request library\n",
    "link = \"https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv\"\n",
    "response = requests.get(link)\n",
    "\n",
    "with open('image-predictions.tsv', mode ='wb') as file:\n",
    "    file.write(response.content)\n",
    "\n",
    "#Read TSV file\n",
    "df_tweet_image_prediction = pd.read_csv('image-predictions.tsv', sep='\\t' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9231f619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check how thois file data looks like:\n",
    "df_tweet_image_prediction.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9dfb0a23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweet_image_prediction.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74e23d4b",
   "metadata": {},
   "source": [
    "# 3-Additional Data Via Twitter API\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e398275",
   "metadata": {},
   "source": [
    "Each tweet's retweet count and favorite (\"like\") count at minimum, and any additional data you find interesting. Using the tweet IDs in the WeRateDogs Twitter archive, query the Twitter API for each tweet's JSON data using Python's Tweepy library and store each tweet's entire set of JSON data in a file called tweet_json.txt file. Each tweet's JSON data should be written to its own line. Then read this .txt file line by line into a pandas DataFrame with (at minimum) tweet ID, retweet count, and favorite count."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e03803b",
   "metadata": {},
   "source": [
    "Query Twitter Data:\n",
    "1-using Tweepy to query Twitter's API for additional data beyond the data included in the WeRateDogs Twitter archive.\n",
    "\n",
    "2-This additional data will include retweet count and favorite count."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f8ddbf",
   "metadata": {},
   "source": [
    "# Accessing Project Data Without a Twitter Account:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c8a303",
   "metadata": {},
   "source": [
    "Here I am Accessing Twitter API data without twitter account and using UDACITY code provided with json txt file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "57a09e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To Access Twitter API to create JSON FILE.\n",
    "#api = tweepy.API(auth, wait_on_rate_limit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e5d1a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "#api.verify_credentials()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6403c00",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "from tweepy import OAuthHandler\n",
    "import json\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "# Query Twitter API for each tweet in the Twitter archive and save JSON in a text file\n",
    "# These are hidden to comply with Twitter's API terms and conditions\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",
    "# NOTE TO STUDENT WITH MOBILE VERIFICATION ISSUES:\n",
    "# df_1 is a DataFrame with the twitter_archive_enhanced.csv file. You may have to\n",
    "# change line 17 to match the name of your DataFrame with twitter_archive_enhanced.csv\n",
    "# NOTE TO REVIEWER: this student had mobile verification issues so the following\n",
    "# Twitter API code was sent to this student from a Udacity instructor\n",
    "# Tweet IDs for which to gather additional data via Twitter's API\n",
    "tweet_ids = df_1.tweet_id.values\n",
    "len(tweet_ids)\n",
    "\n",
    "# Query Twitter's API for JSON data for each tweet ID in the Twitter archive\n",
    "count = 0\n",
    "fails_dict = {}\n",
    "start = timer()\n",
    "# Save each tweet's returned JSON as a new line in a .txt file\n",
    "with open('tweet_json.txt', 'w') as outfile:\n",
    "    # This loop will likely take 20-30 minutes to run because of Twitter's rate limit\n",
    "    for tweet_id in tweet_ids:\n",
    "        count += 1\n",
    "        print(str(count) + \": \" + str(tweet_id))\n",
    "        try:\n",
    "            tweet = api.get_status(tweet_id, tweet_mode='extended')\n",
    "            print(\"Success\")\n",
    "            json.dump(tweet._json, outfile)\n",
    "            outfile.write('\\n')\n",
    "        except tweepy.TweepError as e:\n",
    "            print(\"Fail\")\n",
    "            fails_dict[tweet_id] = e\n",
    "            pass\n",
    "end = timer()\n",
    "print(end - start)\n",
    "print(fails_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e27c05a",
   "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>favorite_count</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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>892420643555336192</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>39467</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</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>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>892177421306343424</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>33819</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</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>2017-07-31 00:18:03+00:00</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>25461</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</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>2017-07-30 15:58:51+00:00</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>42908</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</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>2017-07-29 16:00:24+00:00</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>41048</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</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 × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 created_at                  id              id_str  \\\n",
       "0 2017-08-01 16:23:56+00:00  892420643555336193  892420643555336192   \n",
       "1 2017-08-01 00:17:27+00:00  892177421306343426  892177421306343424   \n",
       "2 2017-07-31 00:18:03+00:00  891815181378084864  891815181378084864   \n",
       "3 2017-07-30 15:58:51+00:00  891689557279858688  891689557279858688   \n",
       "4 2017-07-29 16:00:24+00:00  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",
       "   ...  favorite_count  favorited  retweeted possibly_sensitive  \\\n",
       "0  ...           39467      False      False                0.0   \n",
       "1  ...           33819      False      False                0.0   \n",
       "2  ...           25461      False      False                0.0   \n",
       "3  ...           42908      False      False                0.0   \n",
       "4  ...           41048      False      False                0.0   \n",
       "\n",
       "  possibly_sensitive_appealable  lang  retweeted_status quoted_status_id  \\\n",
       "0                           0.0    en               NaN              NaN   \n",
       "1                           0.0    en               NaN              NaN   \n",
       "2                           0.0    en               NaN              NaN   \n",
       "3                           0.0    en               NaN              NaN   \n",
       "4                           0.0    en               NaN              NaN   \n",
       "\n",
       "   quoted_status_id_str  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 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_api_data = pd.read_json('tweet_json.txt', lines=True)\n",
    "df_api_data.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d007ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_twt_cln.rename(columns={'id': 'tweet_id'}, inplace=True)\n",
    "df_api_data.rename(columns={'id': 'tweet_id'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d8835328",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 31 columns):\n",
      " #   Column                         Non-Null Count  Dtype              \n",
      "---  ------                         --------------  -----              \n",
      " 0   created_at                     2354 non-null   datetime64[ns, UTC]\n",
      " 1   tweet_id                       2354 non-null   int64              \n",
      " 2   id_str                         2354 non-null   int64              \n",
      " 3   full_text                      2354 non-null   object             \n",
      " 4   truncated                      2354 non-null   bool               \n",
      " 5   display_text_range             2354 non-null   object             \n",
      " 6   entities                       2354 non-null   object             \n",
      " 7   extended_entities              2073 non-null   object             \n",
      " 8   source                         2354 non-null   object             \n",
      " 9   in_reply_to_status_id          78 non-null     float64            \n",
      " 10  in_reply_to_status_id_str      78 non-null     float64            \n",
      " 11  in_reply_to_user_id            78 non-null     float64            \n",
      " 12  in_reply_to_user_id_str        78 non-null     float64            \n",
      " 13  in_reply_to_screen_name        78 non-null     object             \n",
      " 14  user                           2354 non-null   object             \n",
      " 15  geo                            0 non-null      float64            \n",
      " 16  coordinates                    0 non-null      float64            \n",
      " 17  place                          1 non-null      object             \n",
      " 18  contributors                   0 non-null      float64            \n",
      " 19  is_quote_status                2354 non-null   bool               \n",
      " 20  retweet_count                  2354 non-null   int64              \n",
      " 21  favorite_count                 2354 non-null   int64              \n",
      " 22  favorited                      2354 non-null   bool               \n",
      " 23  retweeted                      2354 non-null   bool               \n",
      " 24  possibly_sensitive             2211 non-null   float64            \n",
      " 25  possibly_sensitive_appealable  2211 non-null   float64            \n",
      " 26  lang                           2354 non-null   object             \n",
      " 27  retweeted_status               179 non-null    object             \n",
      " 28  quoted_status_id               29 non-null     float64            \n",
      " 29  quoted_status_id_str           29 non-null     float64            \n",
      " 30  quoted_status                  28 non-null     object             \n",
      "dtypes: bool(4), datetime64[ns, UTC](1), float64(11), int64(4), object(11)\n",
      "memory usage: 505.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_api_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f723cc57",
   "metadata": {},
   "source": [
    "# Step 2: Assessing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c300267",
   "metadata": {},
   "source": [
    "# Visual assessment of all 3 datasets:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7250f9ef",
   "metadata": {},
   "source": [
    "# 1. Twitter Archived Enhanced Data:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417fd954",
   "metadata": {},
   "source": [
    "Visual assessment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b1676da7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2356 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0     892420643555336193                    NaN                  NaN   \n",
       "1     892177421306343426                    NaN                  NaN   \n",
       "2     891815181378084864                    NaN                  NaN   \n",
       "3     891689557279858688                    NaN                  NaN   \n",
       "4     891327558926688256                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "2351  666049248165822465                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "2355  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "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",
       "2351  2015-11-16 00:24:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "2355  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "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",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "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",
       "2351  Here we have a 1949 1st generation vulpix. Enj...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                          NaN                        NaN   \n",
       "1                          NaN                        NaN   \n",
       "2                          NaN                        NaN   \n",
       "3                          NaN                        NaN   \n",
       "4                          NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2351                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "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",
       "2351  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator      name doggo floofer pupper puppo  \n",
       "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  \n",
       "...                  ...       ...   ...     ...    ...   ...  \n",
       "2351                  10      None  None    None   None  None  \n",
       "2352                  10         a  None    None   None  None  \n",
       "2353                  10         a  None    None   None  None  \n",
       "2354                  10         a  None    None   None  None  \n",
       "2355                  10      None  None    None   None  None  \n",
       "\n",
       "[2356 rows x 17 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "538a1d8f",
   "metadata": {},
   "source": [
    "By Glancing at data shown above( Visual Assessment),We came to know that column 'name' which displays name of the dog has some invalid or meaningless names which are equal to or less than 2 characters.so we need to look into details of this by programmatic visualisation and later on fix this issue."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3334b5",
   "metadata": {},
   "source": [
    "# Programmmatic Assessment of Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8b2a2259",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c11163d1",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b53f99cf",
   "metadata": {},
   "source": [
    "Programmatic Assesment of Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "78ff3dd8",
   "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>56</th>\n",
       "      <td>881536004380872706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-02 15:32:16 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a pupper approaching maximum borkdrive...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/881536004...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>825876512159186944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-30 01:21:19 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Mo. No one will push him around in the...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/825876512...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Mo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>819015337530290176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-11 02:57:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is Bo. He was a very good ...</td>\n",
       "      <td>8.190048e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-01-11 02:15:36 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/819004803...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>Bo</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>819004803107983360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-11 02:15:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Bo. He was a very good First Doggo. 14...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/819004803...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>Bo</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>804026241225523202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-30 18:16:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Bo. He's going to make me cry. 13/10 p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/804026241...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Bo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:35:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is an odd dog. Hard on the outside but lo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666051853...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>an</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:30:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a truly beautiful English Wilson Staff...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666050758...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "56    881536004380872706                    NaN                  NaN   \n",
       "393   825876512159186944                    NaN                  NaN   \n",
       "446   819015337530290176                    NaN                  NaN   \n",
       "449   819004803107983360                    NaN                  NaN   \n",
       "553   804026241225523202                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "2349  666051853826850816                    NaN                  NaN   \n",
       "2350  666050758794694657                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "56    2017-07-02 15:32:16 +0000   \n",
       "393   2017-01-30 01:21:19 +0000   \n",
       "446   2017-01-11 02:57:27 +0000   \n",
       "449   2017-01-11 02:15:36 +0000   \n",
       "553   2016-11-30 18:16:08 +0000   \n",
       "...                         ...   \n",
       "2349  2015-11-16 00:35:11 +0000   \n",
       "2350  2015-11-16 00:30:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "56    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "393   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "446   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "449   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "553   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2349  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2350  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "56    Here is a pupper approaching maximum borkdrive...                  NaN   \n",
       "393   This is Mo. No one will push him around in the...                  NaN   \n",
       "446   RT @dog_rates: This is Bo. He was a very good ...         8.190048e+17   \n",
       "449   This is Bo. He was a very good First Doggo. 14...                  NaN   \n",
       "553   This is Bo. He's going to make me cry. 13/10 p...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "2349  This is an odd dog. Hard on the outside but lo...                  NaN   \n",
       "2350  This is a truly beautiful English Wilson Staff...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "56                         NaN                        NaN   \n",
       "393                        NaN                        NaN   \n",
       "446               4.196984e+09  2017-01-11 02:15:36 +0000   \n",
       "449                        NaN                        NaN   \n",
       "553                        NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2349                       NaN                        NaN   \n",
       "2350                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "56    https://twitter.com/dog_rates/status/881536004...                14   \n",
       "393   https://twitter.com/dog_rates/status/825876512...                11   \n",
       "446   https://twitter.com/dog_rates/status/819004803...                14   \n",
       "449   https://twitter.com/dog_rates/status/819004803...                14   \n",
       "553   https://twitter.com/dog_rates/status/804026241...                13   \n",
       "...                                                 ...               ...   \n",
       "2349  https://twitter.com/dog_rates/status/666051853...                 2   \n",
       "2350  https://twitter.com/dog_rates/status/666050758...                10   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "\n",
       "      rating_denominator name  doggo floofer  pupper puppo  \n",
       "56                    10    a   None    None  pupper  None  \n",
       "393                   10   Mo   None    None    None  None  \n",
       "446                   10   Bo  doggo    None    None  None  \n",
       "449                   10   Bo  doggo    None    None  None  \n",
       "553                   10   Bo   None    None    None  None  \n",
       "...                  ...  ...    ...     ...     ...   ...  \n",
       "2349                  10   an   None    None    None  None  \n",
       "2350                  10    a   None    None    None  None  \n",
       "2352                  10    a   None    None    None  None  \n",
       "2353                  10    a   None    None    None  None  \n",
       "2354                  10    a   None    None    None  None  \n",
       "\n",
       "[79 rows x 17 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check how many rows /cols have name of dogs which is very small or not proper \n",
    "df_twitter_archive[df_twitter_archive['name'].apply(len) <= 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060f6fb7",
   "metadata": {},
   "source": [
    "Check for any duplication issue in this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6529f4b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_twitter_archive['tweet_id'].duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5682baef",
   "metadata": {},
   "source": [
    "2-Checking 'source' column unique values in URL:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "16122729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>     2221\n",
       "<a href=\"http://vine.co\" rel=\"nofollow\">Vine - Make a Scene</a>                          91\n",
       "<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>                       33\n",
       "<a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>      11\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using pandas.Series.value_counts to return a Series containing counts of unique values.\n",
    "df_twitter_archive.source.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70e46d66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2356.000000\n",
       "mean       13.126486\n",
       "std        45.876648\n",
       "min         0.000000\n",
       "25%        10.000000\n",
       "50%        11.000000\n",
       "75%        12.000000\n",
       "max      1776.000000\n",
       "Name: rating_numerator, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive['rating_numerator'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ada2c65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      558\n",
       "11      464\n",
       "10      461\n",
       "13      351\n",
       "9       158\n",
       "8       102\n",
       "7        55\n",
       "14       54\n",
       "5        37\n",
       "6        32\n",
       "3        19\n",
       "4        17\n",
       "1         9\n",
       "2         9\n",
       "0         2\n",
       "15        2\n",
       "75        2\n",
       "420       2\n",
       "182       1\n",
       "204       1\n",
       "143       1\n",
       "121       1\n",
       "99        1\n",
       "20        1\n",
       "45        1\n",
       "27        1\n",
       "17        1\n",
       "24        1\n",
       "26        1\n",
       "44        1\n",
       "50        1\n",
       "60        1\n",
       "80        1\n",
       "84        1\n",
       "88        1\n",
       "1776      1\n",
       "960       1\n",
       "666       1\n",
       "144       1\n",
       "165       1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive.rating_numerator.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec62d2e9",
   "metadata": {},
   "source": [
    " The above information shows that there are Quality and Tidiness issues in this datset which needs to be addressed in Cleaning phase before moving on to Analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36b0bfd",
   "metadata": {},
   "source": [
    "# Quality Issues :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26157bf9",
   "metadata": {},
   "source": [
    "As far as Quality of data is concerned we must check for any Completeness,Validity,Accuracy and consistency of data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4924bd",
   "metadata": {},
   "source": [
    "1-First one is duplication issue in df_tweet_image_prediction: image_predictions.tsv;This is data validity issue and is identified programatically.column jpg_url has many duplicated entries and these must be removed in order to clean data. \n",
    "\n",
    "2-In df_twitter_archive data type of column 'timestamp' should be datetime.When doing data analysis, it is important to make sure you are using the correct data types; otherwise you may get unexpected results or errors.\n",
    "\n",
    "3-In third data frame i.e Twitter API dataframe(df_api_data)retweet_count and favorite_count should be integers not float.\n",
    "\n",
    "4-In df_twitter_archive dataset 'tweet_id' column should be of object type,not integer.\n",
    "\n",
    "5-In image prediction data set (df_tweet_image_prediction)dog breed columns(p1,p2,p3)has some data in upper case and some in lower case.It gives our data in theses columns look dirty so we can either convert it to lowercase.\n",
    "\n",
    "6-Invalid or non standard dog names in Twitter Archive file,Check for their occurences in the dataset and then replace them with None.\n",
    "\n",
    "7-In Twitter API dataset((df_twt),There is data Duplication Issue in reteweet_count and favorite_count.\n",
    "\n",
