wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrangle and Analyze Data \n",
    "______"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "- [Introduction](#intro)\n",
    "- [Importing required libraries](#import)\n",
    "- [1. Gathering data](#gather)\n",
    "    - [1.a Gather data via manual CSV Download](#csv)\n",
    "    - [1.b Gather data via Requests library](#requests)\n",
    "    - [1.c Gather data via Twitter API](#api)\n",
    "\n",
    "- [2. Assessing data](#assess)\n",
    "    - [2.a Quality issues](#assess_quality)\n",
    "    - [2.b Tidiness issues](#assess_tidiness)\n",
    "    - [3.c Assessment summary](#assess_summary)\n",
    "\n",
    "- [3. Cleaning data](#clean)\n",
    "    - [3.a archive dataset](#clean-achive)\n",
    "        - [3.a.i  Quality issues](#clean-achive-quality)\n",
    "        - [3.a.ii Tidiness issues](#clean-achive-tidiness)\n",
    "    - [3.b image predictions dataset](#clean-image)\n",
    "        - [3.b.i  Quality issues](#clean-image-quality)    \n",
    "    - [3.c api dataset](#clean-api)\n",
    "        - [3.c.i  Quality issues](#clean-api-quality)\n",
    "        - [3.c.ii Tidiness issues](#clean-api-tidiness)\n",
    "    - [3.d General cleaning](#clean-general)\n",
    "    \n",
    "- [4. Storing cleaned data](#store)\n",
    "\n",
    "- [5. Analysis and Visualization](#analysis_vsualization)\n",
    "    - [5.a Ratings analysis](#rate)\n",
    "    - [5.b Favorites and retweets analysis](#fav-rt)\n",
    "    - [5.c Favorites and retweets analysis by month and days](#month-day)\n",
    "    - [5.d Dog stage analysis](#stage)\n",
    "    - [5.e Dog breed analysis](#breed)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "In this project I will gather data from a variety of sources and in a variety of formats, assess its quality and tidiness, then clean it. This is called data wrangling. I will document my wrangling efforts in this Jupyter Notebook, plus showcase them through analyses and visualizations using Python (and its libraries) and reports.\n",
    "\n",
    "The dataset that I will be wrangling (and analyzing and visualizing) is the tweet archive of Twitter user [@dog_rates](https://twitter.com/dog_rates), also known as [WeRateDogs](https://en.wikipedia.org/wiki/WeRateDogs). WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog. These ratings almost always have a denominator of 10. The numerators, though? Almost always greater than 10. 11/10, 12/10, 13/10, etc. Why? Because \"[they're good dogs Brent.](https://knowyourmeme.com/memes/theyre-good-dogs-brent)\" WeRateDogs has over 4 million followers and has received international media coverage.\n",
    "______"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='import'></a>\n",
    "## Importing required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing required libraries\n",
    "import os\n",
    "import json\n",
    "import re\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tweepy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='gather'></a>\n",
    "## 1. Gathering data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='csv'></a>\n",
    "### 1.a Gather data via manual CSV Download"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`twitter-archive-enhanced.csv` file was downloaded manually from WeRateDogs's twitter archive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>800751577355128832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-21 17:23:47 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Say hello to Mauve and Murphy. They're rather ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/800751577...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Mauve</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1780</th>\n",
       "      <td>677700003327029250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-18 04:00:46 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Ralph. He's an interpretive dancer. 10...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/677700003...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Ralph</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>777953400541634568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-19 19:31:59 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: Meet Gerald. He's a fairly exot...</td>\n",
       "      <td>7.681934e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2016-08-23 21:09:14 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/768193404...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>doggo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>688898160958271489</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-18 01:38:15 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>When you have a ton of work to do but then rem...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/688898160...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1692</th>\n",
       "      <td>681302363064414209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-28 02:35:15 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Chuck. He's a neat dog. Very flexible....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/681302363...</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Chuck</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",
       "578   800751577355128832                    NaN                  NaN   \n",
       "1780  677700003327029250                    NaN                  NaN   \n",
       "764   777953400541634568                    NaN                  NaN   \n",
       "1554  688898160958271489                    NaN                  NaN   \n",
       "1692  681302363064414209                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "578   2016-11-21 17:23:47 +0000   \n",
       "1780  2015-12-18 04:00:46 +0000   \n",
       "764   2016-09-19 19:31:59 +0000   \n",
       "1554  2016-01-18 01:38:15 +0000   \n",
       "1692  2015-12-28 02:35:15 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "578   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1780  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "764   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1554  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1692  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "578   Say hello to Mauve and Murphy. They're rather ...                  NaN   \n",
       "1780  This is Ralph. He's an interpretive dancer. 10...                  NaN   \n",
       "764   RT @dog_rates: Meet Gerald. He's a fairly exot...         7.681934e+17   \n",
       "1554  When you have a ton of work to do but then rem...                  NaN   \n",
       "1692  This is Chuck. He's a neat dog. Very flexible....                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "578                        NaN                        NaN   \n",
       "1780                       NaN                        NaN   \n",
       "764               4.196984e+09  2016-08-23 21:09:14 +0000   \n",
       "1554                       NaN                        NaN   \n",
       "1692                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "578   https://twitter.com/dog_rates/status/800751577...                12   \n",
       "1780  https://twitter.com/dog_rates/status/677700003...                10   \n",
       "764   https://twitter.com/dog_rates/status/768193404...                 8   \n",
       "1554  https://twitter.com/dog_rates/status/688898160...                10   \n",
       "1692  https://twitter.com/dog_rates/status/681302363...                 3   \n",
       "\n",
       "      rating_denominator    name  doggo floofer pupper puppo  \n",
       "578                   10   Mauve   None    None   None  None  \n",
       "1780                  10   Ralph   None    None   None  None  \n",
       "764                   10  Gerald  doggo    None   None  None  \n",
       "1554                  10    None   None    None   None  None  \n",
       "1692                  10   Chuck   None    None   None  None  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the csv file\n",
    "archive_df = pd.read_csv('twitter-archive-enhanced.csv')\n",
    "archive_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       " 'doggo',\n",
       " 'floofer',\n",
       " 'pupper',\n",
       " 'puppo']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring columns\n",
    "list(archive_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='requests'></a>\n",
    "### 1.b Gather data via Requests library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gathering additional data using `requests` library to download `image-predictions.tsv` file using provided `url`.\n",
    "\n",
    "`image-predictions.tsv` is a table full of image predictions (the top three only) alongside each tweet ID, image URL, and the image number that corresponded to the most confident prediction (numbered 1 to 4 since tweets can have up to four images)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables\n",
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "file_name = url.split('/')[-1]\n",
    "\n",
    "# response\n",
    "r = requests.get(url)\n",
    "# print(r.status_code)\n",
    "\n",
    "# writing the content to a file\n",
    "# first: Check if the file is already exist\n",
    "if not os.path.isfile(file_name):\n",
    "    with open(file_name, 'wb') as f:\n",
    "        f.write(r.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the file to dataframe\n",
    "image_predictions_df = pd.read_csv(file_name, sep='\\t')\n",
    "image_predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tweet_id',\n",
       " 'jpg_url',\n",
       " 'img_num',\n",
       " 'p1',\n",
       " 'p1_conf',\n",
       " 'p1_dog',\n",
       " 'p2',\n",
       " 'p2_conf',\n",
       " 'p2_dog',\n",
       " 'p3',\n",
       " 'p3_conf',\n",
       " 'p3_dog']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring columns\n",
    "list(image_predictions_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='api'></a>\n",
    "### 1.c Gather data via Twitter API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gathering additional data from Twitter API to get additional information about the tweets we have in `archive_df` using `tweet_id`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0004244999999993837\n",
      "{}\n"
     ]
    }
   ],
   "source": [
    "# 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 = tweepy.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, wait_on_rate_limit_notify=True)\n",
    "\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 = archive_df.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",
    "if not os.path.isfile('tweet_json.txt'):\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, wait_on_rate_limit=True, wait_on_rate_limit_notify=True, 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": "markdown",
   "metadata": {},
   "source": [
    "Creating `api_df` dataframe from the json file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of dictionaries\n",
    "tweets_data = []\n",
    "\n",
    "with open('tweet_json.txt', 'r') as f:\n",
    "    for line in f:\n",
    "        tweet_data = json.loads(line)\n",
    "        tweets_data.append(tweet_data)\n",
    "        \n",
    "api_df = pd.DataFrame(tweets_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['created_at',\n",