    "8-Visual Assesement of the Twitter archive enhance dataset shows issue in 'source' column with HTML tags url in one column.So cleaning of this column is also on of the quality issues that needs to be addressed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24935258",
   "metadata": {},
   "source": [
    "# Tidiness Issue:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87495f39",
   "metadata": {},
   "source": [
    "Tidiness Issues:\n",
    "\n",
    "1-Twitter_archive -These cols show the dog breed (doggo, floofer, pupper and puppo columns)instead of these 4 columns we can make one column' dogitionary' by Melting the doggo, floofer, pupper and puppo columns to dogitionary column.\n",
    "\n",
    "(2) Information about one type of observational unit (tweets) is spread across three different files/dataframes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19723176",
   "metadata": {},
   "source": [
    "# 2. Image Predictions data:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f238b1f1",
   "metadata": {},
   "source": [
    "# Visual Assessment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "89fc2824",
   "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",
       "    <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>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",
       "<p>2075 rows × 12 columns</p>\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",
       "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  \\\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",
       "2070        2                  basset  0.555712    True    English_springer   \n",
       "2071        1             paper_towel  0.170278   False  Labrador_retriever   \n",
       "2072        1               Chihuahua  0.716012    True            malamute   \n",
       "2073        1               Chihuahua  0.323581    True            Pekinese   \n",
       "2074        1                  orange  0.097049   False               bagel   \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  \n",
       "...        ...     ...                          ...       ...     ...  \n",
       "2070  0.225770    True  German_short-haired_pointer  0.175219    True  \n",
       "2071  0.168086    True                      spatula  0.040836   False  \n",
       "2072  0.078253    True                       kelpie  0.031379    True  \n",
       "2073  0.090647    True                     papillon  0.068957    True  \n",
       "2074  0.085851   False                       banana  0.076110   False  \n",
       "\n",
       "[2075 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweet_image_prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861840fd",
   "metadata": {},
   "source": [
    "Results of visual Assesment:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11eb0da3",
   "metadata": {},
   "source": [
    "1-Dog breed p1,p2,p3 has no standard,it has sometimes unusual names like 'cab' also appearing and starting with both uppercase and sometimes lowercase.This issue is more likely related to data consistency which must be taken into account while performing data cleaning processs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d657b612",
   "metadata": {},
   "source": [
    "2-For Improving data quality and make data more readable and understandable for analysis and visulaisation purpose,we can also make a single column for image prediction and another one column for confidence level.This will make data look tidier too."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a44c0ee",
   "metadata": {},
   "source": [
    "# Programmatic Assesment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2a16b0cd",
   "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>1697</th>\n",
       "      <td>816697700272001025</td>\n",
       "      <td>https://pbs.twimg.com/media/C1V-K63UAAEUHqw.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.756992</td>\n",
       "      <td>True</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.052850</td>\n",
       "      <td>True</td>\n",
       "      <td>Maltese_dog</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>692417313023332352</td>\n",
       "      <td>https://pbs.twimg.com/media/CZv13u5WYAA6wQe.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>bison</td>\n",
       "      <td>0.208922</td>\n",
       "      <td>False</td>\n",
       "      <td>mink</td>\n",
       "      <td>0.169945</td>\n",
       "      <td>False</td>\n",
       "      <td>polecat</td>\n",
       "      <td>0.144494</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>707741517457260545</td>\n",
       "      <td>https://pbs.twimg.com/media/CdJnJ1dUEAARNcf.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>whippet</td>\n",
       "      <td>0.738371</td>\n",
       "      <td>True</td>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.191789</td>\n",
       "      <td>True</td>\n",
       "      <td>American_Staffordshire_terrier</td>\n",
       "      <td>0.020126</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>667435689202614272</td>\n",
       "      <td>https://pbs.twimg.com/media/CUM1OHCW4AEgGSi.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.999091</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>True</td>\n",
       "      <td>black-and-tan_coonhound</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>672828477930868736</td>\n",
       "      <td>https://pbs.twimg.com/media/CVZd7ttWcAEs2wP.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>sandbar</td>\n",
       "      <td>0.118154</td>\n",
       "      <td>False</td>\n",
       "      <td>stingray</td>\n",
       "      <td>0.075915</td>\n",
       "      <td>False</td>\n",
       "      <td>seashore</td>\n",
       "      <td>0.075125</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.489814</td>\n",
       "      <td>True</td>\n",
       "      <td>bull_mastiff</td>\n",
       "      <td>0.404722</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.048960</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>749996283729883136</td>\n",
       "      <td>https://pbs.twimg.com/media/CmfoyrrW8AA8v7w.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Old_English_sheepdog</td>\n",
       "      <td>0.515319</td>\n",
       "      <td>True</td>\n",
       "      <td>West_Highland_white_terrier</td>\n",
       "      <td>0.151040</td>\n",
       "      <td>True</td>\n",
       "      <td>soft-coated_wheaten_terrier</td>\n",
       "      <td>0.056420</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>675870721063669760</td>\n",
       "      <td>https://pbs.twimg.com/media/CWEs1b-WEAEhq82.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.263892</td>\n",
       "      <td>True</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.184193</td>\n",
       "      <td>True</td>\n",
       "      <td>beagle</td>\n",
       "      <td>0.182241</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>672834301050937345</td>\n",
       "      <td>https://pbs.twimg.com/media/CVZjOktVAAAtigw.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.582560</td>\n",
       "      <td>True</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.258869</td>\n",
       "      <td>True</td>\n",
       "      <td>nipple</td>\n",
       "      <td>0.033835</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>688179443353796608</td>\n",
       "      <td>https://pbs.twimg.com/media/CYznjAcUEAQ5Zq7.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>sorrel</td>\n",
       "      <td>0.811520</td>\n",
       "      <td>False</td>\n",
       "      <td>horse_cart</td>\n",
       "      <td>0.024820</td>\n",
       "      <td>False</td>\n",
       "      <td>Arabian_camel</td>\n",
       "      <td>0.015155</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1668</th>\n",
       "      <td>813051746834595840</td>\n",
       "      <td>https://pbs.twimg.com/media/C0iKPZIXUAAbDYV.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.914804</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.083550</td>\n",
       "      <td>True</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1573</th>\n",
       "      <td>795400264262053889</td>\n",
       "      <td>https://pbs.twimg.com/media/CwnUUGTWIAE8sFR.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.925494</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.059241</td>\n",
       "      <td>True</td>\n",
       "      <td>tennis_ball</td>\n",
       "      <td>0.004495</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>686050296934563840</td>\n",
       "      <td>https://pbs.twimg.com/media/CYVXBb9WsAAwL3p.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.985789</td>\n",
       "      <td>True</td>\n",
       "      <td>keeshond</td>\n",
       "      <td>0.004083</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.003334</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>729854734790754305</td>\n",
       "      <td>https://pbs.twimg.com/media/CiD3AfkXEAA3S_r.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>doormat</td>\n",
       "      <td>0.359586</td>\n",
       "      <td>False</td>\n",
       "      <td>china_cabinet</td>\n",
       "      <td>0.053901</td>\n",
       "      <td>False</td>\n",
       "      <td>passenger_car</td>\n",
       "      <td>0.052665</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>802624713319034886</td>\n",
       "      <td>https://pbs.twimg.com/media/CsrjryzWgAAZY00.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.253442</td>\n",
       "      <td>True</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.162850</td>\n",
       "      <td>True</td>\n",
       "      <td>otterhound</td>\n",
       "      <td>0.110921</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "1697  816697700272001025  https://pbs.twimg.com/media/C1V-K63UAAEUHqw.jpg   \n",
       "811   692417313023332352  https://pbs.twimg.com/media/CZv13u5WYAA6wQe.jpg   \n",
       "987   707741517457260545  https://pbs.twimg.com/media/CdJnJ1dUEAARNcf.jpg   \n",
       "76    667435689202614272  https://pbs.twimg.com/media/CUM1OHCW4AEgGSi.jpg   \n",
       "361   672828477930868736  https://pbs.twimg.com/media/CVZd7ttWcAEs2wP.jpg   \n",
       "13    666082916733198337  https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg   \n",
       "1271  749996283729883136  https://pbs.twimg.com/media/CmfoyrrW8AA8v7w.jpg   \n",
       "502   675870721063669760  https://pbs.twimg.com/media/CWEs1b-WEAEhq82.jpg   \n",
       "362   672834301050937345  https://pbs.twimg.com/media/CVZjOktVAAAtigw.jpg   \n",
       "753   688179443353796608  https://pbs.twimg.com/media/CYznjAcUEAQ5Zq7.jpg   \n",
       "1668  813051746834595840  https://pbs.twimg.com/media/C0iKPZIXUAAbDYV.jpg   \n",
       "1573  795400264262053889  https://pbs.twimg.com/media/CwnUUGTWIAE8sFR.jpg   \n",
       "725   686050296934563840  https://pbs.twimg.com/media/CYVXBb9WsAAwL3p.jpg   \n",
       "1142  729854734790754305  https://pbs.twimg.com/media/CiD3AfkXEAA3S_r.jpg   \n",
       "1619  802624713319034886  https://pbs.twimg.com/media/CsrjryzWgAAZY00.jpg   \n",
       "\n",
       "      img_num                    p1   p1_conf  p1_dog  \\\n",
       "1697        1             Chihuahua  0.756992    True   \n",
       "811         1                 bison  0.208922   False   \n",
       "987         1               whippet  0.738371    True   \n",
       "76          1            Rottweiler  0.999091    True   \n",
       "361         1               sandbar  0.118154   False   \n",
       "13          1                   pug  0.489814    True   \n",
       "1271        1  Old_English_sheepdog  0.515319    True   \n",
       "502         1      golden_retriever  0.263892    True   \n",
       "362         1              Pembroke  0.582560    True   \n",
       "753         1                sorrel  0.811520   False   \n",
       "1668        1      golden_retriever  0.914804    True   \n",
       "1573        2      golden_retriever  0.925494    True   \n",
       "725         1            Pomeranian  0.985789    True   \n",
       "1142        1               doormat  0.359586   False   \n",
       "1619        1        cocker_spaniel  0.253442    True   \n",
       "\n",
       "                               p2   p2_conf  p2_dog  \\\n",
       "1697                   Pomeranian  0.052850    True   \n",
       "811                          mink  0.169945   False   \n",
       "987             Italian_greyhound  0.191789    True   \n",
       "76             miniature_pinscher  0.000450    True   \n",
       "361                      stingray  0.075915   False   \n",
       "13                   bull_mastiff  0.404722    True   \n",
       "1271  West_Highland_white_terrier  0.151040    True   \n",
       "502        Welsh_springer_spaniel  0.184193    True   \n",
       "362                      Cardigan  0.258869    True   \n",
       "753                    horse_cart  0.024820   False   \n",
       "1668           Labrador_retriever  0.083550    True   \n",
       "1573           Labrador_retriever  0.059241    True   \n",
       "725                      keeshond  0.004083    True   \n",
       "1142                china_cabinet  0.053901   False   \n",
       "1619             golden_retriever  0.162850    True   \n",
       "\n",
       "                                  p3   p3_conf  p3_dog  \n",
       "1697                     Maltese_dog  0.047608    True  \n",
       "811                          polecat  0.144494   False  \n",
       "987   American_Staffordshire_terrier  0.020126    True  \n",
       "76           black-and-tan_coonhound  0.000157    True  \n",
       "361                         seashore  0.075125   False  \n",
       "13                    French_bulldog  0.048960    True  \n",
       "1271     soft-coated_wheaten_terrier  0.056420    True  \n",
       "502                           beagle  0.182241    True  \n",
       "362                           nipple  0.033835   False  \n",
       "753                    Arabian_camel  0.015155   False  \n",
       "1668                          kuvasz  0.000453    True  \n",
       "1573                     tennis_ball  0.004495   False  \n",
       "725                         Pekinese  0.003334    True  \n",
       "1142                   passenger_car  0.052665   False  \n",
       "1619                      otterhound  0.110921    True  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sample() is an inbuilt function of random module in Python that returns a particular length list of items chosen from the sequence i.e. list, tuple, string or set. Used for random sampling without replacement.\n",
    "df_tweet_image_prediction.sample(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a265fbc7",
   "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>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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweet_image_prediction.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3d39581d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "https://pbs.twimg.com/media/CU3mITUWIAAfyQS.jpg    2\n",
       "https://pbs.twimg.com/media/CZhn-QAWwAASQan.jpg    2\n",
       "https://pbs.twimg.com/media/CiibOMzUYAA9Mxz.jpg    2\n",
       "https://pbs.twimg.com/media/Cs_DYr1XEAA54Pu.jpg    2\n",
       "https://pbs.twimg.com/media/CwiuEJmW8AAZnit.jpg    2\n",
       "                                                  ..\n",
       "https://pbs.twimg.com/media/ClrK-rGWAAENcAa.jpg    1\n",
       "https://pbs.twimg.com/media/CXBq6RPWkAAaNuU.jpg    1\n",
       "https://pbs.twimg.com/media/C_RAFTxUAAAbXjV.jpg    1\n",
       "https://pbs.twimg.com/media/DEi_N9qXYAAgEEw.jpg    1\n",
       "https://pbs.twimg.com/media/Ck18CFcXIAAUWoy.jpg    1\n",
       "Name: jpg_url, Length: 2009, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweet_image_prediction.jpg_url.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "81b289f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweet_image_prediction.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "02740b48",
   "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>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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweet_image_prediction.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb6ae9e",
   "metadata": {},
   "source": [
    "\n",
    "Let's analyze the duplicated entries.\n",
    "\n",
    ".duplicated(),duplication issue in df_tweet_image_prediction: image_predictions.tsv;This is data validity issue and is identified programatically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2c3b1e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_tweet_image_prediction.tweet_id.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c784002f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Duplicated jpg_url.\n",
    "sum(df_tweet_image_prediction.jpg_url.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "724b118d",
   "metadata": {},
   "source": [
    "2-The above issue seems to be data validity issue It has duplicated images and consequently double entry."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f966112f",
   "metadata": {},
   "source": [
    "# 3-Twitter API data:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2002d736",
   "metadata": {},
   "source": [
    "# Visual Assesment: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8a3e6d37",
   "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>tweet_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>favorite_count</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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>892420643555336192</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>39467</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</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>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>892177421306343424</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>33819</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</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>2017-07-31 00:18:03+00:00</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>25461</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</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>2017-07-30 15:58:51+00:00</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>42908</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</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>2017-07-29 16:00:24+00:00</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>41048</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>2015-11-16 00:24:50+00:00</td>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>666049248165822464</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 120]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 666049244999131136, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>111</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>2015-11-16 00:04:52+00:00</td>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 137]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 666044217047650304, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>311</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>2015-11-15 23:21:54+00:00</td>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>666033412701032448</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 130]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 666033409081393153, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>128</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>2015-11-15 23:05:30+00:00</td>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 139]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 666029276303482880, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>132</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>2015-11-15 22:32:08+00:00</td>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>666020888022790144</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 131]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 666020881337073664, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2535</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>en</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>2354 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    created_at            tweet_id              id_str  \\\n",
       "0    2017-08-01 16:23:56+00:00  892420643555336193  892420643555336192   \n",
       "1    2017-08-01 00:17:27+00:00  892177421306343426  892177421306343424   \n",
       "2    2017-07-31 00:18:03+00:00  891815181378084864  891815181378084864   \n",
       "3    2017-07-30 15:58:51+00:00  891689557279858688  891689557279858688   \n",
       "4    2017-07-29 16:00:24+00:00  891327558926688256  891327558926688256   \n",
       "...                        ...                 ...                 ...   \n",
       "2349 2015-11-16 00:24:50+00:00  666049248165822465  666049248165822464   \n",
       "2350 2015-11-16 00:04:52+00:00  666044226329800704  666044226329800704   \n",
       "2351 2015-11-15 23:21:54+00:00  666033412701032449  666033412701032448   \n",
       "2352 2015-11-15 23:05:30+00:00  666029285002620928  666029285002620928   \n",
       "2353 2015-11-15 22:32:08+00:00  666020888022790149  666020888022790144   \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",
       "2349  Here we have a 1949 1st generation vulpix. Enj...      False   \n",
       "2350  This is a purebred Piers Morgan. Loves to Netf...      False   \n",
       "2351  Here is a very happy pup. Big fan of well-main...      False   \n",
       "2352  This is a western brown Mitsubishi terrier. Up...      False   \n",
       "2353  Here we have a Japanese Irish Setter. Lost eye...      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",
       "2349           [0, 120]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2350           [0, 137]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2351           [0, 130]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2352           [0, 139]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2353           [0, 131]  {'hashtags': [], 'symbols': [], 'user_mentions...   \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",
       "2349  {'media': [{'id': 666049244999131136, 'id_str'...   \n",
       "2350  {'media': [{'id': 666044217047650304, 'id_str'...   \n",
       "2351  {'media': [{'id': 666033409081393153, 'id_str'...   \n",
       "2352  {'media': [{'id': 666029276303482880, 'id_str'...   \n",
       "2353  {'media': [{'id': 666020881337073664, 'id_str'...   \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",
       "2349  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2350  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "      in_reply_to_status_id  ...  favorite_count  favorited  retweeted  \\\n",
       "0                       NaN  ...           39467      False      False   \n",
       "1                       NaN  ...           33819      False      False   \n",
       "2                       NaN  ...           25461      False      False   \n",
       "3                       NaN  ...           42908      False      False   \n",
       "4                       NaN  ...           41048      False      False   \n",
       "...                     ...  ...             ...        ...        ...   \n",
       "2349                    NaN  ...             111      False      False   \n",
       "2350                    NaN  ...             311      False      False   \n",
       "2351                    NaN  ...             128      False      False   \n",
       "2352                    NaN  ...             132      False      False   \n",
       "2353                    NaN  ...            2535      False      False   \n",
       "\n",
       "     possibly_sensitive possibly_sensitive_appealable  lang  retweeted_status  \\\n",
       "0                   0.0                           0.0    en               NaN   \n",
       "1                   0.0                           0.0    en               NaN   \n",
       "2                   0.0                           0.0    en               NaN   \n",
       "3                   0.0                           0.0    en               NaN   \n",
       "4                   0.0                           0.0    en               NaN   \n",
       "...                 ...                           ...   ...               ...   \n",
       "2349                0.0                           0.0    en               NaN   \n",
       "2350                0.0                           0.0    en               NaN   \n",
       "2351                0.0                           0.0    en               NaN   \n",
       "2352                0.0                           0.0    en               NaN   \n",
       "2353                0.0                           0.0    en               NaN   \n",
       "\n",
       "     quoted_status_id  quoted_status_id_str  quoted_status  \n",
       "0                 NaN                   NaN            NaN  \n",
       "1                 NaN                   NaN            NaN  \n",
       "2                 NaN                   NaN            NaN  \n",
       "3                 NaN                   NaN            NaN  \n",
       "4                 NaN                   NaN            NaN  \n",
       "...               ...                   ...            ...  \n",
       "2349              NaN                   NaN            NaN  \n",
       "2350              NaN                   NaN            NaN  \n",
       "2351              NaN                   NaN            NaN  \n",
       "2352              NaN                   NaN            NaN  \n",
       "2353              NaN                   NaN            NaN  \n",
       "\n",
       "[2354 rows x 31 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_api_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007b8c7d",
   "metadata": {},
   "source": [
    "# Programmatic Assesment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9daf6ded",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 31 columns):\n",
      " #   Column                         Non-Null Count  Dtype              \n",
      "---  ------                         --------------  -----              \n",
      " 0   created_at                     2354 non-null   datetime64[ns, UTC]\n",
      " 1   tweet_id                       2354 non-null   int64              \n",
      " 2   id_str                         2354 non-null   int64              \n",
      " 3   full_text                      2354 non-null   object             \n",
      " 4   truncated                      2354 non-null   bool               \n",
      " 5   display_text_range             2354 non-null   object             \n",
      " 6   entities                       2354 non-null   object             \n",
      " 7   extended_entities              2073 non-null   object             \n",
      " 8   source                         2354 non-null   object             \n",
      " 9   in_reply_to_status_id          78 non-null     float64            \n",
      " 10  in_reply_to_status_id_str      78 non-null     float64            \n",
      " 11  in_reply_to_user_id            78 non-null     float64            \n",
      " 12  in_reply_to_user_id_str        78 non-null     float64            \n",
      " 13  in_reply_to_screen_name        78 non-null     object             \n",
      " 14  user                           2354 non-null   object             \n",
      " 15  geo                            0 non-null      float64            \n",
      " 16  coordinates                    0 non-null      float64            \n",
      " 17  place                          1 non-null      object             \n",
      " 18  contributors                   0 non-null      float64            \n",
      " 19  is_quote_status                2354 non-null   bool               \n",
      " 20  retweet_count                  2354 non-null   int64              \n",
      " 21  favorite_count                 2354 non-null   int64              \n",
      " 22  favorited                      2354 non-null   bool               \n",
      " 23  retweeted                      2354 non-null   bool               \n",
      " 24  possibly_sensitive             2211 non-null   float64            \n",
      " 25  possibly_sensitive_appealable  2211 non-null   float64            \n",
      " 26  lang                           2354 non-null   object             \n",
      " 27  retweeted_status               179 non-null    object             \n",
      " 28  quoted_status_id               29 non-null     float64            \n",
      " 29  quoted_status_id_str           29 non-null     float64            \n",
      " 30  quoted_status                  28 non-null     object             \n",
      "dtypes: bool(4), datetime64[ns, UTC](1), float64(11), int64(4), object(11)\n",
      "memory usage: 505.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_api_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b1776df",
   "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>id_str</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_status_id_str</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>in_reply_to_user_id_str</th>\n",
       "      <th>geo</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>contributors</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>possibly_sensitive_appealable</th>\n",
       "      <th>quoted_status_id</th>\n",
       "      <th>quoted_status_id_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.354000e+03</td>\n",
       "      <td>2.354000e+03</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2354.000000</td>\n",
       "      <td>2354.000000</td>\n",
       "      <td>2211.0</td>\n",
       "      <td>2211.0</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>2.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.426978e+17</td>\n",
       "      <td>7.426978e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3164.797366</td>\n",
       "      <td>8080.968564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.162686e+17</td>\n",
       "      <td>8.162686e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.852812e+16</td>\n",
       "      <td>6.852812e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5284.770364</td>\n",
       "      <td>11814.771334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.164161e+16</td>\n",
       "      <td>6.164161e+16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.721083e+17</td>\n",
       "      <td>6.721083e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783975e+17</td>\n",
       "      <td>6.783975e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>624.500000</td>\n",
       "      <td>1415.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.888183e+17</td>\n",
       "      <td>7.888183e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.194596e+17</td>\n",
       "      <td>7.194596e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1473.500000</td>\n",
       "      <td>3603.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.340867e+17</td>\n",
       "      <td>8.340867e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.993058e+17</td>\n",
       "      <td>7.993058e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3652.000000</td>\n",
       "      <td>10122.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.664587e+17</td>\n",
       "      <td>8.664587e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79515.000000</td>\n",
       "      <td>132810.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.860534e+17</td>\n",
       "      <td>8.860534e+17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id        id_str  in_reply_to_status_id  \\\n",
       "count  2.354000e+03  2.354000e+03           7.800000e+01   \n",
       "mean   7.426978e+17  7.426978e+17           7.455079e+17   \n",
       "std    6.852812e+16  6.852812e+16           7.582492e+16   \n",
       "min    6.660209e+17  6.660209e+17           6.658147e+17   \n",
       "25%    6.783975e+17  6.783975e+17           6.757419e+17   \n",
       "50%    7.194596e+17  7.194596e+17           7.038708e+17   \n",
       "75%    7.993058e+17  7.993058e+17           8.257804e+17   \n",
       "max    8.924206e+17  8.924206e+17           8.862664e+17   \n",
       "\n",
       "       in_reply_to_status_id_str  in_reply_to_user_id  \\\n",
       "count               7.800000e+01         7.800000e+01   \n",
       "mean                7.455079e+17         2.014171e+16   \n",
       "std                 7.582492e+16         1.252797e+17   \n",
       "min                 6.658147e+17         1.185634e+07   \n",
       "25%                 6.757419e+17         3.086374e+08   \n",
       "50%                 7.038708e+17         4.196984e+09   \n",
       "75%                 8.257804e+17         4.196984e+09   \n",
       "max                 8.862664e+17         8.405479e+17   \n",
       "\n",
       "       in_reply_to_user_id_str  geo  coordinates  contributors  retweet_count  \\\n",
       "count             7.800000e+01  0.0          0.0           0.0    2354.000000   \n",
       "mean              2.014171e+16  NaN          NaN           NaN    3164.797366   \n",
       "std               1.252797e+17  NaN          NaN           NaN    5284.770364   \n",
       "min               1.185634e+07  NaN          NaN           NaN       0.000000   \n",
       "25%               3.086374e+08  NaN          NaN           NaN     624.500000   \n",
       "50%               4.196984e+09  NaN          NaN           NaN    1473.500000   \n",
       "75%               4.196984e+09  NaN          NaN           NaN    3652.000000   \n",
       "max               8.405479e+17  NaN          NaN           NaN   79515.000000   \n",
       "\n",
       "       favorite_count  possibly_sensitive  possibly_sensitive_appealable  \\\n",
       "count     2354.000000              2211.0                         2211.0   \n",
       "mean      8080.968564                 0.0                            0.0   \n",
       "std      11814.771334                 0.0                            0.0   \n",
       "min          0.000000                 0.0                            0.0   \n",
       "25%       1415.000000                 0.0                            0.0   \n",
       "50%       3603.500000                 0.0                            0.0   \n",
       "75%      10122.250000                 0.0                            0.0   \n",
       "max     132810.000000                 0.0                            0.0   \n",
       "\n",
       "       quoted_status_id  quoted_status_id_str  \n",
       "count      2.900000e+01          2.900000e+01  \n",
       "mean       8.162686e+17          8.162686e+17  \n",
       "std        6.164161e+16          6.164161e+16  \n",
       "min        6.721083e+17          6.721083e+17  \n",
       "25%        7.888183e+17          7.888183e+17  \n",
       "50%        8.340867e+17          8.340867e+17  \n",
       "75%        8.664587e+17          8.664587e+17  \n",
       "max        8.860534e+17          8.860534e+17  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_api_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be351a24",
   "metadata": {},
   "source": [
    "1-'retweet_count'and 'favorite_count' seems to have duplication issues which must be addressed in Cleaning step."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a67a921",
   "metadata": {},
   "source": [
    "2-Delete columns that won't be used for analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54bb04fd",
   "metadata": {},
   "source": [
    "# Data Cleaning:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "721e0979",
   "metadata": {},
   "source": [
    "# Copying Data:\n",
    "    First of all we need to copy this data set before making any kind of modifications:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2b54e5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copying the df_twitter_archive\n",
    "df_twitter_archive_cln = df_twitter_archive.copy()\n",
    "\n",
    "# Copying the df_tweet_image_prediction\n",
    "df_tweet_image_prediction_cln = df_tweet_image_prediction.copy() \n",
    "\n",
    "# Copying the df_twt_raw. \n",
    "df_twt_cln = df_api_data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d4d5399",
   "metadata": {},
   "source": [
    "# After making copies of data we are going to start cleaning process for the issues we highlighted:\n",
    "  \n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cfa4758",
   "metadata": {},
   "source": [
    "# Issue #1: (Quality Issue)\n",
    "  \n",
    "First one is duplication issue in df_tweet_image_prediction: image_predictions.tsv;This is data validity issue and is identified programatically.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "48cdcb55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Duplicated jpg_url.\n",
    "sum(df_tweet_image_prediction_cln.jpg_url.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f91a0a",
   "metadata": {},
   "source": [
    "Define:\n",
    "    Remove all duplicated entries in this column 'jpg_url'."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776a837b",
   "metadata": {},
   "source": [
    "Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fed337c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_tweet_image_prediction.drop_duplicates(subset='jpg_url', keep='first', inplace=False)\n",
    "\n",
    "# dropping ALL duplicate values\n",
    "df_tweet_image_prediction_cln.drop_duplicates(subset =\"jpg_url\",\n",
    "                    keep = False, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c955d40e",
   "metadata": {},
   "source": [
    "Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1ada22f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Duplicated jpg_url.\n",
    "sum(df_tweet_image_prediction_cln.jpg_url.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3debe7",
   "metadata": {},
   "source": [
    "# Issue 2:(Quality Issue)\n",
    "Issue: Visual Assesement of the Twitter archive enhance dataset shows issue in 'source' column with HTML tags url in one column.So cleaning of this column is also on of the quality issues that needs to be addressed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db71216c",
   "metadata": {},
   "source": [
    "Define:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "178ec5c6",
   "metadata": {},
   "source": [
    "Remove HTML tags and after that Create a new column to store the href, and; Assign to the source column the content between HTML tags."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb40cfc4",
   "metadata": {},
   "source": [
    "Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d9089b92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating auxiliary list to store the source and source_url.\n",
    "aux_ls_sr = []\n",
    "aux_ls_sr_url = [] \n",
    "\n",
    "# This loop will remove all HTML tags using .split() and indexing.\n",
    "for index in df_twitter_archive_cln.source:\n",
    "    aux_ls_sr.append(index.split('</a')[0].split('<a href=\"')[1].split('\" rel=\"nofollow\">')[1] )\n",
    "    aux_ls_sr_url.append(index.split('</a')[0].split('<a href=\"')[1].split('\" rel=\"nofollow\">')[0] )\n",
    "\n",
    "# Updating the source column with aux_ls_sr.\n",
    "df_twitter_archive_cln.source = aux_ls_sr\n",
    "\n",
    "# Appending a new column with source_url info.\n",
    "df_twitter_archive_cln = pd.concat([df_twitter_archive_cln, pd.DataFrame(aux_ls_sr_url, columns = ['source_url'])], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26930df0",
   "metadata": {},
   "source": [
    "Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c9deda56",
   "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>source</th>\n",
       "      <th>source_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id              source                          source_url\n",
       "0  892420643555336193  Twitter for iPhone  http://twitter.com/download/iphone\n",
       "1  892177421306343426  Twitter for iPhone  http://twitter.com/download/iphone\n",
       "2  891815181378084864  Twitter for iPhone  http://twitter.com/download/iphone\n",
       "3  891689557279858688  Twitter for iPhone  http://twitter.com/download/iphone\n",
       "4  891327558926688256  Twitter for iPhone  http://twitter.com/download/iphone"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_cln[['tweet_id','source','source_url']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e99f41ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Twitter for iPhone     2221\n",
       "Vine - Make a Scene      91\n",
       "Twitter Web Client       33\n",
       "TweetDeck                11\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_cln.source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4b887857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "http://twitter.com/download/iphone              2221\n",
       "http://vine.co                                    91\n",
       "http://twitter.com                                33\n",
       "https://about.twitter.com/products/tweetdeck      11\n",
       "Name: source_url, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_cln.source_url.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "15dcbb54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 18 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",
      " 17  source_url                  2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(11)\n",
      "memory usage: 331.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive_cln.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03931a22",
   "metadata": {},
   "source": [
    "Now we have made column source more readable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c0bcbb5",
   "metadata": {},
   "source": [
    "# Issue 3:(Tidiness Issue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c33fefeb",
   "metadata": {},
   "source": [
    "Twitter_archive -These cols show the dog breed (doggo, floofer, pupper and puppo columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f5508e",
   "metadata": {},
   "source": [
    "Define:3. \n",
    "\n",
    "Melt the doggo, floofer, pupper and puppo columns to dogitionary column. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e43d74e",
   "metadata": {},
   "source": [
    "Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6e38ef08",
   "metadata": {},
   "outputs": [],
   "source": [
    "melt_coumns = ['doggo', 'floofer', 'pupper', 'puppo']\n",
    "stay_columns = [x for x in df_twitter_archive_cln.columns.tolist() if x not in melt_coumns]\n",
    "\n",
    "# Melt the the columns into values\n",
    "df_twitter_archive_cln = pd.melt(df_twitter_archive_cln, id_vars = stay_columns, value_vars = melt_coumns, \n",
    "                         var_name = 'stages', value_name = 'dogtionary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "707b1912",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete column 'stages'\n",
    "df_twitter_archive_cln = df_twitter_archive_cln.drop('stages', 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "840ff84c",
   "metadata": {},
   "source": [
    "Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "25c32848",
   "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>source_url</th>\n",
       "      <th>dogtionary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>783839966405230592</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-06 01:23:05 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Riley. His owner put a donut pillow ar...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/783839966...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Riley</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5475</th>\n",
       "      <td>778027034220126208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-20 00:24:34 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Sophie. She's a Jubilant Bush Pupper. ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/778027034...</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>Sophie</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>885528943205470208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-13 15:58:47 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Maisey. She fell asleep mid-excavation...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/885528943...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Maisey</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6582</th>\n",
       "      <td>675149409102012420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-11 03:05:37 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>holy shit 12/10 https://t.co/p6O8X93bTQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/675149409...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7696</th>\n",
       "      <td>794926597468000259</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-05 15:37:24 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Mack. He's rather h*ckin sleepy. Excep...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/794926597...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Mack</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6177</th>\n",
       "      <td>694352839993344000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-02-02 02:53:12 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Meet Oliviér. He takes killer selfies. Has a d...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/694352839...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Oliviér</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7158</th>\n",
       "      <td>874680097055178752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-06-13 17:29:20 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Meet Cash. He hath acquired a stick. A very go...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/874680097...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Cash</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>832769181346996225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-02-18 01:50:19 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @EmilieGambril: 12/10 h*cking excited about...</td>\n",
       "      <td>8.327664e+17</td>\n",
       "      <td>487197737.0</td>\n",
       "      <td>2017-02-18 01:39:12 +0000</td>\n",
       "      <td>https://twitter.com/EmilieGambril/status/83276...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>709556954897764353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-15 01:48:55 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Cecil. She's a Gigglefloof Poofer. Out...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/709556954...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Cecil</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6186</th>\n",
       "      <td>693644216740769793</td>\n",
       "      <td>6.936422e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-01-31 03:57:23 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>BREAKING PUPDATE: I've just been notified that...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>764857477905154048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-14 16:13:27 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Aubie. He has paws for days. Nibbling ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/764857477...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Aubie</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3994</th>\n",
       "      <td>684188786104872960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-05 01:44:52 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>\"Yo Boomer I'm taking a selfie, grab your stic...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/684188786...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6814</th>\n",
       "      <td>670679630144274432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-28 19:04:19 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Pluto. He's holding little waddling do...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670679630...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Pluto</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4538</th>\n",
       "      <td>668992363537309700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-24 03:19:43 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Harrison. He braves the snow like a ch...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/668992363...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Harrison</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4875</th>\n",
       "      <td>860276583193509888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-05-04 23:34:55 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Jordy. He likes to go on adventures an...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/860276583...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Jordy</td>\n",
       "      <td>http://twitter.com/download/iphone</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",
       "3071  783839966405230592                    NaN                  NaN   \n",
       "5475  778027034220126208                    NaN                  NaN   \n",
       "34    885528943205470208                    NaN                  NaN   \n",
       "6582  675149409102012420                    NaN                  NaN   \n",
       "7696  794926597468000259                    NaN                  NaN   \n",
       "6177  694352839993344000                    NaN                  NaN   \n",
       "7158  874680097055178752                    NaN                  NaN   \n",
       "332   832769181346996225                    NaN                  NaN   \n",
       "1268  709556954897764353                    NaN                  NaN   \n",
       "6186  693644216740769793           6.936422e+17         4.196984e+09   \n",
       "5567  764857477905154048                    NaN                  NaN   \n",
       "3994  684188786104872960                    NaN                  NaN   \n",
       "6814  670679630144274432                    NaN                  NaN   \n",
       "4538  668992363537309700                    NaN                  NaN   \n",
       "4875  860276583193509888                    NaN                  NaN   \n",
       "\n",
       "                      timestamp              source  \\\n",
       "3071  2016-10-06 01:23:05 +0000  Twitter for iPhone   \n",
       "5475  2016-09-20 00:24:34 +0000  Twitter for iPhone   \n",
       "34    2017-07-13 15:58:47 +0000  Twitter for iPhone   \n",
       "6582  2015-12-11 03:05:37 +0000  Twitter for iPhone   \n",
       "7696  2016-11-05 15:37:24 +0000  Twitter for iPhone   \n",
       "6177  2016-02-02 02:53:12 +0000  Twitter for iPhone   \n",
       "7158  2017-06-13 17:29:20 +0000  Twitter for iPhone   \n",
       "332   2017-02-18 01:50:19 +0000  Twitter for iPhone   \n",
       "1268  2016-03-15 01:48:55 +0000  Twitter for iPhone   \n",
       "6186  2016-01-31 03:57:23 +0000  Twitter for iPhone   \n",
       "5567  2016-08-14 16:13:27 +0000  Twitter for iPhone   \n",
       "3994  2016-01-05 01:44:52 +0000  Twitter for iPhone   \n",
       "6814  2015-11-28 19:04:19 +0000  Twitter for iPhone   \n",
       "4538  2015-11-24 03:19:43 +0000  Twitter for iPhone   \n",
       "4875  2017-05-04 23:34:55 +0000  Twitter for iPhone   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "3071  This is Riley. His owner put a donut pillow ar...                  NaN   \n",
       "5475  This is Sophie. She's a Jubilant Bush Pupper. ...                  NaN   \n",
       "34    This is Maisey. She fell asleep mid-excavation...                  NaN   \n",
       "6582            holy shit 12/10 https://t.co/p6O8X93bTQ                  NaN   \n",