       " '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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring columns\n",
    "list(api_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we don't need all these columns. We are going to use only three columns and drop the others.\n",
    "- `id` \n",
    "- `retweet_count`\n",
    "- `favorite_count`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_df = api_df[['id', 'retweet_count', 'favorite_count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   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"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   id              2354 non-null   int64\n",
      " 1   retweet_count   2354 non-null   int64\n",
      " 2   favorite_count  2354 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 55.3 KB\n"
     ]
    }
   ],
   "source": [
    "api_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "______"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='assess'></a>\n",
    "## 2. Assessing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can assess data for:**\n",
    "\n",
    "- **Quality:** issues with content. Low quality data is also known as dirty data.\n",
    "- **Tidiness:** issues with structure that prevent easy analysis. Untidy data is also known as messy data.\n",
    "\n",
    "**... Using two types of assessment:**\n",
    "\n",
    "- **Visual assessment:** scrolling through the data.\n",
    "- **Programmatic assessment:** using code to view specific portions and summaries of the data (pandas' `head`, `tail`, and `info` methods, for example)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='assess_quality'></a>\n",
    "### 2.a Quality issues\n",
    "\n",
    "- **Quality issues** pertain to the content of data. Low quality data is also known as dirty data. There are four dimensions of quality data:<br><br>\n",
    "\n",
    "    - **Completeness:** do we have all of the records that we should? Do we have missing records or not? Are there specific rows, columns, or cells missing?\n",
    "    - **Validity**: we have the records, but they’re not valid, i.e., they don’t conform to a defined schema. A schema is a defined set of rules for data. These rules can be real-world constraints (e.g. negative height is impossible) and table-specific constraints (e.g. unique key constraints in tables).\n",
    "    - **Accuracy**: inaccurate data is wrong data that is valid. It adheres to the defined schema, but it is still incorrect. Example: a patient’s weight that is 5 lbs too heavy because the scale was faulty.\n",
    "    - **Consistency**: inconsistent data is both valid and accurate, but there are multiple correct ways of referring to the same thing. Consistency, i.e., a standard format, in columns that represent the same data across tables and/or within tables is desired.\n",
    "    _____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.a.i archive_df table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>27</th>\n",
       "      <td>886736880519319552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-16 23:58:41 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Mingus. He's a wonderful father to his...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.gofundme.com/mingusneedsus,https:/...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Mingus</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844</th>\n",
       "      <td>675849018447167488</td>\n",
       "      <td>6.758457e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-12-13 01:25:37 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This dog is being demoted to a 9/10 for not we...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>887101392804085760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-18 00:07:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This... is a Jubilant Antarctic House Bear. We...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887101392...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "27    886736880519319552                    NaN                  NaN   \n",
       "1844  675849018447167488           6.758457e+17         4.196984e+09   \n",
       "25    887101392804085760                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "27    2017-07-16 23:58:41 +0000   \n",
       "1844  2015-12-13 01:25:37 +0000   \n",
       "25    2017-07-18 00:07:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "27    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1844  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "25    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "27    This is Mingus. He's a wonderful father to his...                  NaN   \n",
       "1844  This dog is being demoted to a 9/10 for not we...                  NaN   \n",
       "25    This... is a Jubilant Antarctic House Bear. We...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "27                         NaN                        NaN   \n",
       "1844                       NaN                        NaN   \n",
       "25                         NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "27    https://www.gofundme.com/mingusneedsus,https:/...                13   \n",
       "1844                                                NaN                 9   \n",
       "25    https://twitter.com/dog_rates/status/887101392...                12   \n",
       "\n",
       "      rating_denominator    name doggo floofer pupper puppo  \n",
       "27                    10  Mingus  None    None   None  None  \n",
       "1844                  10    None  None    None   None  None  \n",