       "7696  This is Mack. He's rather h*ckin sleepy. Excep...                  NaN   \n",
       "6177  Meet Oliviér. He takes killer selfies. Has a d...                  NaN   \n",
       "7158  Meet Cash. He hath acquired a stick. A very go...                  NaN   \n",
       "332   RT @EmilieGambril: 12/10 h*cking excited about...         8.327664e+17   \n",
       "1268  This is Cecil. She's a Gigglefloof Poofer. Out...                  NaN   \n",
       "6186  BREAKING PUPDATE: I've just been notified that...                  NaN   \n",
       "5567  This is Aubie. He has paws for days. Nibbling ...                  NaN   \n",
       "3994  \"Yo Boomer I'm taking a selfie, grab your stic...                  NaN   \n",
       "6814  This is Pluto. He's holding little waddling do...                  NaN   \n",
       "4538  This is Harrison. He braves the snow like a ch...                  NaN   \n",
       "4875  This is Jordy. He likes to go on adventures an...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "3071                       NaN                        NaN   \n",
       "5475                       NaN                        NaN   \n",
       "34                         NaN                        NaN   \n",
       "6582                       NaN                        NaN   \n",
       "7696                       NaN                        NaN   \n",
       "6177                       NaN                        NaN   \n",
       "7158                       NaN                        NaN   \n",
       "332                487197737.0  2017-02-18 01:39:12 +0000   \n",
       "1268                       NaN                        NaN   \n",
       "6186                       NaN                        NaN   \n",
       "5567                       NaN                        NaN   \n",
       "3994                       NaN                        NaN   \n",
       "6814                       NaN                        NaN   \n",
       "4538                       NaN                        NaN   \n",
       "4875                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "3071  https://twitter.com/dog_rates/status/783839966...                13   \n",
       "5475  https://twitter.com/dog_rates/status/778027034...                27   \n",
       "34    https://twitter.com/dog_rates/status/885528943...                13   \n",
       "6582  https://twitter.com/dog_rates/status/675149409...                12   \n",
       "7696  https://twitter.com/dog_rates/status/794926597...                12   \n",
       "6177  https://twitter.com/dog_rates/status/694352839...                10   \n",
       "7158  https://twitter.com/dog_rates/status/874680097...                12   \n",
       "332   https://twitter.com/EmilieGambril/status/83276...                12   \n",
       "1268  https://twitter.com/dog_rates/status/709556954...                12   \n",
       "6186                                                NaN                10   \n",
       "5567  https://twitter.com/dog_rates/status/764857477...                12   \n",
       "3994  https://twitter.com/dog_rates/status/684188786...                10   \n",
       "6814  https://twitter.com/dog_rates/status/670679630...                 8   \n",
       "4538  https://twitter.com/dog_rates/status/668992363...                 8   \n",
       "4875  https://twitter.com/dog_rates/status/860276583...                12   \n",
       "\n",
       "      rating_denominator      name                          source_url  \\\n",
       "3071                  10     Riley  http://twitter.com/download/iphone   \n",
       "5475                  10    Sophie  http://twitter.com/download/iphone   \n",
       "34                    10    Maisey  http://twitter.com/download/iphone   \n",
       "6582                  10      None  http://twitter.com/download/iphone   \n",
       "7696                  10      Mack  http://twitter.com/download/iphone   \n",
       "6177                  10   Oliviér  http://twitter.com/download/iphone   \n",
       "7158                  10      Cash  http://twitter.com/download/iphone   \n",
       "332                   10      None  http://twitter.com/download/iphone   \n",
       "1268                  10     Cecil  http://twitter.com/download/iphone   \n",
       "6186                  10      None  http://twitter.com/download/iphone   \n",
       "5567                  10     Aubie  http://twitter.com/download/iphone   \n",
       "3994                  10      None  http://twitter.com/download/iphone   \n",
       "6814                  10     Pluto  http://twitter.com/download/iphone   \n",
       "4538                  10  Harrison  http://twitter.com/download/iphone   \n",
       "4875                  10     Jordy  http://twitter.com/download/iphone   \n",
       "\n",
       "     dogtionary  \n",
       "3071       None  \n",
       "5475     pupper  \n",
       "34         None  \n",
       "6582       None  \n",
       "7696       None  \n",
       "6177       None  \n",
       "7158       None  \n",
       "332        None  \n",
       "1268       None  \n",
       "6186       None  \n",
       "5567       None  \n",
       "3994       None  \n",
       "6814       None  \n",
       "4538       None  \n",
       "4875       None  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_cln.sample(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f5ccedda",
   "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>source_url</th>\n",
       "      <th>dogtionary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9399</th>\n",
       "      <td>666353288456101888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 20:32:58 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here we have a mixed Asiago from the Galápagos...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666353288...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9400</th>\n",
       "      <td>666345417576210432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 20:01:42 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Look at this jokester thinking seat belt laws ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666345417...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9401</th>\n",
       "      <td>666337882303524864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 19:31:45 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is an extremely rare horned Parthenon. No...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666337882...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>an</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9402</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 16:37:02 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is a funny dog. Weird toes. Won't come do...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666293911...</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9403</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 16:11:11 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666287406...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>an</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9404</th>\n",
       "      <td>666273097616637952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 15:14:19 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Can take selfies 11/10 https://t.co/ws2AMaNwPW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666273097...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9405</th>\n",
       "      <td>666268910803644416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 14:57:41 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Very concerned about fellow dog trapped in com...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666268910...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9406</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 04:02:55 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Not familiar with this breed. No tail (weird)....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666104133...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9407</th>\n",
       "      <td>666102155909144576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 03:55:04 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Oh my. Here you are seeing an Adobe Setter giv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666102155...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9408</th>\n",
       "      <td>666099513787052032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 03:44:34 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Can stand on stump for what seems like a while...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666099513...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9409</th>\n",
       "      <td>666094000022159362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 03:22:39 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This appears to be a Mongolian Presbyterian mi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666094000...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9410</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 02:38:37 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here we have a well-established sunblockerspan...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666082916...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9411</th>\n",
       "      <td>666073100786774016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:59:36 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Let's hope this flight isn't Malaysian (lol). ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666073100...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9412</th>\n",
       "      <td>666071193221509120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:52:02 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here we have a northern speckled Rhododendron....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666071193...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9413</th>\n",
       "      <td>666063827256086533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:22:45 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is the happiest dog you will ever see. Ve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666063827...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>the</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9414</th>\n",
       "      <td>666058600524156928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:01:59 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here is the Rand Paul of retrievers folks! He'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666058600...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>the</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9415</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:55:59 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>My oh my. This is a rare blond Canadian terrie...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666057090...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9416</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:49:46 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here is a Siberian heavily armored polar bear ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666055525...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9417</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:35:11 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is an odd dog. Hard on the outside but lo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666051853...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>an</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9418</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:30:50 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is a truly beautiful English Wilson Staff...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666050758...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9419</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9420</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9421</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9422</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9423</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</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",
       "9399  666353288456101888                    NaN                  NaN   \n",
       "9400  666345417576210432                    NaN                  NaN   \n",
       "9401  666337882303524864                    NaN                  NaN   \n",
       "9402  666293911632134144                    NaN                  NaN   \n",
       "9403  666287406224695296                    NaN                  NaN   \n",
       "9404  666273097616637952                    NaN                  NaN   \n",
       "9405  666268910803644416                    NaN                  NaN   \n",
       "9406  666104133288665088                    NaN                  NaN   \n",
       "9407  666102155909144576                    NaN                  NaN   \n",
       "9408  666099513787052032                    NaN                  NaN   \n",
       "9409  666094000022159362                    NaN                  NaN   \n",
       "9410  666082916733198337                    NaN                  NaN   \n",
       "9411  666073100786774016                    NaN                  NaN   \n",
       "9412  666071193221509120                    NaN                  NaN   \n",
       "9413  666063827256086533                    NaN                  NaN   \n",
       "9414  666058600524156928                    NaN                  NaN   \n",
       "9415  666057090499244032                    NaN                  NaN   \n",
       "9416  666055525042405380                    NaN                  NaN   \n",
       "9417  666051853826850816                    NaN                  NaN   \n",
       "9418  666050758794694657                    NaN                  NaN   \n",
       "9419  666049248165822465                    NaN                  NaN   \n",
       "9420  666044226329800704                    NaN                  NaN   \n",
       "9421  666033412701032449                    NaN                  NaN   \n",
       "9422  666029285002620928                    NaN                  NaN   \n",
       "9423  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp              source  \\\n",
       "9399  2015-11-16 20:32:58 +0000  Twitter for iPhone   \n",
       "9400  2015-11-16 20:01:42 +0000  Twitter for iPhone   \n",
       "9401  2015-11-16 19:31:45 +0000  Twitter for iPhone   \n",
       "9402  2015-11-16 16:37:02 +0000  Twitter for iPhone   \n",
       "9403  2015-11-16 16:11:11 +0000  Twitter for iPhone   \n",
       "9404  2015-11-16 15:14:19 +0000  Twitter for iPhone   \n",
       "9405  2015-11-16 14:57:41 +0000  Twitter for iPhone   \n",
       "9406  2015-11-16 04:02:55 +0000  Twitter for iPhone   \n",
       "9407  2015-11-16 03:55:04 +0000  Twitter for iPhone   \n",
       "9408  2015-11-16 03:44:34 +0000  Twitter for iPhone   \n",
       "9409  2015-11-16 03:22:39 +0000  Twitter for iPhone   \n",
       "9410  2015-11-16 02:38:37 +0000  Twitter for iPhone   \n",
       "9411  2015-11-16 01:59:36 +0000  Twitter for iPhone   \n",
       "9412  2015-11-16 01:52:02 +0000  Twitter for iPhone   \n",
       "9413  2015-11-16 01:22:45 +0000  Twitter for iPhone   \n",
       "9414  2015-11-16 01:01:59 +0000  Twitter for iPhone   \n",
       "9415  2015-11-16 00:55:59 +0000  Twitter for iPhone   \n",
       "9416  2015-11-16 00:49:46 +0000  Twitter for iPhone   \n",
       "9417  2015-11-16 00:35:11 +0000  Twitter for iPhone   \n",
       "9418  2015-11-16 00:30:50 +0000  Twitter for iPhone   \n",
       "9419  2015-11-16 00:24:50 +0000  Twitter for iPhone   \n",
       "9420  2015-11-16 00:04:52 +0000  Twitter for iPhone   \n",
       "9421  2015-11-15 23:21:54 +0000  Twitter for iPhone   \n",
       "9422  2015-11-15 23:05:30 +0000  Twitter for iPhone   \n",
       "9423  2015-11-15 22:32:08 +0000  Twitter for iPhone   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "9399  Here we have a mixed Asiago from the Galápagos...                  NaN   \n",
       "9400  Look at this jokester thinking seat belt laws ...                  NaN   \n",
       "9401  This is an extremely rare horned Parthenon. No...                  NaN   \n",
       "9402  This is a funny dog. Weird toes. Won't come do...                  NaN   \n",
       "9403  This is an Albanian 3 1/2 legged  Episcopalian...                  NaN   \n",
       "9404     Can take selfies 11/10 https://t.co/ws2AMaNwPW                  NaN   \n",
       "9405  Very concerned about fellow dog trapped in com...                  NaN   \n",
       "9406  Not familiar with this breed. No tail (weird)....                  NaN   \n",
       "9407  Oh my. Here you are seeing an Adobe Setter giv...                  NaN   \n",
       "9408  Can stand on stump for what seems like a while...                  NaN   \n",
       "9409  This appears to be a Mongolian Presbyterian mi...                  NaN   \n",
       "9410  Here we have a well-established sunblockerspan...                  NaN   \n",
       "9411  Let's hope this flight isn't Malaysian (lol). ...                  NaN   \n",
       "9412  Here we have a northern speckled Rhododendron....                  NaN   \n",
       "9413  This is the happiest dog you will ever see. Ve...                  NaN   \n",
       "9414  Here is the Rand Paul of retrievers folks! He'...                  NaN   \n",
       "9415  My oh my. This is a rare blond Canadian terrie...                  NaN   \n",
       "9416  Here is a Siberian heavily armored polar bear ...                  NaN   \n",
       "9417  This is an odd dog. Hard on the outside but lo...                  NaN   \n",
       "9418  This is a truly beautiful English Wilson Staff...                  NaN   \n",
       "9419  Here we have a 1949 1st generation vulpix. Enj...                  NaN   \n",
       "9420  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "9421  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "9422  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "9423  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "9399                       NaN                        NaN   \n",
       "9400                       NaN                        NaN   \n",
       "9401                       NaN                        NaN   \n",
       "9402                       NaN                        NaN   \n",
       "9403                       NaN                        NaN   \n",
       "9404                       NaN                        NaN   \n",
       "9405                       NaN                        NaN   \n",
       "9406                       NaN                        NaN   \n",
       "9407                       NaN                        NaN   \n",
       "9408                       NaN                        NaN   \n",
       "9409                       NaN                        NaN   \n",
       "9410                       NaN                        NaN   \n",
       "9411                       NaN                        NaN   \n",
       "9412                       NaN                        NaN   \n",
       "9413                       NaN                        NaN   \n",
       "9414                       NaN                        NaN   \n",
       "9415                       NaN                        NaN   \n",
       "9416                       NaN                        NaN   \n",
       "9417                       NaN                        NaN   \n",
       "9418                       NaN                        NaN   \n",
       "9419                       NaN                        NaN   \n",
       "9420                       NaN                        NaN   \n",
       "9421                       NaN                        NaN   \n",
       "9422                       NaN                        NaN   \n",
       "9423                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "9399  https://twitter.com/dog_rates/status/666353288...                 8   \n",
       "9400  https://twitter.com/dog_rates/status/666345417...                10   \n",
       "9401  https://twitter.com/dog_rates/status/666337882...                 9   \n",
       "9402  https://twitter.com/dog_rates/status/666293911...                 3   \n",
       "9403  https://twitter.com/dog_rates/status/666287406...                 1   \n",
       "9404  https://twitter.com/dog_rates/status/666273097...                11   \n",
       "9405  https://twitter.com/dog_rates/status/666268910...                10   \n",
       "9406  https://twitter.com/dog_rates/status/666104133...                 1   \n",
       "9407  https://twitter.com/dog_rates/status/666102155...                11   \n",
       "9408  https://twitter.com/dog_rates/status/666099513...                 8   \n",
       "9409  https://twitter.com/dog_rates/status/666094000...                 9   \n",
       "9410  https://twitter.com/dog_rates/status/666082916...                 6   \n",
       "9411  https://twitter.com/dog_rates/status/666073100...                10   \n",
       "9412  https://twitter.com/dog_rates/status/666071193...                 9   \n",
       "9413  https://twitter.com/dog_rates/status/666063827...                10   \n",
       "9414  https://twitter.com/dog_rates/status/666058600...                 8   \n",
       "9415  https://twitter.com/dog_rates/status/666057090...                 9   \n",
       "9416  https://twitter.com/dog_rates/status/666055525...                10   \n",
       "9417  https://twitter.com/dog_rates/status/666051853...                 2   \n",
       "9418  https://twitter.com/dog_rates/status/666050758...                10   \n",
       "9419  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "9420  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "9421  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "9422  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "9423  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator  name                          source_url dogtionary  \n",
       "9399                  10  None  http://twitter.com/download/iphone       None  \n",
       "9400                  10  None  http://twitter.com/download/iphone       None  \n",
       "9401                  10    an  http://twitter.com/download/iphone       None  \n",
       "9402                  10     a  http://twitter.com/download/iphone       None  \n",
       "9403                   2    an  http://twitter.com/download/iphone       None  \n",
       "9404                  10  None  http://twitter.com/download/iphone       None  \n",
       "9405                  10  None  http://twitter.com/download/iphone       None  \n",
       "9406                  10  None  http://twitter.com/download/iphone       None  \n",
       "9407                  10  None  http://twitter.com/download/iphone       None  \n",
       "9408                  10  None  http://twitter.com/download/iphone       None  \n",
       "9409                  10  None  http://twitter.com/download/iphone       None  \n",
       "9410                  10  None  http://twitter.com/download/iphone       None  \n",
       "9411                  10  None  http://twitter.com/download/iphone       None  \n",
       "9412                  10  None  http://twitter.com/download/iphone       None  \n",
       "9413                  10   the  http://twitter.com/download/iphone       None  \n",
       "9414                  10   the  http://twitter.com/download/iphone       None  \n",
       "9415                  10     a  http://twitter.com/download/iphone       None  \n",
       "9416                  10     a  http://twitter.com/download/iphone       None  \n",
       "9417                  10    an  http://twitter.com/download/iphone       None  \n",
       "9418                  10     a  http://twitter.com/download/iphone       None  \n",
       "9419                  10  None  http://twitter.com/download/iphone       None  \n",
       "9420                  10     a  http://twitter.com/download/iphone       None  \n",
       "9421                  10     a  http://twitter.com/download/iphone       None  \n",
       "9422                  10     a  http://twitter.com/download/iphone       None  \n",
       "9423                  10  None  http://twitter.com/download/iphone       None  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_cln.tail(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f7eaa757",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_twitter_archive_cln2=df_twitter_archive_cln.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e1a8620e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2750 entries, 0 to 8151\n",
      "Data columns (total 15 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2750 non-null   int64  \n",
      " 1   in_reply_to_status_id       87 non-null     float64\n",
      " 2   in_reply_to_user_id         87 non-null     float64\n",
      " 3   timestamp                   2750 non-null   object \n",
      " 4   source                      2750 non-null   object \n",
      " 5   text                        2750 non-null   object \n",
      " 6   retweeted_status_id         219 non-null    float64\n",
      " 7   retweeted_status_user_id    219 non-null    float64\n",
      " 8   retweeted_status_timestamp  219 non-null    object \n",
      " 9   expanded_urls               2685 non-null   object \n",
      " 10  rating_numerator            2750 non-null   int64  \n",
      " 11  rating_denominator          2750 non-null   int64  \n",
      " 12  name                        2750 non-null   object \n",
      " 13  source_url                  2750 non-null   object \n",
      " 14  dogtionary                  2750 non-null   object \n",
      "dtypes: float64(4), int64(3), object(8)\n",
      "memory usage: 343.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive_cln2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "167c6e81",
   "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>source_url</th>\n",
       "      <th>dogtionary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7236</th>\n",
       "      <td>859607811541651456</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-05-03 03:17:27 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Sorry for the lack of posts today. I came home...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/859607811...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7259</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>Twitter for iPhone</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>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7463</th>\n",
       "      <td>825535076884762624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-29 02:44:34 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here's a very loving and accepting puppo. Appe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/825535076...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7465</th>\n",
       "      <td>825120256414846976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-27 23:16:13 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @dog_rates: Say hello to Pablo. He's one go...</td>\n",
       "      <td>8.250266e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-01-27 17:04:02 +0000</td>\n",
       "      <td>https://www.gofundme.com/my-puppys-double-cata...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Pablo</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7466</th>\n",
       "      <td>825026590719483904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-27 17:04:02 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Say hello to Pablo. He's one gorgeous puppo. A...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.gofundme.com/my-puppys-double-cata...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Pablo</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7481</th>\n",
       "      <td>822872901745569793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-21 18:26:02 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here's a super supportive puppo participating ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/822872901...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7506</th>\n",
       "      <td>820013781606658049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-13 21:04:55 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @dog_rates: This is Oliver. He has dreams o...</td>\n",
       "      <td>8.199522e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-01-13 17:00:21 +0000</td>\n",
       "      <td>https://www.gofundme.com/servicedogoliver,http...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Oliver</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7507</th>\n",
       "      <td>819952236453363712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-13 17:00:21 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Oliver. He has dreams of being a servi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.gofundme.com/servicedogoliver,http...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Oliver</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7537</th>\n",
       "      <td>816829038950027264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-05 02:09:53 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @dog_rates: This is Betty. She's assisting ...</td>\n",
       "      <td>7.909461e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-10-25 16:00:09 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/790946055...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Betty</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7614</th>\n",
       "      <td>805823200554876929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-05 17:16:37 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @dog_rates: This is Reginald. He's one magi...</td>\n",
       "      <td>7.841832e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-10-07 00:06:50 +0000</td>\n",
       "      <td>https://vine.co/v/5ghHLBMMdlV,https://vine.co/...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Reginald</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7622</th>\n",
       "      <td>803773340896923648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-30 01:31:12 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Diogi. He fell in the pool as soon as ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/803773340...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Diogi</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7635</th>\n",
       "      <td>802239329049477120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-25 19:55:35 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Loki. He'll do your taxes for you. Can...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/802239329...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Loki</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7683</th>\n",
       "      <td>796563435802726400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-10 04:01:37 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @dog_rates: I want to finally rate this ico...</td>\n",
       "      <td>7.809316e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-09-28 00:46:20 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/780931614...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7711</th>\n",
       "      <td>793195938047070209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-31 21:00:23 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Say hello to Lily. She's pupset that her costu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/793195938...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Lily</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7731</th>\n",
       "      <td>790946055508652032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-25 16:00:09 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Betty. She's assisting with the dishes...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/790946055...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Betty</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7757</th>\n",
       "      <td>787717603741622272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-16 18:11:26 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tonks. She is a service puppo. Can hea...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/787717603...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tonks</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7769</th>\n",
       "      <td>786051337297522688</td>\n",
       "      <td>7.727430e+17</td>\n",
       "      <td>7.305050e+17</td>\n",
       "      <td>2016-10-12 03:50:17 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>13/10 for breakdancing puppo @shibbnbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7781</th>\n",
       "      <td>784183165795655680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-07 00:06:50 +0000</td>\n",
       "      <td>Vine - Make a Scene</td>\n",
       "      <td>This is Reginald. He's one magical puppo. Aero...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://vine.co/v/5ghHLBMMdlV</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Reginald</td>\n",
       "      <td>http://vine.co</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7804</th>\n",
       "      <td>780931614150983680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-28 00:46:20 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>I want to finally rate this iconic puppo who t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/780931614...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7990</th>\n",
       "      <td>756275833623502848</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-21 23:53:04 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>When ur older siblings get to play in the deep...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/756275833...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8015</th>\n",
       "      <td>752519690950500352</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-11 15:07:30 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Hopefully this puppo on a swing will help get ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/752519690...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8029</th>\n",
       "      <td>751132876104687617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-07 19:16:47 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Cooper. He's just so damn happy. 10/10...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/751132876...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Cooper</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8103</th>\n",
       "      <td>744995568523612160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-20 20:49:19 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Abby. She got her face stuck in a glas...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/744995568...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Abby</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8116</th>\n",
       "      <td>743253157753532416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-16 01:25:36 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Kilo. He cannot reach the snackum. Nif...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/743253157...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Kilo</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8151</th>\n",
       "      <td>738537504001953792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-03 01:07:16 +0000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Bayley. She fell asleep trying to esca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/738537504...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Bayley</td>\n",
       "      <td>http://twitter.com/download/iphone</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",
       "7236  859607811541651456                    NaN                  NaN   \n",
       "7259  855851453814013952                    NaN                  NaN   \n",
       "7463  825535076884762624                    NaN                  NaN   \n",
       "7465  825120256414846976                    NaN                  NaN   \n",
       "7466  825026590719483904                    NaN                  NaN   \n",
       "7481  822872901745569793                    NaN                  NaN   \n",
       "7506  820013781606658049                    NaN                  NaN   \n",
       "7507  819952236453363712                    NaN                  NaN   \n",
       "7537  816829038950027264                    NaN                  NaN   \n",
       "7614  805823200554876929                    NaN                  NaN   \n",
       "7622  803773340896923648                    NaN                  NaN   \n",
       "7635  802239329049477120                    NaN                  NaN   \n",
       "7683  796563435802726400                    NaN                  NaN   \n",
       "7711  793195938047070209                    NaN                  NaN   \n",
       "7731  790946055508652032                    NaN                  NaN   \n",
       "7757  787717603741622272                    NaN                  NaN   \n",
       "7769  786051337297522688           7.727430e+17         7.305050e+17   \n",
       "7781  784183165795655680                    NaN                  NaN   \n",
       "7804  780931614150983680                    NaN                  NaN   \n",
       "7990  756275833623502848                    NaN                  NaN   \n",
       "8015  752519690950500352                    NaN                  NaN   \n",
       "8029  751132876104687617                    NaN                  NaN   \n",
       "8103  744995568523612160                    NaN                  NaN   \n",
       "8116  743253157753532416                    NaN                  NaN   \n",
       "8151  738537504001953792                    NaN                  NaN   \n",
       "\n",
       "                      timestamp               source  \\\n",
       "7236  2017-05-03 03:17:27 +0000   Twitter for iPhone   \n",
       "7259  2017-04-22 18:31:02 +0000   Twitter for iPhone   \n",
       "7463  2017-01-29 02:44:34 +0000   Twitter for iPhone   \n",
       "7465  2017-01-27 23:16:13 +0000   Twitter for iPhone   \n",
       "7466  2017-01-27 17:04:02 +0000   Twitter for iPhone   \n",
       "7481  2017-01-21 18:26:02 +0000   Twitter for iPhone   \n",
       "7506  2017-01-13 21:04:55 +0000   Twitter for iPhone   \n",
       "7507  2017-01-13 17:00:21 +0000   Twitter for iPhone   \n",
       "7537  2017-01-05 02:09:53 +0000   Twitter for iPhone   \n",
       "7614  2016-12-05 17:16:37 +0000   Twitter for iPhone   \n",
       "7622  2016-11-30 01:31:12 +0000   Twitter for iPhone   \n",
       "7635  2016-11-25 19:55:35 +0000   Twitter for iPhone   \n",
       "7683  2016-11-10 04:01:37 +0000   Twitter for iPhone   \n",
       "7711  2016-10-31 21:00:23 +0000   Twitter for iPhone   \n",
       "7731  2016-10-25 16:00:09 +0000   Twitter for iPhone   \n",
       "7757  2016-10-16 18:11:26 +0000   Twitter for iPhone   \n",
       "7769  2016-10-12 03:50:17 +0000   Twitter for iPhone   \n",
       "7781  2016-10-07 00:06:50 +0000  Vine - Make a Scene   \n",
       "7804  2016-09-28 00:46:20 +0000   Twitter for iPhone   \n",
       "7990  2016-07-21 23:53:04 +0000   Twitter for iPhone   \n",
       "8015  2016-07-11 15:07:30 +0000   Twitter for iPhone   \n",
       "8029  2016-07-07 19:16:47 +0000   Twitter for iPhone   \n",
       "8103  2016-06-20 20:49:19 +0000   Twitter for iPhone   \n",
       "8116  2016-06-16 01:25:36 +0000   Twitter for iPhone   \n",
       "8151  2016-06-03 01:07:16 +0000   Twitter for iPhone   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "7236  Sorry for the lack of posts today. I came home...                  NaN   \n",
       "7259  Here's a puppo participating in the #ScienceMa...                  NaN   \n",
       "7463  Here's a very loving and accepting puppo. Appe...                  NaN   \n",
       "7465  RT @dog_rates: Say hello to Pablo. He's one go...         8.250266e+17   \n",
       "7466  Say hello to Pablo. He's one gorgeous puppo. A...                  NaN   \n",
       "7481  Here's a super supportive puppo participating ...                  NaN   \n",
       "7506  RT @dog_rates: This is Oliver. He has dreams o...         8.199522e+17   \n",
       "7507  This is Oliver. He has dreams of being a servi...                  NaN   \n",
       "7537  RT @dog_rates: This is Betty. She's assisting ...         7.909461e+17   \n",
       "7614  RT @dog_rates: This is Reginald. He's one magi...         7.841832e+17   \n",
       "7622  This is Diogi. He fell in the pool as soon as ...                  NaN   \n",
       "7635  This is Loki. He'll do your taxes for you. Can...                  NaN   \n",
       "7683  RT @dog_rates: I want to finally rate this ico...         7.809316e+17   \n",
       "7711  Say hello to Lily. She's pupset that her costu...                  NaN   \n",
       "7731  This is Betty. She's assisting with the dishes...                  NaN   \n",
       "7757  This is Tonks. She is a service puppo. Can hea...                  NaN   \n",
       "7769            13/10 for breakdancing puppo @shibbnbot                  NaN   \n",
       "7781  This is Reginald. He's one magical puppo. Aero...                  NaN   \n",
       "7804  I want to finally rate this iconic puppo who t...                  NaN   \n",
       "7990  When ur older siblings get to play in the deep...                  NaN   \n",
       "8015  Hopefully this puppo on a swing will help get ...                  NaN   \n",
       "8029  This is Cooper. He's just so damn happy. 10/10...                  NaN   \n",
       "8103  This is Abby. She got her face stuck in a glas...                  NaN   \n",
       "8116  This is Kilo. He cannot reach the snackum. Nif...                  NaN   \n",
       "8151  This is Bayley. She fell asleep trying to esca...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "7236                       NaN                        NaN   \n",
       "7259                       NaN                        NaN   \n",
       "7463                       NaN                        NaN   \n",
       "7465              4.196984e+09  2017-01-27 17:04:02 +0000   \n",
       "7466                       NaN                        NaN   \n",
       "7481                       NaN                        NaN   \n",
       "7506              4.196984e+09  2017-01-13 17:00:21 +0000   \n",
       "7507                       NaN                        NaN   \n",
       "7537              4.196984e+09  2016-10-25 16:00:09 +0000   \n",
       "7614              4.196984e+09  2016-10-07 00:06:50 +0000   \n",
       "7622                       NaN                        NaN   \n",
       "7635                       NaN                        NaN   \n",
       "7683              4.196984e+09  2016-09-28 00:46:20 +0000   \n",
       "7711                       NaN                        NaN   \n",
       "7731                       NaN                        NaN   \n",
       "7757                       NaN                        NaN   \n",
       "7769                       NaN                        NaN   \n",
       "7781                       NaN                        NaN   \n",
       "7804                       NaN                        NaN   \n",
       "7990                       NaN                        NaN   \n",
       "8015                       NaN                        NaN   \n",
       "8029                       NaN                        NaN   \n",
       "8103                       NaN                        NaN   \n",
       "8116                       NaN                        NaN   \n",
       "8151                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "7236  https://twitter.com/dog_rates/status/859607811...                13   \n",
       "7259  https://twitter.com/dog_rates/status/855851453...                13   \n",
       "7463  https://twitter.com/dog_rates/status/825535076...                14   \n",
       "7465  https://www.gofundme.com/my-puppys-double-cata...                12   \n",
       "7466  https://www.gofundme.com/my-puppys-double-cata...                12   \n",
       "7481  https://twitter.com/dog_rates/status/822872901...                13   \n",
       "7506  https://www.gofundme.com/servicedogoliver,http...                13   \n",
       "7507  https://www.gofundme.com/servicedogoliver,http...                13   \n",
       "7537  https://twitter.com/dog_rates/status/790946055...                12   \n",
       "7614  https://vine.co/v/5ghHLBMMdlV,https://vine.co/...                12   \n",
       "7622  https://twitter.com/dog_rates/status/803773340...                12   \n",
       "7635  https://twitter.com/dog_rates/status/802239329...                12   \n",
       "7683  https://twitter.com/dog_rates/status/780931614...                13   \n",
       "7711  https://twitter.com/dog_rates/status/793195938...                12   \n",
       "7731  https://twitter.com/dog_rates/status/790946055...                12   \n",
       "7757  https://twitter.com/dog_rates/status/787717603...                13   \n",
       "7769                                                NaN                13   \n",
       "7781                      https://vine.co/v/5ghHLBMMdlV                12   \n",
       "7804  https://twitter.com/dog_rates/status/780931614...                13   \n",
       "7990  https://twitter.com/dog_rates/status/756275833...                10   \n",
       "8015  https://twitter.com/dog_rates/status/752519690...                11   \n",
       "8029  https://twitter.com/dog_rates/status/751132876...                10   \n",
       "8103  https://twitter.com/dog_rates/status/744995568...                 9   \n",
       "8116  https://twitter.com/dog_rates/status/743253157...                10   \n",
       "8151  https://twitter.com/dog_rates/status/738537504...                11   \n",
       "\n",
       "      rating_denominator      name                          source_url  \\\n",
       "7236                  10      None  http://twitter.com/download/iphone   \n",
       "7259                  10      None  http://twitter.com/download/iphone   \n",
       "7463                  10      None  http://twitter.com/download/iphone   \n",
       "7465                  10     Pablo  http://twitter.com/download/iphone   \n",
       "7466                  10     Pablo  http://twitter.com/download/iphone   \n",
       "7481                  10      None  http://twitter.com/download/iphone   \n",
       "7506                  10    Oliver  http://twitter.com/download/iphone   \n",
       "7507                  10    Oliver  http://twitter.com/download/iphone   \n",
       "7537                  10     Betty  http://twitter.com/download/iphone   \n",
       "7614                  10  Reginald  http://twitter.com/download/iphone   \n",
       "7622                  10     Diogi  http://twitter.com/download/iphone   \n",
       "7635                  10      Loki  http://twitter.com/download/iphone   \n",
       "7683                  10      None  http://twitter.com/download/iphone   \n",
       "7711                  10      Lily  http://twitter.com/download/iphone   \n",
       "7731                  10     Betty  http://twitter.com/download/iphone   \n",
       "7757                  10     Tonks  http://twitter.com/download/iphone   \n",
       "7769                  10      None  http://twitter.com/download/iphone   \n",
       "7781                  10  Reginald                      http://vine.co   \n",
       "7804                  10      None  http://twitter.com/download/iphone   \n",
       "7990                  10      None  http://twitter.com/download/iphone   \n",
       "8015                  10      None  http://twitter.com/download/iphone   \n",
       "8029                  10    Cooper  http://twitter.com/download/iphone   \n",
       "8103                  10      Abby  http://twitter.com/download/iphone   \n",
       "8116                  10      Kilo  http://twitter.com/download/iphone   \n",
       "8151                  10    Bayley  http://twitter.com/download/iphone   \n",
       "\n",
       "     dogtionary  \n",
       "7236      puppo  \n",
       "7259      puppo  \n",
       "7463      puppo  \n",
       "7465      puppo  \n",
       "7466      puppo  \n",
       "7481      puppo  \n",
       "7506      puppo  \n",
       "7507      puppo  \n",
       "7537      puppo  \n",
       "7614      puppo  \n",
       "7622      puppo  \n",
       "7635      puppo  \n",
       "7683      puppo  \n",
       "7711      puppo  \n",
       "7731      puppo  \n",
       "7757      puppo  \n",
       "7769      puppo  \n",
       "7781      puppo  \n",
       "7804      puppo  \n",
       "7990      puppo  \n",
       "8015      puppo  \n",
       "8029      puppo  \n",
       "8103      puppo  \n",
       "8116      puppo  \n",
       "8151      puppo  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_cln2.tail(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0f2288b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_cln2.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e7f1ce32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       2356\n",
       "pupper      257\n",
       "doggo        97\n",
       "puppo        30\n",
       "floofer      10\n",
       "Name: dogtionary, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_cln2.dogtionary.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "901e21b7",
   "metadata": {},
   "source": [
    "Similarly in dataset for Twitter API(additional data)we can see that yhere are several columns which are of less or no importance and can be deleted from dataset to make cleaning process more efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f66eb69",
   "metadata": {},
   "source": [
    "# Removing Unnecessary Columns from Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cdcf7f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculates the number of columns.\n",
    "len(df_twitter_archive_cln2.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4e20f174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tweet_id',\n",
       " 'in_reply_to_status_id',\n",
       " 'in_reply_to_user_id',\n",
       " 'timestamp',\n",
       " 'source',\n",
       " 'text',\n",
       " 'retweeted_status_id',\n",
       " 'retweeted_status_user_id',\n",
       " 'retweeted_status_timestamp',\n",
       " 'expanded_urls',\n",
       " 'rating_numerator',\n",
       " 'rating_denominator',\n",
       " 'name',\n",
       " 'source_url',\n",
       " 'dogtionary']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_cln2.columns.tolist()#display names of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cdf984ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['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', 'source_url', 'dogtionary']\n"
     ]
    }
   ],
   "source": [
    "#get the column names of twitter_archive_clean\n",
    "print(list(df_twitter_archive_cln2))\n",
    "\n",
    "#CODE: Delete columns no needed\n",
    "df_twitter_archive_cln = df_twitter_archive_cln.drop(['in_reply_to_status_id',\n",
    "                                                    'in_reply_to_user_id',\n",
    "              'retweeted_status_timestamp',\n",
    "              'retweeted_status_id',\n",