       "25                    10    None  None    None   None  None  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "archive_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Erroneous datatype:\n",
    "    - timestamp and retweeted_status_timestamp should be datetime\n",
    "    - tweet_id should be string\n",
    "    - Doggo, floofer, pupper, and puppo are categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.tweet_id.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2356.000000</td>\n",
       "      <td>2356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.126486</td>\n",
       "      <td>10.455433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45.876648</td>\n",
       "      <td>6.745237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rating_numerator  rating_denominator\n",
       "count       2356.000000         2356.000000\n",
       "mean          13.126486           10.455433\n",
       "std           45.876648            6.745237\n",
       "min            0.000000            0.000000\n",
       "25%           10.000000           10.000000\n",
       "50%           11.000000           10.000000\n",
       "75%           12.000000           10.000000\n",
       "max         1776.000000          170.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df[['rating_numerator', 'rating_denominator']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>This is Atticus. He's quite simply America af....</td>\n",
       "      <td>1776</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  text  rating_numerator  \\\n",
       "979  This is Atticus. He's quite simply America af....              1776   \n",
       "\n",
       "     rating_denominator  \n",
       "979                  10  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.query('rating_numerator == 1776')[['text','rating_numerator','rating_denominator']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>@jonnysun @Lin_Manuel ok jomny I know you're e...</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>@docmisterio account started on 11/15/15</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>The floofs have been released I repeat the flo...</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspir...</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>RT @dog_rates: After so many requests, this is...</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>Why does this never happen at my front door......</td>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>After so many requests, this is Bretagne. She ...</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>Say hello to this unbelievably well behaved sq...</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https...</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>This is Bluebert. He just saw that both #Final...</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99...</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>Here's a brigade of puppers. All look very pre...</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro...</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll ...</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40...</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>Yes I do realize a rating of 4/20 would've bee...</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>Two sneaky puppers were not initially seen, mo...</td>\n",
       "      <td>143</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>Someone help the girl is being mugged. Several...</td>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>This is Darrel. He just robbed a 7/11 and is i...</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>I'm aware that I could've said 20/16, but here...</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think...</td>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>Here we have an entire platoon of puppers. Tot...</td>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  rating_numerator  \\\n",
       "313   @jonnysun @Lin_Manuel ok jomny I know you're e...               960   \n",
       "342            @docmisterio account started on 11/15/15                11   \n",
       "433   The floofs have been released I repeat the flo...                84   \n",
       "516   Meet Sam. She smiles 24/7 &amp; secretly aspir...                24   \n",
       "784   RT @dog_rates: After so many requests, this is...                 9   \n",
       "902   Why does this never happen at my front door......               165   \n",
       "1068  After so many requests, this is Bretagne. She ...                 9   \n",
       "1120  Say hello to this unbelievably well behaved sq...               204   \n",
       "1165  Happy 4/20 from the squad! 13/10 for all https...                 4   \n",
       "1202  This is Bluebert. He just saw that both #Final...                50   \n",
       "1228  Happy Saturday here's 9 puppers on a bench. 99...                99   \n",
       "1254  Here's a brigade of puppers. All look very pre...                80   \n",
       "1274  From left to right:\\nCletus, Jerome, Alejandro...                45   \n",
       "1351  Here is a whole flock of puppers.  60/50 I'll ...                60   \n",
       "1433  Happy Wednesday here's a bucket of pups. 44/40...                44   \n",
       "1598  Yes I do realize a rating of 4/20 would've bee...                 4   \n",
       "1634  Two sneaky puppers were not initially seen, mo...               143   \n",
       "1635  Someone help the girl is being mugged. Several...               121   \n",
       "1662  This is Darrel. He just robbed a 7/11 and is i...                 7   \n",