    "              'retweeted_status_user_id'], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9dfff29d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2750 entries, 0 to 8151\n",
      "Data columns (total 15 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2750 non-null   int64  \n",
      " 1   in_reply_to_status_id       87 non-null     float64\n",
      " 2   in_reply_to_user_id         87 non-null     float64\n",
      " 3   timestamp                   2750 non-null   object \n",
      " 4   source                      2750 non-null   object \n",
      " 5   text                        2750 non-null   object \n",
      " 6   retweeted_status_id         219 non-null    float64\n",
      " 7   retweeted_status_user_id    219 non-null    float64\n",
      " 8   retweeted_status_timestamp  219 non-null    object \n",
      " 9   expanded_urls               2685 non-null   object \n",
      " 10  rating_numerator            2750 non-null   int64  \n",
      " 11  rating_denominator          2750 non-null   int64  \n",
      " 12  name                        2750 non-null   object \n",
      " 13  source_url                  2750 non-null   object \n",
      " 14  dogtionary                  2750 non-null   object \n",
      "dtypes: float64(4), int64(3), object(8)\n",
      "memory usage: 343.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive_cln2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfe0b1f1",
   "metadata": {},
   "source": [
    "2-for Twitter API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b245cc74",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['created_at',\n",
       " 'tweet_id',\n",
       " 'id_str',\n",
       " 'full_text',\n",
       " 'truncated',\n",
       " 'display_text_range',\n",
       " 'entities',\n",
       " 'extended_entities',\n",
       " 'source',\n",
       " 'in_reply_to_status_id',\n",
       " 'in_reply_to_status_id_str',\n",
       " 'in_reply_to_user_id',\n",
       " 'in_reply_to_user_id_str',\n",
       " 'in_reply_to_screen_name',\n",
       " 'user',\n",
       " 'geo',\n",
       " 'coordinates',\n",
       " 'place',\n",
       " 'contributors',\n",
       " 'is_quote_status',\n",
       " 'retweet_count',\n",
       " 'favorite_count',\n",
       " 'favorited',\n",
       " 'retweeted',\n",
       " 'possibly_sensitive',\n",
       " 'possibly_sensitive_appealable',\n",
       " 'lang',\n",
       " 'retweeted_status',\n",
       " 'quoted_status_id',\n",
       " 'quoted_status_id_str',\n",
       " 'quoted_status']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twt_cln.columns.tolist()#display names of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bf34cb58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 31 columns):\n",
      " #   Column                         Non-Null Count  Dtype              \n",
      "---  ------                         --------------  -----              \n",
      " 0   created_at                     2354 non-null   datetime64[ns, UTC]\n",
      " 1   tweet_id                       2354 non-null   int64              \n",
      " 2   id_str                         2354 non-null   int64              \n",
      " 3   full_text                      2354 non-null   object             \n",
      " 4   truncated                      2354 non-null   bool               \n",
      " 5   display_text_range             2354 non-null   object             \n",
      " 6   entities                       2354 non-null   object             \n",
      " 7   extended_entities              2073 non-null   object             \n",
      " 8   source                         2354 non-null   object             \n",
      " 9   in_reply_to_status_id          78 non-null     float64            \n",
      " 10  in_reply_to_status_id_str      78 non-null     float64            \n",
      " 11  in_reply_to_user_id            78 non-null     float64            \n",
      " 12  in_reply_to_user_id_str        78 non-null     float64            \n",
      " 13  in_reply_to_screen_name        78 non-null     object             \n",
      " 14  user                           2354 non-null   object             \n",
      " 15  geo                            0 non-null      float64            \n",
      " 16  coordinates                    0 non-null      float64            \n",
      " 17  place                          1 non-null      object             \n",
      " 18  contributors                   0 non-null      float64            \n",
      " 19  is_quote_status                2354 non-null   bool               \n",
      " 20  retweet_count                  2354 non-null   int64              \n",
      " 21  favorite_count                 2354 non-null   int64              \n",
      " 22  favorited                      2354 non-null   bool               \n",
      " 23  retweeted                      2354 non-null   bool               \n",
      " 24  possibly_sensitive             2211 non-null   float64            \n",
      " 25  possibly_sensitive_appealable  2211 non-null   float64            \n",
      " 26  lang                           2354 non-null   object             \n",
      " 27  retweeted_status               179 non-null    object             \n",
      " 28  quoted_status_id               29 non-null     float64            \n",
      " 29  quoted_status_id_str           29 non-null     float64            \n",
      " 30  quoted_status                  28 non-null     object             \n",
      "dtypes: bool(4), datetime64[ns, UTC](1), float64(11), int64(4), object(11)\n",
      "memory usage: 505.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twt_cln.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9d437492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['created_at', 'tweet_id', 'id_str', 'full_text', 'truncated', 'display_text_range', 'entities', 'extended_entities', 'source', 'in_reply_to_status_id', 'in_reply_to_status_id_str', 'in_reply_to_user_id', 'in_reply_to_user_id_str', 'in_reply_to_screen_name', 'user', 'geo', 'coordinates', 'place', 'contributors', 'is_quote_status', 'retweet_count', 'favorite_count', 'favorited', 'retweeted', 'possibly_sensitive', 'possibly_sensitive_appealable', 'lang', 'retweeted_status', 'quoted_status_id', 'quoted_status_id_str', 'quoted_status']\n"
     ]
    }
   ],
   "source": [
    "#get the column names of twitter_archive_clean\n",
    "print(list(df_twt_cln))\n",
    "\n",
    "#CODE: Delete columns no needed\n",
    "df_twt_cln = df_twt_cln.drop(['in_reply_to_status_id',\n",
    "                                                    'in_reply_to_user_id',\n",
    "              'in_reply_to_status_id_str',\n",
    "              'in_reply_to_user_id_str',\n",
    "              'in_reply_to_screen_name',\n",
    "             'possibly_sensitive_appealable',\n",
    "            'possibly_sensitive',\n",
    "            'quoted_status_id',\n",
    "           'quoted_status_id_str',\n",
    "            'quoted_status'\n",
    "                              \n",
    "                             ], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c1accec1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 31 columns):\n",
      " #   Column                         Non-Null Count  Dtype              \n",
      "---  ------                         --------------  -----              \n",
      " 0   created_at                     2354 non-null   datetime64[ns, UTC]\n",
      " 1   tweet_id                       2354 non-null   int64              \n",
      " 2   id_str                         2354 non-null   int64              \n",
      " 3   full_text                      2354 non-null   object             \n",
      " 4   truncated                      2354 non-null   bool               \n",
      " 5   display_text_range             2354 non-null   object             \n",
      " 6   entities                       2354 non-null   object             \n",
      " 7   extended_entities              2073 non-null   object             \n",
      " 8   source                         2354 non-null   object             \n",
      " 9   in_reply_to_status_id          78 non-null     float64            \n",
      " 10  in_reply_to_status_id_str      78 non-null     float64            \n",
      " 11  in_reply_to_user_id            78 non-null     float64            \n",
      " 12  in_reply_to_user_id_str        78 non-null     float64            \n",
      " 13  in_reply_to_screen_name        78 non-null     object             \n",
      " 14  user                           2354 non-null   object             \n",
      " 15  geo                            0 non-null      float64            \n",
      " 16  coordinates                    0 non-null      float64            \n",
      " 17  place                          1 non-null      object             \n",
      " 18  contributors                   0 non-null      float64            \n",
      " 19  is_quote_status                2354 non-null   bool               \n",
      " 20  retweet_count                  2354 non-null   int64              \n",
      " 21  favorite_count                 2354 non-null   int64              \n",
      " 22  favorited                      2354 non-null   bool               \n",
      " 23  retweeted                      2354 non-null   bool               \n",
      " 24  possibly_sensitive             2211 non-null   float64            \n",
      " 25  possibly_sensitive_appealable  2211 non-null   float64            \n",
      " 26  lang                           2354 non-null   object             \n",
      " 27  retweeted_status               179 non-null    object             \n",
      " 28  quoted_status_id               29 non-null     float64            \n",
      " 29  quoted_status_id_str           29 non-null     float64            \n",
      " 30  quoted_status                  28 non-null     object             \n",
      "dtypes: bool(4), datetime64[ns, UTC](1), float64(11), int64(4), object(11)\n",
      "memory usage: 505.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twt_cln.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8e24c6e",
   "metadata": {},
   "source": [
    "# ISSUE 5:(Quality Issue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2bde255",
   "metadata": {},
   "source": [
    "Define:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e46f5354",
   "metadata": {},
   "source": [
    "Change 'timestamp'column datatype to datetime.When doing data analysis, it is important to make sure you are using the correct data types; otherwise you may get unexpected results or errors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f1aacbb",
   "metadata": {},
   "source": [
    "Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3e66463b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_twitter_archive_cln2['timestamp'] = pd.to_datetime(df_twitter_archive_cln2['timestamp'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44fba90e",
   "metadata": {},
   "source": [
    "Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2b31815c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2750 entries, 0 to 8151\n",
      "Data columns (total 15 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2750 non-null   int64              \n",
      " 1   in_reply_to_status_id       87 non-null     float64            \n",
      " 2   in_reply_to_user_id         87 non-null     float64            \n",
      " 3   timestamp                   2750 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2750 non-null   object             \n",
      " 5   text                        2750 non-null   object             \n",
      " 6   retweeted_status_id         219 non-null    float64            \n",
      " 7   retweeted_status_user_id    219 non-null    float64            \n",
      " 8   retweeted_status_timestamp  219 non-null    object             \n",
      " 9   expanded_urls               2685 non-null   object             \n",
      " 10  rating_numerator            2750 non-null   int64              \n",
      " 11  rating_denominator          2750 non-null   int64              \n",
      " 12  name                        2750 non-null   object             \n",
      " 13  source_url                  2750 non-null   object             \n",
      " 14  dogtionary                  2750 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(4), int64(3), object(7)\n",
      "memory usage: 343.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive_cln2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69a4a60b",
   "metadata": {},
   "source": [
    "# Issue#6 (Quality Issue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054a7164",
   "metadata": {},
   "source": [
    "Define:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c07e785",
   "metadata": {},
   "source": [
    "Change datatype for tweet_id to object so that we can have Text or mixed numeric and non-numeric values.object data type can actually contain multiple different types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0e28ff69",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_twitter_archive_cln2['tweet_id'] = df_twitter_archive_cln2['tweet_id'].astype('str')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0450fa61",
   "metadata": {},
   "source": [
    "Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5b2f5348",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2750 entries, 0 to 8151\n",
      "Data columns (total 15 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2750 non-null   object             \n",
      " 1   in_reply_to_status_id       87 non-null     float64            \n",
      " 2   in_reply_to_user_id         87 non-null     float64            \n",
      " 3   timestamp                   2750 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2750 non-null   object             \n",
      " 5   text                        2750 non-null   object             \n",
      " 6   retweeted_status_id         219 non-null    float64            \n",
      " 7   retweeted_status_user_id    219 non-null    float64            \n",
      " 8   retweeted_status_timestamp  219 non-null    object             \n",
      " 9   expanded_urls               2685 non-null   object             \n",
      " 10  rating_numerator            2750 non-null   int64              \n",
      " 11  rating_denominator          2750 non-null   int64              \n",
      " 12  name                        2750 non-null   object             \n",
      " 13  source_url                  2750 non-null   object             \n",
      " 14  dogtionary                  2750 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(4), int64(2), object(8)\n",
      "memory usage: 343.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive_cln2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fb1d4b4",
   "metadata": {},
   "source": [
    "# Issue #7:(Quality Issue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ce84deb",
   "metadata": {},
   "source": [
    "When we look visually at Image Predictions data it shows dog breeds like 'p1','p2','p3' have some data starting with lowercase letters and some data is displayed with upper case letters start.so it also gives our data in theses columns look dirty so we can either convert it to lowercase."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8dce4ae",
   "metadata": {},
   "source": [
    "Define:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bcf63b1",
   "metadata": {},
   "source": [
    "In Image predictions data convert p1,p2,p3 to lowercase."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51ab419",
   "metadata": {},
   "source": [
    "Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9522ed4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweet_image_prediction_cln['p1'] = df_tweet_image_prediction_cln['p1'].str.lower()\n",
    "df_tweet_image_prediction_cln['p2'] = df_tweet_image_prediction_cln['p2'].str.lower()\n",
    "df_tweet_image_prediction_cln['p3'] = df_tweet_image_prediction_cln['p3'].str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d83d24",
   "metadata": {},
   "source": [
    "Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d09e603f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    welsh_springer_spaniel\n",
       "1                   redbone\n",
       "2           german_shepherd\n",
       "3       rhodesian_ridgeback\n",
       "4        miniature_pinscher\n",
       "5      bernese_mountain_dog\n",
       "6                box_turtle\n",
       "7                      chow\n",
       "8             shopping_cart\n",
       "9          miniature_poodle\n",
       "Name: p1, dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweet_image_prediction_cln.p1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2dc74c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                collie\n",
       "1    miniature_pinscher\n",
       "2              malinois\n",
       "3               redbone\n",
       "4            rottweiler\n",
       "5      english_springer\n",
       "6            mud_turtle\n",
       "7       tibetan_mastiff\n",
       "8       shopping_basket\n",
       "9              komondor\n",
       "Name: p2, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweet_image_prediction_cln.p2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "af1e4b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              shetland_sheepdog\n",
       "1            rhodesian_ridgeback\n",
       "2                     bloodhound\n",
       "3             miniature_pinscher\n",
       "4                       doberman\n",
       "5     greater_swiss_mountain_dog\n",
       "6                       terrapin\n",
       "7                       fur_coat\n",
       "8               golden_retriever\n",
       "9    soft-coated_wheaten_terrier\n",
       "Name: p3, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweet_image_prediction_cln.p3.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ca903e7f",
   "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>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": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweet_image_prediction.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7c5998f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweet_image_prediction.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7dbeac9",
   "metadata": {},
   "source": [
    "# ISSUE 8 (Quality Issue)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb01382",
   "metadata": {},
   "source": [
    "Define:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecd95824",
   "metadata": {},
   "source": [
    "Invalid or non standard dog names in Twitter Archive file,Check for their occurences in the dataset and then replace them with None."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e33189f2",
   "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>source_url</th>\n",
       "      <th>dogtionary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>881536004380872706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-02 15:32:16+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here is a pupper approaching maximum borkdrive...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/881536004...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>825876512159186944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-30 01:21:19+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Mo. No one will push him around in the...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/825876512...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Mo</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>819015337530290176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-11 02:57:27+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @dog_rates: This is Bo. He was a very good ...</td>\n",
       "      <td>8.190048e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-01-11 02:15:36 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/819004803...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>Bo</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>819004803107983360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-01-11 02:15:36+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Bo. He was a very good First Doggo. 14...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/819004803...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>Bo</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>804026241225523202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-30 18:16:08+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Bo. He's going to make me cry. 13/10 p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/804026241...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Bo</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5295</th>\n",
       "      <td>800188575492947969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-20 04:06:37+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>RT @dog_rates: This is Bo. He's a Benedoop Cum...</td>\n",
       "      <td>6.816941e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-12-29 04:31:49 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/681694085...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Bo</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5761</th>\n",
       "      <td>743222593470234624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-15 23:24:09+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is a very rare Great Alaskan Bush Pupper....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/743222593...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6052</th>\n",
       "      <td>704859558691414016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-02 02:43:09+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here is a heartbreaking scene of an incredible...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/704859558...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6094</th>\n",
       "      <td>700864154249383937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-02-20 02:06:50+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>\"Pupper is a present to world. Here is a bow f...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/700864154...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6395</th>\n",
       "      <td>681694085539872773</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-29 04:31:49+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Bo. He's a Benedoop Cumbersnatch. Seem...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/681694085...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Bo</td>\n",
       "      <td>http://twitter.com/download/iphone</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "56    881536004380872706                    NaN                  NaN   \n",
       "393   825876512159186944                    NaN                  NaN   \n",
       "446   819015337530290176                    NaN                  NaN   \n",
       "449   819004803107983360                    NaN                  NaN   \n",
       "553   804026241225523202                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "5295  800188575492947969                    NaN                  NaN   \n",
       "5761  743222593470234624                    NaN                  NaN   \n",
       "6052  704859558691414016                    NaN                  NaN   \n",
       "6094  700864154249383937                    NaN                  NaN   \n",
       "6395  681694085539872773                    NaN                  NaN   \n",
       "\n",
       "                     timestamp              source  \\\n",
       "56   2017-07-02 15:32:16+00:00  Twitter for iPhone   \n",
       "393  2017-01-30 01:21:19+00:00  Twitter for iPhone   \n",
       "446  2017-01-11 02:57:27+00:00  Twitter for iPhone   \n",
       "449  2017-01-11 02:15:36+00:00  Twitter for iPhone   \n",
       "553  2016-11-30 18:16:08+00:00  Twitter for iPhone   \n",
       "...                        ...                 ...   \n",
       "5295 2016-11-20 04:06:37+00:00  Twitter for iPhone   \n",
       "5761 2016-06-15 23:24:09+00:00  Twitter for iPhone   \n",
       "6052 2016-03-02 02:43:09+00:00  Twitter for iPhone   \n",
       "6094 2016-02-20 02:06:50+00:00  Twitter for iPhone   \n",
       "6395 2015-12-29 04:31:49+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "56    Here is a pupper approaching maximum borkdrive...                  NaN   \n",
       "393   This is Mo. No one will push him around in the...                  NaN   \n",
       "446   RT @dog_rates: This is Bo. He was a very good ...         8.190048e+17   \n",
       "449   This is Bo. He was a very good First Doggo. 14...                  NaN   \n",
       "553   This is Bo. He's going to make me cry. 13/10 p...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "5295  RT @dog_rates: This is Bo. He's a Benedoop Cum...         6.816941e+17   \n",
       "5761  This is a very rare Great Alaskan Bush Pupper....                  NaN   \n",
       "6052  Here is a heartbreaking scene of an incredible...                  NaN   \n",
       "6094  \"Pupper is a present to world. Here is a bow f...                  NaN   \n",
       "6395  This is Bo. He's a Benedoop Cumbersnatch. Seem...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "56                         NaN                        NaN   \n",
       "393                        NaN                        NaN   \n",
       "446               4.196984e+09  2017-01-11 02:15:36 +0000   \n",
       "449                        NaN                        NaN   \n",
       "553                        NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "5295              4.196984e+09  2015-12-29 04:31:49 +0000   \n",
       "5761                       NaN                        NaN   \n",
       "6052                       NaN                        NaN   \n",
       "6094                       NaN                        NaN   \n",
       "6395                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "56    https://twitter.com/dog_rates/status/881536004...                14   \n",
       "393   https://twitter.com/dog_rates/status/825876512...                11   \n",
       "446   https://twitter.com/dog_rates/status/819004803...                14   \n",
       "449   https://twitter.com/dog_rates/status/819004803...                14   \n",
       "553   https://twitter.com/dog_rates/status/804026241...                13   \n",
       "...                                                 ...               ...   \n",
       "5295  https://twitter.com/dog_rates/status/681694085...                11   \n",
       "5761  https://twitter.com/dog_rates/status/743222593...                12   \n",
       "6052  https://twitter.com/dog_rates/status/704859558...                10   \n",
       "6094  https://twitter.com/dog_rates/status/700864154...                12   \n",
       "6395  https://twitter.com/dog_rates/status/681694085...                11   \n",
       "\n",
       "      rating_denominator name                          source_url dogtionary  \n",
       "56                    10    a  http://twitter.com/download/iphone       None  \n",
       "393                   10   Mo  http://twitter.com/download/iphone       None  \n",
       "446                   10   Bo  http://twitter.com/download/iphone      doggo  \n",
       "449                   10   Bo  http://twitter.com/download/iphone      doggo  \n",
       "553                   10   Bo  http://twitter.com/download/iphone       None  \n",
       "...                  ...  ...                                 ...        ...  \n",
       "5295                  10   Bo  http://twitter.com/download/iphone     pupper  \n",
       "5761                  10    a  http://twitter.com/download/iphone     pupper  \n",
       "6052                  10    a  http://twitter.com/download/iphone     pupper  \n",
       "6094                  10    a  http://twitter.com/download/iphone     pupper  \n",
       "6395                  10   Bo  http://twitter.com/download/iphone     pupper  \n",
       "\n",
       "[87 rows x 15 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_cln2[df_twitter_archive_cln2['name'].apply(len) <= 2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a4e6fa7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None         913\n",
       "a             59\n",
       "Cooper        14\n",
       "Oliver        14\n",
       "Bo            13\n",
       "            ... \n",
       "Binky          1\n",
       "Ben            1\n",
       "Alexander      1\n",
       "Horace         1\n",
       "Champ          1\n",
       "Name: name, Length: 957, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inorder To further investigate this issue and check what dog name occurences are in which frequency we use following code?\n",
    "df_twitter_archive_cln2.name.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ed878e9",
   "metadata": {},
   "source": [
    "Code:\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a4b9be65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['life',\n",
       " 'infuriating',\n",
       " 'unacceptable',\n",
       " 'by',\n",
       " 'light',\n",
       " 'my',\n",
       " 'such',\n",
       " 'all',\n",
       " 'officially',\n",
       " 'space',\n",
       " 'actually',\n",
       " 'a',\n",
       " 'his',\n",
       " 'an',\n",
       " 'the',\n",
       " 'this',\n",
       " 'mad',\n",
       " 'not',\n",
       " 'one',\n",
       " 'incredibly',\n",
       " 'getting',\n",
       " 'very',\n",
       " 'old',\n",
       " 'quite',\n",
       " 'just']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialization of non name variable in array\n",
    "non_names = []\n",
    "\n",
    "# using a Loop to find non name words.\n",
    "for index in df_twitter_archive_cln2.name:\n",
    "    # names are stsrting with uppercase here and non names are with lower case letters.\n",
    "    if index.islower():\n",
    "        # If found then it will append to non_names.\n",
    "        non_names.append(index)\n",
    "\n",
    "# The list will give us unique names\n",
    "non_names = list(set(non_names))\n",
    "\n",
    "# Printing invalid names.\n",
    "non_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4afdad7",
   "metadata": {},
   "source": [
    "The above list of names displayed above has all non-standard names from 'name' column. We can use replace() method to replace it with some meaningful name or 'None'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "43d80aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to replace each non standard name (non_name).\n",
    "\n",
    "mask = df_twitter_archive_cln2.name.str.islower() \n",
    "column_name = 'name' \n",
    "df_twitter_archive_cln2.loc[mask, column_name] = np.nan\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1e3d14c",
   "metadata": {},
   "source": [
    "Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "63912902",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check if non standard names are removed from dataset column name or not:\n",
    "sum(df_twitter_archive_cln2.name.isin(non_names)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e5113f39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None         913\n",
       "Cooper        14\n",
       "Oliver        14\n",
       "Bo            13\n",
       "Charlie       13\n",
       "            ... \n",
       "Binky          1\n",
       "Ben            1\n",
       "Alexander      1\n",
       "Horace         1\n",
       "Champ          1\n",
       "Name: name, Length: 932, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_cln2.name.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3ee32e4",
   "metadata": {},
   "source": [
    "# Issue 9:Tidiness Issue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e4afac",
   "metadata": {},
   "source": [
    "In Twitter API dataset:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dffbe3c4",
   "metadata": {},
   "source": [
    "Define :\n",
    "    Data Duplication Issue in reteweet_count and favorite_count."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c54db2d3",
   "metadata": {},
   "source": [
    "Code:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4f4dfa",
   "metadata": {},
   "source": [
    "Check for duplications in id:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "62fc8717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_twt_cln.tweet_id.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1fb8965",
   "metadata": {},
   "source": [
    "So there is no duplication issue in tweet id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f40e7989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "630"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_twt_cln.retweet_count.duplicated())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "66a923b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3652    5\n",
       "1972    5\n",
       "83      5\n",
       "819     4\n",
       "115     4\n",
       "       ..\n",
       "1526    1\n",
       "230     1\n",
       "1522    1\n",
       "5616    1\n",
       "7       1\n",
       "Name: retweet_count, Length: 1724, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twt_cln.retweet_count.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "98cae10e",
   "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>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>8853</td>\n",
       "      <td>39467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>6514</td>\n",
       "      <td>33819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>4328</td>\n",
       "      <td>25461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>8964</td>\n",
       "      <td>42908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>9774</td>\n",
       "      <td>41048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>891087950875897856</td>\n",
       "      <td>3261</td>\n",
       "      <td>20562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>890971913173991426</td>\n",
       "      <td>2158</td>\n",
       "      <td>12041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>890729181411237888</td>\n",
       "      <td>16716</td>\n",
       "      <td>56848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>890609185150312448</td>\n",
       "      <td>4429</td>\n",
       "      <td>28226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>890240255349198849</td>\n",
       "      <td>7711</td>\n",
       "      <td>32467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>890006608113172480</td>\n",
       "      <td>7624</td>\n",
       "      <td>31166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>889880896479866881</td>\n",
       "      <td>5156</td>\n",
       "      <td>28268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>889665388333682689</td>\n",
       "      <td>8538</td>\n",
       "      <td>38818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>889638837579907072</td>\n",
       "      <td>4735</td>\n",
       "      <td>27672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>889531135344209921</td>\n",
       "      <td>2321</td>\n",
       "      <td>15359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>889278841981685760</td>\n",
       "      <td>5637</td>\n",
       "      <td>25652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>888917238123831296</td>\n",
       "      <td>4709</td>\n",
       "      <td>29611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>888804989199671297</td>\n",
       "      <td>4559</td>\n",
       "      <td>26080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>888554962724278272</td>\n",
       "      <td>3732</td>\n",
       "      <td>20290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>888078434458587136</td>\n",
       "      <td>3653</td>\n",
       "      <td>22201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>887705289381826560</td>\n",
       "      <td>5609</td>\n",
       "      <td>30779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>887517139158093824</td>\n",
       "      <td>12082</td>\n",
       "      <td>46959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>887473957103951883</td>\n",
       "      <td>18781</td>\n",
       "      <td>69871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>887343217045368832</td>\n",
       "      <td>10737</td>\n",
       "      <td>34222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>887101392804085760</td>\n",
       "      <td>6167</td>\n",
       "      <td>31061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>886983233522544640</td>\n",
       "      <td>8084</td>\n",
       "      <td>35859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>886736880519319552</td>\n",
       "      <td>3443</td>\n",
       "      <td>12306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>886680336477933568</td>\n",
       "      <td>4610</td>\n",
       "      <td>22798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>886366144734445568</td>\n",
       "      <td>3316</td>\n",
       "      <td>21524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>886267009285017600</td>\n",
       "      <td>4</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id  retweet_count  favorite_count\n",
       "0   892420643555336193           8853           39467\n",
       "1   892177421306343426           6514           33819\n",
       "2   891815181378084864           4328           25461\n",
       "3   891689557279858688           8964           42908\n",
       "4   891327558926688256           9774           41048\n",
       "5   891087950875897856           3261           20562\n",
       "6   890971913173991426           2158           12041\n",
       "7   890729181411237888          16716           56848\n",
       "8   890609185150312448           4429           28226\n",
       "9   890240255349198849           7711           32467\n",
       "10  890006608113172480           7624           31166\n",
       "11  889880896479866881           5156           28268\n",
       "12  889665388333682689           8538           38818\n",
       "13  889638837579907072           4735           27672\n",
       "14  889531135344209921           2321           15359\n",
       "15  889278841981685760           5637           25652\n",
       "16  888917238123831296           4709           29611\n",
       "17  888804989199671297           4559           26080\n",
       "18  888554962724278272           3732           20290\n",
       "19  888078434458587136           3653           22201\n",
       "20  887705289381826560           5609           30779\n",
       "21  887517139158093824          12082           46959\n",
       "22  887473957103951883          18781           69871\n",
       "23  887343217045368832          10737           34222\n",
       "24  887101392804085760           6167           31061\n",
       "25  886983233522544640           8084           35859\n",
       "26  886736880519319552           3443           12306\n",
       "27  886680336477933568           4610           22798\n",
       "28  886366144734445568           3316           21524\n",
       "29  886267009285017600              4             117"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twt_cln[['tweet_id','retweet_count','favorite_count']].head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5efb79bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "347"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_twt_cln.favorite_count.duplicated())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2657afb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        179\n",
       "2918       3\n",
       "2706       3\n",
       "2768       3\n",
       "1691       3\n",
       "        ... \n",
       "7493       1\n",
       "5444       1\n",
       "5442       1\n",
       "7489       1\n",
       "24574      1\n",
       "Name: favorite_count, Length: 2007, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twt_cln.favorite_count.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "28a80b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping ALL duplicate values\n",
    "df_twt_cln.drop_duplicates(subset =\"favorite_count\",\n",
    "                    keep = False, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0a1e33d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping ALL duplicate values\n",
    "df_twt_cln.drop_duplicates(subset =\"retweet_count\",\n",
    "                    keep = False, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca27471",
   "metadata": {},
   "source": [
    "Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d2902e55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_twt_cln.retweet_count.duplicated())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1f73fe5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_twt_cln.favorite_count.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f95516",
   "metadata": {},
   "source": [
    "# Merging Dataset to make one bigger master Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "b62ed617",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1280 entries, 0 to 2352\n",
      "Data columns (total 31 columns):\n",
      " #   Column                         Non-Null Count  Dtype              \n",
      "---  ------                         --------------  -----              \n",
      " 0   created_at                     1280 non-null   datetime64[ns, UTC]\n",
      " 1   tweet_id                       1280 non-null   int64              \n",
      " 2   id_str                         1280 non-null   int64              \n",
      " 3   full_text                      1280 non-null   object             \n",
      " 4   truncated                      1280 non-null   bool               \n",
      " 5   display_text_range             1280 non-null   object             \n",
      " 6   entities                       1280 non-null   object             \n",
      " 7   extended_entities              1173 non-null   object             \n",
      " 8   source                         1280 non-null   object             \n",
      " 9   in_reply_to_status_id          35 non-null     float64            \n",
      " 10  in_reply_to_status_id_str      35 non-null     float64            \n",
      " 11  in_reply_to_user_id            35 non-null     float64            \n",
      " 12  in_reply_to_user_id_str        35 non-null     float64            \n",
      " 13  in_reply_to_screen_name        35 non-null     object             \n",
      " 14  user                           1280 non-null   object             \n",
      " 15  geo                            0 non-null      float64            \n",
      " 16  coordinates                    0 non-null      float64            \n",
      " 17  place                          0 non-null      object             \n",
      " 18  contributors                   0 non-null      float64            \n",
      " 19  is_quote_status                1280 non-null   bool               \n",
      " 20  retweet_count                  1280 non-null   int64              \n",
      " 21  favorite_count                 1280 non-null   int64              \n",
      " 22  favorited                      1280 non-null   bool               \n",
      " 23  retweeted                      1280 non-null   bool               \n",
      " 24  possibly_sensitive             1257 non-null   float64            \n",
      " 25  possibly_sensitive_appealable  1257 non-null   float64            \n",
      " 26  lang                           1280 non-null   object             \n",
      " 27  retweeted_status               0 non-null      object             \n",
      " 28  quoted_status_id               20 non-null     float64            \n",
      " 29  quoted_status_id_str           20 non-null     float64            \n",
      " 30  quoted_status                  20 non-null     object             \n",
      "dtypes: bool(4), datetime64[ns, UTC](1), float64(11), int64(4), object(11)\n",
      "memory usage: 285.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twt_cln.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "151bbed8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2750 entries, 0 to 8151\n",
      "Data columns (total 15 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2750 non-null   object             \n",
      " 1   in_reply_to_status_id       87 non-null     float64            \n",
      " 2   in_reply_to_user_id         87 non-null     float64            \n",
      " 3   timestamp                   2750 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2750 non-null   object             \n",
      " 5   text                        2750 non-null   object             \n",
      " 6   retweeted_status_id         219 non-null    float64            \n",
      " 7   retweeted_status_user_id    219 non-null    float64            \n",
      " 8   retweeted_status_timestamp  219 non-null    object             \n",
      " 9   expanded_urls               2685 non-null   object             \n",
      " 10  rating_numerator            2750 non-null   int64              \n",
      " 11  rating_denominator          2750 non-null   int64              \n",
      " 12  name                        2623 non-null   object             \n",
      " 13  source_url                  2750 non-null   object             \n",
      " 14  dogtionary                  2750 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(4), int64(2), object(8)\n",
      "memory usage: 343.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive_cln2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7cc793dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#change datatype of tweet_id to object\n",
    "df_twt_cln['tweet_id'] = df_twt_cln['tweet_id'].astype('str') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fe66213a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_merge = df_dog_clean.merge(df_tweet_clean, on='tweet_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ebdc03fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1943 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  1943 non-null   int64  \n",
      " 1   jpg_url   1943 non-null   object \n",
      " 2   img_num   1943 non-null   int64  \n",
      " 3   p1        1943 non-null   object \n",
      " 4   p1_conf   1943 non-null   float64\n",
      " 5   p1_dog    1943 non-null   bool   \n",
      " 6   p2        1943 non-null   object \n",
      " 7   p2_conf   1943 non-null   float64\n",
      " 8   p2_dog    1943 non-null   bool   \n",
      " 9   p3        1943 non-null   object \n",
      " 10  p3_conf   1943 non-null   float64\n",
      " 11  p3_dog    1943 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 157.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweet_image_prediction_cln.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "177c3199",
   "metadata": {},
   "outputs": [],
   "source": [
    "#First part of datasets merging\n",
    "df_merge_clean = pd.merge(left=df_twitter_archive_cln2 ,\n",
    "                                 right=df_twt_cln, left_on='tweet_id', right_on='tweet_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "8cd86003",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1505 entries, 0 to 1504\n",
      "Data columns (total 45 columns):\n",
      " #   Column                         Non-Null Count  Dtype              \n",
      "---  ------                         --------------  -----              \n",
      " 0   tweet_id                       1505 non-null   object             \n",
      " 1   in_reply_to_status_id_x        39 non-null     float64            \n",
      " 2   in_reply_to_user_id_x          39 non-null     float64            \n",
      " 3   timestamp                      1505 non-null   datetime64[ns, UTC]\n",
      " 4   source_x                       1505 non-null   object             \n",
      " 5   text                           1505 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                  1481 non-null   object             \n",
      " 10  rating_numerator               1505 non-null   int64              \n",
      " 11  rating_denominator             1505 non-null   int64              \n",
      " 12  name                           1441 non-null   object             \n",
      " 13  source_url                     1505 non-null   object             \n",
      " 14  dogtionary                     1505 non-null   object             \n",
      " 15  created_at                     1505 non-null   datetime64[ns, UTC]\n",
      " 16  id_str                         1505 non-null   int64              \n",
      " 17  full_text                      1505 non-null   object             \n",
      " 18  truncated                      1505 non-null   bool               \n",
      " 19  display_text_range             1505 non-null   object             \n",
      " 20  entities                       1505 non-null   object             \n",
      " 21  extended_entities              1373 non-null   object             \n",
      " 22  source_y                       1505 non-null   object             \n",
      " 23  in_reply_to_status_id_y        39 non-null     float64            \n",
      " 24  in_reply_to_status_id_str      39 non-null     float64            \n",
      " 25  in_reply_to_user_id_y          39 non-null     float64            \n",
      " 26  in_reply_to_user_id_str        39 non-null     float64            \n",
      " 27  in_reply_to_screen_name        39 non-null     object             \n",
      " 28  user                           1505 non-null   object             \n",
      " 29  geo                            0 non-null      float64            \n",
      " 30  coordinates                    0 non-null      float64            \n",
      " 31  place                          0 non-null      object             \n",
      " 32  contributors                   0 non-null      float64            \n",
      " 33  is_quote_status                1505 non-null   bool               \n",
      " 34  retweet_count                  1505 non-null   int64              \n",
      " 35  favorite_count                 1505 non-null   int64              \n",
      " 36  favorited                      1505 non-null   bool               \n",
      " 37  retweeted                      1505 non-null   bool               \n",
      " 38  possibly_sensitive             1481 non-null   float64            \n",
      " 39  possibly_sensitive_appealable  1481 non-null   float64            \n",
      " 40  lang                           1505 non-null   object             \n",
      " 41  retweeted_status               0 non-null      object             \n",
      " 42  quoted_status_id               23 non-null     float64            \n",
      " 43  quoted_status_id_str           23 non-null     float64            \n",
      " 44  quoted_status                  23 non-null     object             \n",
      "dtypes: bool(4), datetime64[ns, UTC](2), float64(15), int64(5), object(19)\n",
      "memory usage: 499.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#check results of above join\n",
    "df_merge_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "00ff2134",
   "metadata": {},
   "outputs": [],
   "source": [
    "#change datatype of tweet_id to object\n",
    "df_tweet_image_prediction_cln['tweet_id'] = df_tweet_image_prediction_cln['tweet_id'].astype('str') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "8c221d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#second part of merging datasets:\n",