       "1663  I'm aware that I could've said 20/16, but here...                20   \n",
       "1779  IT'S PUPPERGEDDON. Total of 144/120 ...I think...               144   \n",
       "1843  Here we have an entire platoon of puppers. Tot...                88   \n",
       "2335  This is an Albanian 3 1/2 legged  Episcopalian...                 1   \n",
       "\n",
       "      rating_denominator  \n",
       "313                    0  \n",
       "342                   15  \n",
       "433                   70  \n",
       "516                    7  \n",
       "784                   11  \n",
       "902                  150  \n",
       "1068                  11  \n",
       "1120                 170  \n",
       "1165                  20  \n",
       "1202                  50  \n",
       "1228                  90  \n",
       "1254                  80  \n",
       "1274                  50  \n",
       "1351                  50  \n",
       "1433                  40  \n",
       "1598                  20  \n",
       "1634                 130  \n",
       "1635                 110  \n",
       "1662                  11  \n",
       "1663                  16  \n",
       "1779                 120  \n",
       "1843                  80  \n",
       "2335                   2  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.query('rating_denominator != 10')[['text','rating_numerator','rating_denominator']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1120    170\n",
       "902     150\n",
       "1634    130\n",
       "1779    120\n",
       "1635    110\n",
       "       ... \n",
       "1575     10\n",
       "1576     10\n",
       "516       7\n",
       "2335      2\n",
       "313       0\n",
       "Name: rating_denominator, Length: 2356, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.rating_denominator.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "979     1776\n",
       "313      960\n",
       "189      666\n",
       "188      420\n",
       "2074     420\n",
       "        ... \n",
       "2338       1\n",
       "2261       1\n",
       "2335       1\n",
       "1016       0\n",
       "315        0\n",
       "Name: rating_numerator, Length: 2356, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.rating_numerator.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- invalid and inconsistent rating data is both `rating_numerator` and `rating_denominator`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       745\n",
       "a           55\n",
       "Charlie     12\n",
       "Oliver      11\n",
       "Lucy        11\n",
       "          ... \n",
       "Ember        1\n",
       "Charl        1\n",
       "Tango        1\n",
       "Jazz         1\n",
       "Chubbs       1\n",
       "Name: name, Length: 957, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.name.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_______"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default\n",
    "# pd.set_option('display.max_colwidth', 50)\n",
    "# max\n",
    "pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "________"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>881536004380872706</td>\n",
       "      <td>Here is a pupper approaching maximum borkdrive. Zooming at never before seen speeds. 14/10 paw-inspiring af \\n(IG: puffie_the_chow) https://t.co/ghXBIIeQZF</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>792913359805018113</td>\n",
       "      <td>Here is a perfect example of someone who has their priorities in order. 13/10 for both owner and Forrest https://t.co/LRyMrU7Wfq</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>772581559778025472</td>\n",
       "      <td>Guys this is getting so out of hand. We only rate dogs. This is a Galapagos Speed Panda. Pls only send dogs... 10/10 https://t.co/8lpAGaZRFn</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>747885874273214464</td>\n",
       "      <td>This is a mighty rare blue-tailed hammer sherk. Human almost lost a limb trying to take these. Be careful guys. 8/10 https://t.co/TGenMeXreW</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>747816857231626240</td>\n",
       "      <td>Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  \\\n",
       "56    881536004380872706   \n",
       "649   792913359805018113   \n",
       "801   772581559778025472   \n",
       "1002  747885874273214464   \n",
       "1004  747816857231626240   \n",
       "\n",
       "                                                                                                                                                             text  \\\n",
       "56    Here is a pupper approaching maximum borkdrive. Zooming at never before seen speeds. 14/10 paw-inspiring af \\n(IG: puffie_the_chow) https://t.co/ghXBIIeQZF   \n",
       "649                              Here is a perfect example of someone who has their priorities in order. 13/10 for both owner and Forrest https://t.co/LRyMrU7Wfq   \n",
       "801                  Guys this is getting so out of hand. We only rate dogs. This is a Galapagos Speed Panda. Pls only send dogs... 10/10 https://t.co/8lpAGaZRFn   \n",
       "1002                 This is a mighty rare blue-tailed hammer sherk. Human almost lost a limb trying to take these. Be careful guys. 8/10 https://t.co/TGenMeXreW   \n",
       "1004                   Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R   \n",
       "\n",
       "     name  \n",
       "56      a  \n",
       "649     a  \n",
       "801     a  \n",
       "1002    a  \n",
       "1004    a  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking names\n",
    "# name == a\n",
    "archive_df.query('name == \"a\"')[['tweet_id','text','name']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1035     Abby\n",
       "1021     Abby\n",
       "938       Ace\n",
       "1933     Acro\n",
       "1327    Adele\n",
       "        ...  \n",
       "1031     very\n",
       "773      very\n",
       "1097     very\n",
       "819      very\n",
       "1385     very\n",
       "Name: name, Length: 2356, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.name.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>778396591732486144</td>\n",
       "      <td>RT @dog_rates: This is an East African Chalupa Seal. We only rate dogs. Please only send in dogs. Thank you... 10/10 https://t.co/iHe6liLwWR</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>746369468511756288</td>\n",
       "      <td>This is an Iraqi Speed Kangaroo. It is not a dog. Please only send in dogs. I'm very angry with all of you ...9/10 https://t.co/5qpBTTpgUt</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>703041949650034688</td>\n",
       "      <td>This is an East African Chalupa Seal. We only rate dogs. Please only send in dogs. Thank you... 10/10 https://t.co/iHe6liLwWR</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>668636665813057536</td>\n",
       "      <td>This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>666337882303524864</td>\n",
       "      <td>This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  \\\n",
       "759   778396591732486144   \n",
       "1025  746369468511756288   \n",
       "1362  703041949650034688   \n",
       "2204  668636665813057536   \n",
       "2333  666337882303524864   \n",
       "2335  666287406224695296   \n",
       "2349  666051853826850816   \n",
       "\n",
       "                                                                                                                                              text  \\\n",
       "759   RT @dog_rates: This is an East African Chalupa Seal. We only rate dogs. Please only send in dogs. Thank you... 10/10 https://t.co/iHe6liLwWR   \n",
       "1025    This is an Iraqi Speed Kangaroo. It is not a dog. Please only send in dogs. I'm very angry with all of you ...9/10 https://t.co/5qpBTTpgUt   \n",
       "1362                 This is an East African Chalupa Seal. We only rate dogs. Please only send in dogs. Thank you... 10/10 https://t.co/iHe6liLwWR   \n",
       "2204   This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg   \n",
       "2333   This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL   \n",
       "2335      This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv   \n",
       "2349    This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc   \n",
       "\n",
       "     name  \n",
       "759    an  \n",
       "1025   an  \n",
       "1362   an  \n",
       "2204   an  \n",
       "2333   an  \n",
       "2335   an  \n",
       "2349   an  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# name == an\n",
    "archive_df.query('name == \"an\"')[['tweet_id', 'text', 'name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1527</th>\n",
       "      <td>690360449368465409</td>\n",
       "      <td>Stop sending in lobsters. This is the final warning. We only rate dogs. Thank you... 9/10 https://t.co/B9ZXXKJYNx</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1603</th>\n",
       "      <td>685943807276412928</td>\n",
       "      <td>This is the newly formed pupper a capella group. They're just starting out but I see tons of potential. 8/10 for all https://t.co/wbAcvFoNtn</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1797</th>\n",
       "      <td>677269281705472000</td>\n",
       "      <td>This is the happiest pupper I've ever seen. 10/10 would trade lives with https://t.co/ep8ATEJwRb</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1815</th>\n",
       "      <td>676613908052996102</td>\n",
       "      <td>This is the saddest/sweetest/best picture I've been sent. 12/10 😢🐶 https://t.co/vQ2Lw1BLBF</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2037</th>\n",
       "      <td>671561002136281088</td>\n",
       "      <td>This is the best thing I've ever seen so spread it like wildfire &amp;amp; maybe we'll find the genius who created it. 13/10 https://t.co/q6RsuOVYwU</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>668587383441514497</td>\n",
       "      <td>Never forget this vine. You will not stop watching for at least 15 minutes. This is the second coveted.. 13/10 https://t.co/roqIxCvEB3</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2345</th>\n",
       "      <td>666063827256086533</td>\n",
       "      <td>This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2346</th>\n",
       "      <td>666058600524156928</td>\n",
       "      <td>Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  \\\n",
       "1527  690360449368465409   \n",
       "1603  685943807276412928   \n",
       "1797  677269281705472000   \n",
       "1815  676613908052996102   \n",
       "2037  671561002136281088   \n",
       "2212  668587383441514497   \n",
       "2345  666063827256086533   \n",
       "2346  666058600524156928   \n",
       "\n",
       "                                                                                                                                                  text  \\\n",
       "1527                                 Stop sending in lobsters. This is the final warning. We only rate dogs. Thank you... 9/10 https://t.co/B9ZXXKJYNx   \n",
       "1603      This is the newly formed pupper a capella group. They're just starting out but I see tons of potential. 8/10 for all https://t.co/wbAcvFoNtn   \n",