    "df_merge_clean = df_merge_clean.merge(df_tweet_image_prediction_cln, on='tweet_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "787983c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_merge_clean = df_merge_clean.merge(df_tweet_image_prediction_cln, on='tweet_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9054c721",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_merge_clean = df_twitter_enhanced_clean.merge(df_tweet_image_prediction_cln, on='tweet_id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "6988cf82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1303 entries, 0 to 1302\n",
      "Data columns (total 56 columns):\n",
      " #   Column                         Non-Null Count  Dtype              \n",
      "---  ------                         --------------  -----              \n",
      " 0   tweet_id                       1303 non-null   object             \n",
      " 1   in_reply_to_status_id_x        16 non-null     float64            \n",
      " 2   in_reply_to_user_id_x          16 non-null     float64            \n",
      " 3   timestamp                      1303 non-null   datetime64[ns, UTC]\n",
      " 4   source_x                       1303 non-null   object             \n",
      " 5   text                           1303 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                  1303 non-null   object             \n",
      " 10  rating_numerator               1303 non-null   int64              \n",
      " 11  rating_denominator             1303 non-null   int64              \n",
      " 12  name                           1245 non-null   object             \n",
      " 13  source_url                     1303 non-null   object             \n",
      " 14  dogtionary                     1303 non-null   object             \n",
      " 15  created_at                     1303 non-null   datetime64[ns, UTC]\n",
      " 16  id_str                         1303 non-null   int64              \n",
      " 17  full_text                      1303 non-null   object             \n",
      " 18  truncated                      1303 non-null   bool               \n",
      " 19  display_text_range             1303 non-null   object             \n",
      " 20  entities                       1303 non-null   object             \n",
      " 21  extended_entities              1303 non-null   object             \n",
      " 22  source_y                       1303 non-null   object             \n",
      " 23  in_reply_to_status_id_y        16 non-null     float64            \n",
      " 24  in_reply_to_status_id_str      16 non-null     float64            \n",
      " 25  in_reply_to_user_id_y          16 non-null     float64            \n",
      " 26  in_reply_to_user_id_str        16 non-null     float64            \n",
      " 27  in_reply_to_screen_name        16 non-null     object             \n",
      " 28  user                           1303 non-null   object             \n",
      " 29  geo                            0 non-null      float64            \n",
      " 30  coordinates                    0 non-null      float64            \n",
      " 31  place                          0 non-null      object             \n",
      " 32  contributors                   0 non-null      float64            \n",
      " 33  is_quote_status                1303 non-null   bool               \n",
      " 34  retweet_count                  1303 non-null   int64              \n",
      " 35  favorite_count                 1303 non-null   int64              \n",
      " 36  favorited                      1303 non-null   bool               \n",
      " 37  retweeted                      1303 non-null   bool               \n",
      " 38  possibly_sensitive             1303 non-null   float64            \n",
      " 39  possibly_sensitive_appealable  1303 non-null   float64            \n",
      " 40  lang                           1303 non-null   object             \n",
      " 41  retweeted_status               0 non-null      object             \n",
      " 42  quoted_status_id               0 non-null      float64            \n",
      " 43  quoted_status_id_str           0 non-null      float64            \n",
      " 44  quoted_status                  0 non-null      object             \n",
      " 45  jpg_url                        1303 non-null   object             \n",
      " 46  img_num                        1303 non-null   int64              \n",
      " 47  p1                             1303 non-null   object             \n",
      " 48  p1_conf                        1303 non-null   float64            \n",
      " 49  p1_dog                         1303 non-null   bool               \n",
      " 50  p2                             1303 non-null   object             \n",
      " 51  p2_conf                        1303 non-null   float64            \n",
      " 52  p2_dog                         1303 non-null   bool               \n",
      " 53  p3                             1303 non-null   object             \n",
      " 54  p3_conf                        1303 non-null   float64            \n",
      " 55  p3_dog                         1303 non-null   bool               \n",
      "dtypes: bool(7), datetime64[ns, UTC](2), float64(18), int64(6), object(23)\n",
      "memory usage: 517.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_merge_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d9c382b",
   "metadata": {},
   "source": [
    "# Storing Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "9f81443e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Store the clean DataFrame in a CSV file\n",
    "#pandas.DataFrame.to_csv\n",
    "df_merge_clean.to_csv('twitter_archive_master.csv', \n",
    "                 index=False, encoding = 'utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c139c355",
   "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_x</th>\n",
       "      <th>in_reply_to_user_id_x</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source_x</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>...</th>\n",
       "      <th>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>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>...</td>\n",
       "      <td>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",
       "    <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+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>...</td>\n",
       "      <td>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>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>...</td>\n",
       "      <td>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>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>...</td>\n",
       "      <td>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>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>...</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id_x  in_reply_to_user_id_x  \\\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            source_x  \\\n",
       "0  2017-08-01 16:23:56+00:00  Twitter for iPhone   \n",
       "1  2017-08-01 00:17:27+00:00  Twitter for iPhone   \n",
       "2  2017-07-31 00:18:03+00:00  Twitter for iPhone   \n",
       "3  2017-07-30 15:58:51+00:00  Twitter for iPhone   \n",
       "4  2017-07-29 16:00:24+00:00  Twitter for iPhone   \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  ...  img_num  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...  ...        1   \n",
       "1  https://twitter.com/dog_rates/status/892177421...  ...        1   \n",
       "2  https://twitter.com/dog_rates/status/891815181...  ...        1   \n",
       "3  https://twitter.com/dog_rates/status/891689557...  ...        1   \n",
       "4  https://twitter.com/dog_rates/status/891327558...  ...        2   \n",
       "\n",
       "            p1   p1_conf p1_dog                  p2   p2_conf  p2_dog  \\\n",
       "0       orange  0.097049  False               bagel  0.085851   False   \n",
       "1    chihuahua  0.323581   True            pekinese  0.090647    True   \n",
       "2    chihuahua  0.716012   True            malamute  0.078253    True   \n",
       "3  paper_towel  0.170278  False  labrador_retriever  0.168086    True   \n",
       "4       basset  0.555712   True    english_springer  0.225770    True   \n",
       "\n",
       "                            p3   p3_conf p3_dog  \n",
       "0                       banana  0.076110  False  \n",
       "1                     papillon  0.068957   True  \n",
       "2                       kelpie  0.031379   True  \n",
       "3                      spatula  0.040836  False  \n",
       "4  german_short-haired_pointer  0.175219   True  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read csv as a Pandas DataFrame\n",
    "df_twitter_archive_clean = pd.read_csv('twitter_archive_master.csv')\n",
    "df_twitter_archive_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "3f030fba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 56 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   tweet_id                       1303 non-null   int64  \n",
      " 1   in_reply_to_status_id_x        16 non-null     float64\n",
      " 2   in_reply_to_user_id_x          16 non-null     float64\n",
      " 3   timestamp                      1303 non-null   object \n",
      " 4   source_x                       1303 non-null   object \n",
      " 5   text                           1303 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      float64\n",
      " 9   expanded_urls                  1303 non-null   object \n",
      " 10  rating_numerator               1303 non-null   int64  \n",
      " 11  rating_denominator             1303 non-null   int64  \n",
      " 12  name                           1245 non-null   object \n",
      " 13  source_url                     1303 non-null   object \n",
      " 14  dogtionary                     1303 non-null   object \n",
      " 15  created_at                     1303 non-null   object \n",
      " 16  id_str                         1303 non-null   int64  \n",
      " 17  full_text                      1303 non-null   object \n",
      " 18  truncated                      1303 non-null   bool   \n",
      " 19  display_text_range             1303 non-null   object \n",
      " 20  entities                       1303 non-null   object \n",
      " 21  extended_entities              1303 non-null   object \n",
      " 22  source_y                       1303 non-null   object \n",
      " 23  in_reply_to_status_id_y        16 non-null     float64\n",
      " 24  in_reply_to_status_id_str      16 non-null     float64\n",
      " 25  in_reply_to_user_id_y          16 non-null     float64\n",
      " 26  in_reply_to_user_id_str        16 non-null     float64\n",
      " 27  in_reply_to_screen_name        16 non-null     object \n",
      " 28  user                           1303 non-null   object \n",
      " 29  geo                            0 non-null      float64\n",
      " 30  coordinates                    0 non-null      float64\n",
      " 31  place                          0 non-null      float64\n",
      " 32  contributors                   0 non-null      float64\n",
      " 33  is_quote_status                1303 non-null   bool   \n",
      " 34  retweet_count                  1303 non-null   int64  \n",
      " 35  favorite_count                 1303 non-null   int64  \n",
      " 36  favorited                      1303 non-null   bool   \n",
      " 37  retweeted                      1303 non-null   bool   \n",
      " 38  possibly_sensitive             1303 non-null   float64\n",
      " 39  possibly_sensitive_appealable  1303 non-null   float64\n",
      " 40  lang                           1303 non-null   object \n",
      " 41  retweeted_status               0 non-null      float64\n",
      " 42  quoted_status_id               0 non-null      float64\n",
      " 43  quoted_status_id_str           0 non-null      float64\n",
      " 44  quoted_status                  0 non-null      float64\n",
      " 45  jpg_url                        1303 non-null   object \n",
      " 46  img_num                        1303 non-null   int64  \n",
      " 47  p1                             1303 non-null   object \n",
      " 48  p1_conf                        1303 non-null   float64\n",
      " 49  p1_dog                         1303 non-null   bool   \n",
      " 50  p2                             1303 non-null   object \n",
      " 51  p2_conf                        1303 non-null   float64\n",
      " 52  p2_dog                         1303 non-null   bool   \n",
      " 53  p3                             1303 non-null   object \n",
      " 54  p3_conf                        1303 non-null   float64\n",
      " 55  p3_dog                         1303 non-null   bool   \n",
      "dtypes: bool(7), float64(22), int64(7), object(20)\n",
      "memory usage: 507.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e98e284a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_twitter_archive_clean.drop_duplicates(\n",
    "                    keep = False, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "01d21746",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1303 entries, 0 to 1302\n",
      "Data columns (total 56 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   tweet_id                       1303 non-null   int64  \n",
      " 1   in_reply_to_status_id_x        16 non-null     float64\n",
      " 2   in_reply_to_user_id_x          16 non-null     float64\n",
      " 3   timestamp                      1303 non-null   object \n",
      " 4   source_x                       1303 non-null   object \n",
      " 5   text                           1303 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      float64\n",
      " 9   expanded_urls                  1303 non-null   object \n",
      " 10  rating_numerator               1303 non-null   int64  \n",
      " 11  rating_denominator             1303 non-null   int64  \n",
      " 12  name                           1245 non-null   object \n",
      " 13  source_url                     1303 non-null   object \n",
      " 14  dogtionary                     1303 non-null   object \n",
      " 15  created_at                     1303 non-null   object \n",
      " 16  id_str                         1303 non-null   int64  \n",
      " 17  full_text                      1303 non-null   object \n",
      " 18  truncated                      1303 non-null   bool   \n",
      " 19  display_text_range             1303 non-null   object \n",
      " 20  entities                       1303 non-null   object \n",
      " 21  extended_entities              1303 non-null   object \n",
      " 22  source_y                       1303 non-null   object \n",
      " 23  in_reply_to_status_id_y        16 non-null     float64\n",
      " 24  in_reply_to_status_id_str      16 non-null     float64\n",
      " 25  in_reply_to_user_id_y          16 non-null     float64\n",
      " 26  in_reply_to_user_id_str        16 non-null     float64\n",
      " 27  in_reply_to_screen_name        16 non-null     object \n",
      " 28  user                           1303 non-null   object \n",
      " 29  geo                            0 non-null      float64\n",
      " 30  coordinates                    0 non-null      float64\n",
      " 31  place                          0 non-null      float64\n",
      " 32  contributors                   0 non-null      float64\n",
      " 33  is_quote_status                1303 non-null   bool   \n",
      " 34  retweet_count                  1303 non-null   int64  \n",
      " 35  favorite_count                 1303 non-null   int64  \n",
      " 36  favorited                      1303 non-null   bool   \n",
      " 37  retweeted                      1303 non-null   bool   \n",
      " 38  possibly_sensitive             1303 non-null   float64\n",
      " 39  possibly_sensitive_appealable  1303 non-null   float64\n",
      " 40  lang                           1303 non-null   object \n",
      " 41  retweeted_status               0 non-null      float64\n",
      " 42  quoted_status_id               0 non-null      float64\n",
      " 43  quoted_status_id_str           0 non-null      float64\n",
      " 44  quoted_status                  0 non-null      float64\n",
      " 45  jpg_url                        1303 non-null   object \n",
      " 46  img_num                        1303 non-null   int64  \n",
      " 47  p1                             1303 non-null   object \n",
      " 48  p1_conf                        1303 non-null   float64\n",
      " 49  p1_dog                         1303 non-null   bool   \n",
      " 50  p2                             1303 non-null   object \n",
      " 51  p2_conf                        1303 non-null   float64\n",
      " 52  p2_dog                         1303 non-null   bool   \n",
      " 53  p3                             1303 non-null   object \n",
      " 54  p3_conf                        1303 non-null   float64\n",
      " 55  p3_dog                         1303 non-null   bool   \n",
      "dtypes: bool(7), float64(22), int64(7), object(20)\n",
      "memory usage: 517.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_twitter_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca4317bc",
   "metadata": {},
   "source": [
    "# Step 5: Analyzing and Visualizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "e34ac8ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       1114\n",
       "pupper      119\n",
       "doggo        48\n",
       "puppo        17\n",
       "floofer       5\n",
       "Name: dogtionary, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_clean['dogtionary'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f8466fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "golden_retriever      102\n",
       "labrador_retriever     70\n",
       "pembroke               66\n",
       "chihuahua              40\n",
       "chow                   30\n",
       "                     ... \n",
       "comic_book              1\n",
       "convertible             1\n",
       "giant_schnauzer         1\n",
       "electric_fan            1\n",
       "ice_bear                1\n",
       "Name: p1, Length: 286, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_clean['p1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "64c0d765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "labrador_retriever    74\n",
       "golden_retriever      57\n",
       "cardigan              57\n",
       "pomeranian            33\n",
       "siberian_husky        27\n",
       "                      ..\n",
       "hamper                 1\n",
       "tick                   1\n",
       "arabian_camel          1\n",
       "toilet_seat            1\n",
       "seashore               1\n",
       "Name: p2, Length: 298, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_clean['p2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ccc854c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "labrador_retriever    46\n",
       "golden_retriever      32\n",
       "chihuahua             32\n",
       "kelpie                24\n",
       "kuvasz                23\n",
       "                      ..\n",
       "ashcan                 1\n",
       "canoe                  1\n",
       "plunger                1\n",
       "sombrero               1\n",
       "prison                 1\n",
       "Name: p3, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_twitter_archive_clean['p3'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "538c389f",
   "metadata": {},
   "source": [
    "# Data Visualisation for Most Rated Dogs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "f57656f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dog_type = df_twitter_archive_clean.groupby('p1').filter(lambda x: len(x) >= 30)\n",
    "\n",
    "df_dog_type['p1'].value_counts().plot(kind = 'bar')\n",
    "plt.title('Histogram for Most rated Dogs')\n",
    "plt.xlabel('Type of Dogs')\n",
    "plt.ylabel('Number of Dogs')\n",
    "\n",
    "fig = plt.gcf() \n",
    "fig.savefig('output.png',bbox_inches='tight');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "96d0326e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dog_type = df_twitter_archive_clean.groupby('p2').filter(lambda x: len(x) >= 30)\n",
    "\n",
    "df_dog_type['p2'].value_counts().plot(kind = 'bar')\n",
    "plt.title('Histogram for Most rated Dogs')\n",
    "plt.xlabel('Type of Dogs')\n",
    "plt.ylabel('Number of Dogs')\n",
    "\n",
    "\n",
    "fig = plt.gcf() \n",
    "fig.savefig('output.png',bbox_inches='tight');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "ee2358d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dog_type = df_twitter_archive_clean.groupby('p3').filter(lambda x: len(x) >= 30)\n",
    "\n",
    "df_dog_type['p3'].value_counts().plot(kind = 'bar')\n",
    "plt.title('Histogram for Most rated Dogs')\n",
    "plt.xlabel('Type of Dogs')\n",
    "plt.ylabel('Number of Dogs')\n",
    "\n",
    "fig = plt.gcf() \n",
    "fig.savefig('output.png',bbox_inches='tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf744d6",
   "metadata": {},
   "source": [
    "Analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92867aa2",
   "metadata": {},
   "source": [
    "golden_retriever is the most common breed of dogs that is in this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a7a4f6",
   "metadata": {},
   "source": [
    "# Visualisation based on 'DOGTIONARY'(which explians various stages of Dogs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df45df8d",
   "metadata": {},
   "source": [
    "Various Stages of Dogs Data Partition:(Pie chart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "b6eccb98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "dog_stage_count = list(df_twitter_archive_clean[df_twitter_archive_clean['dogtionary'] != 'None']['dogtionary'].value_counts())[0:4]\n",
    "plot_stages = df_twitter_archive_clean[df_twitter_archive_clean['dogtionary'] != 'None']['dogtionary'].value_counts().index.tolist()[0:4]\n",
    "explode = (0.2, 0.1, 0.1, 0.1) \n",
    "\n",
    "#matplotlib.pyplot.subplots\n",
    "#This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call.\n",
    "figure, axis = plt.subplots()\n",
    "#ref:https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.pie.html\n",
    "axis.pie(dog_stage_count, explode = explode, labels = plot_stages, shadow = True, startangle = 90)\n",
    "axis.axis('equal')# Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b734d2",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "    Based on above visualisation we can infer that Dogs which are in Pupper stage of dog life cycle get most tweets.So they are most popular stage of dogs according to data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3df560af",
   "metadata": {},
   "source": [
    "#  Scatter plot between favorites and retweets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "831f65c3",
   "metadata": {},
   "source": [
    "We want to analyse if there is any correlation between Favorite and Retweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9b6bb59e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# General configuration for all plots.\n",
    "font = 12          # X and Y axis fontsize.\n",
    "font_title = 16    # Graphic's Title fontsize.\n",
    "transparency = 0.5 # Transparency in cases of scatter plot.\n",
    "width = 0.7        # Bar width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "59673c75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= [14,6])                               # Graphic size\n",
    "plt.scatter(x = df_twitter_archive_clean['retweet_count'],                  # X axis varibale\n",
    "            y = df_twitter_archive_clean['favorite_count'],                 # Y axis variable\n",
    "            alpha = transparency)                         # Transparency\n",
    "plt.xlabel('Number of Retweets', fontsize = font)         # X label\n",
    "plt.ylabel('Number of favorite tweets', fontsize = font)         # Y label\n",
    "plt.title('ScatterPlot showing Correlation between retweets and favorite tweets.',\n",
    "          fontsize = font_title)     # Graphic Title\n",
    "plt.show()                                                # Plot the graphic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27177157",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "    The above plot shows there is a positive corelation between number of retweets and favorite tweets.Basically we can  interpret a scatterplot by looking for trends in the data as we go from left to right: If the data show an uphill pattern as we move from left to right, this indicates a positive relationship between X and Y. As the X-values increase (move right), the Y-values tend to increase (move up)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d261cfe",
   "metadata": {},
   "source": [
    "This can be further proved through calculation of or each retweet how many favourites it will generate (in average)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c08f1388",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1303.000000\n",
       "mean        3.414345\n",
       "std         1.153693\n",
       "min         1.097147\n",
       "25%         2.573548\n",
       "50%         3.239544\n",
       "75%         4.043475\n",
       "max        11.429577\n",
       "dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "favorites_rate = df_twitter_archive_clean.favorite_count/df_twitter_archive_clean.retweet_count\n",
    "\n",
    "# Calculating mean n standard deviation\n",
    "favorites_rate[np.logical_not(df_twitter_archive_clean.favorite_count.isnull())].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b7056a",
   "metadata": {},
   "source": [
    "The above calculations shows that for each new retweet the favourites will increase 3 in average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6e109a7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}