       "1797                                                  This is the happiest pupper I've ever seen. 10/10 would trade lives with https://t.co/ep8ATEJwRb   \n",
       "1815                                                        This is the saddest/sweetest/best picture I've been sent. 12/10 😢🐶 https://t.co/vQ2Lw1BLBF   \n",
       "2037  This is the best thing I've ever seen so spread it like wildfire &amp; maybe we'll find the genius who created it. 13/10 https://t.co/q6RsuOVYwU   \n",
       "2212            Never forget this vine. You will not stop watching for at least 15 minutes. This is the second coveted.. 13/10 https://t.co/roqIxCvEB3   \n",
       "2345                                       This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK   \n",
       "2346           Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p   \n",
       "\n",
       "     name  \n",
       "1527  the  \n",
       "1603  the  \n",
       "1797  the  \n",
       "1815  the  \n",
       "2037  the  \n",
       "2212  the  \n",
       "2345  the  \n",
       "2346  the  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# name == the\n",
    "archive_df.query('name == \"the\"')[['tweet_id', 'text', 'name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>776249906839351296</td>\n",
       "      <td>RT @dog_rates: We only rate dogs. Pls stop sending in non-canines like this Mongolian grass snake. This is very frustrating. 11/10 https://…</td>\n",
       "      <td>very</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>770655142660169732</td>\n",
       "      <td>We only rate dogs. Pls stop sending in non-canines like this Arctic Floof Kangaroo. This is very frustrating. 11/10 https://t.co/qlUDuPoE3d</td>\n",
       "      <td>very</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>745422732645535745</td>\n",
       "      <td>We only rate dogs. Pls stop sending in non-canines like this Jamaican Flop Seal. This is very very frustrating. 9/10 https://t.co/nc53zEN0hZ</td>\n",
       "      <td>very</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>736225175608430592</td>\n",
       "      <td>We only rate dogs. Please stop sending in non-canines like this Alaskan Flop Turtle. This is very frustrating. 10/10 https://t.co/qXteK6Atxc</td>\n",
       "      <td>very</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>700747788515020802</td>\n",
       "      <td>We only rate dogs. Pls stop sending in non-canines like this Mongolian grass snake. This is very frustrating. 11/10 https://t.co/22x9SbCYCU</td>\n",
       "      <td>very</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  \\\n",
       "773   776249906839351296   \n",
       "819   770655142660169732   \n",
       "1031  745422732645535745   \n",
       "1097  736225175608430592   \n",
       "1385  700747788515020802   \n",
       "\n",
       "                                                                                                                                              text  \\\n",
       "773   RT @dog_rates: We only rate dogs. Pls stop sending in non-canines like this Mongolian grass snake. This is very frustrating. 11/10 https://…   \n",
       "819    We only rate dogs. Pls stop sending in non-canines like this Arctic Floof Kangaroo. This is very frustrating. 11/10 https://t.co/qlUDuPoE3d   \n",
       "1031  We only rate dogs. Pls stop sending in non-canines like this Jamaican Flop Seal. This is very very frustrating. 9/10 https://t.co/nc53zEN0hZ   \n",
       "1097  We only rate dogs. Please stop sending in non-canines like this Alaskan Flop Turtle. This is very frustrating. 10/10 https://t.co/qXteK6Atxc   \n",
       "1385   We only rate dogs. Pls stop sending in non-canines like this Mongolian grass snake. This is very frustrating. 11/10 https://t.co/22x9SbCYCU   \n",
       "\n",
       "      name  \n",
       "773   very  \n",
       "819   very  \n",
       "1031  very  \n",
       "1097  very  \n",
       "1385  very  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# name == very \n",
    "archive_df.query('name == \"very\"')[['tweet_id', 'text', 'name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Invaled names in `name` column. (a, an, the, very)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.query('(doggo != \"None\")')['name'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None     2259\n",
       "doggo      97\n",
       "Name: doggo, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.doggo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.query('(floofer != \"None\")')['name'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       2346\n",
       "floofer      10\n",
       "Name: floofer, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.floofer.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "257"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.query('(pupper != \"None\")')['name'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None      2099\n",
       "pupper     257\n",
       "Name: pupper, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.pupper.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.query('(puppo != \"None\")')['name'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None     2326\n",
       "puppo      30\n",
       "Name: puppo, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.puppo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>text</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>At first I thought this was a shy doggo, but it's actually a Rare Canadian Floofer Owl. Amateurs would confuse the two. 11/10 only send dogs https://t.co/TXdT3tmuYk</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>floofer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                     text  \\\n",
       "200  At first I thought this was a shy doggo, but it's actually a Rare Canadian Floofer Owl. Amateurs would confuse the two. 11/10 only send dogs https://t.co/TXdT3tmuYk   \n",
       "\n",
       "     name  doggo  floofer pupper puppo  \n",
       "200  None  doggo  floofer   None  None  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " archive_df.query('(doggo != \"None\") & (floofer != \"None\")')[['text','name', 'doggo','floofer', 'pupper','puppo']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.a.ii image_predictions_df table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "image_predictions_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ('p1', 'p1_conf', 'p1_dog', 'p2','p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog') Columns names are not informative. Names should be changed to be more informative.\n",
    "- Erroneous datatypes:\n",
    "    - p1, p2 and p3 should be categorical\n",
    "    - tweet_id should be string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions_df.tweet_id.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions_df.jpg_url.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1297    752309394570878976\n",
       "1315    754874841593970688\n",
       "1333    757729163776290825\n",
       "1345    759159934323924993\n",
       "1349    759566828574212096\n",
       "               ...        \n",
       "1903    851953902622658560\n",
       "1944    861769973181624320\n",
       "1992    873697596434513921\n",
       "2041    885311592912609280\n",
       "2055    888202515573088257\n",
       "Name: tweet_id, Length: 66, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions_df[image_predictions_df.jpg_url.duplicated()]['tweet_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check tweets_id existance in both archive_df & image_predictions_df\n",
    "image_predictions_df.tweet_id.isin(archive_df.tweet_id).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check tweets_id existance in both api_df & image_predictions_df\n",
    "image_predictions_df.tweet_id.isin(api_df.id).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- missing tweets data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2040    4\n",
       "1713    4\n",
       "1768    4\n",
       "1496    4\n",
       "1978    4\n",
       "       ..\n",
       "1292    1\n",
       "1293    1\n",
       "1294    1\n",
       "1295    1\n",
       "0       1\n",
       "Name: img_num, Length: 2075, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions_df.img_num.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.a.iii api_df table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id  retweet_count  favorite_count\n",
       "0  892420643555336193           8853           39467\n",
       "1  892177421306343426           6514           33819\n",
       "2  891815181378084864           4328           25461"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   id              2354 non-null   int64\n",
      " 1   retweet_count   2354 non-null   int64\n",
      " 2   favorite_count  2354 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 55.3 KB\n"
     ]
    }
   ],
   "source": [
    "api_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Erroneous datatype\n",
    "    - id should be string\n",
    "- id should be tweet_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api_df.id.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "archive_df's shape: \n",
      "(2356, 17)\n",
      "\n",
      "image_predictions_df's shape: \n",
      "(2075, 12)\n",
      "\n",
      "api_df's shape: \n",
      "(2354, 3)\n"
     ]
    }
   ],
   "source": [
    "# Comparing dataframes shapes\n",
    "print(f\"archive_df's shape: \\n{archive_df.shape}\\n\")\n",
    "print(f\"image_predictions_df's shape: \\n{image_predictions_df.shape}\\n\")\n",
    "print(f\"api_df's shape: \\n{api_df.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- dataframes do not have the same number of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='assess_tidiness'></a>\n",
    "### 2.b Tidiness issues\n",
    "\n",
    "- **Tidiness issues** pertain to the structure of data. These structural problems generally prevent easy analysis. Untidy data is also known as messy data. The requirements for tidy data are:<br><br>\n",
    "    - Each variable forms a column.\n",
    "    - Each observation forms a row.\n",
    "    - Each type of observational unit forms a table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.b.i archive_df table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</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\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</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\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421306343426/photo/1</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\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181378084864/photo/1</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\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557279858688/photo/1</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\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1</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\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                                                                 text  \\\n",
       "0                                                       This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "2                   This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB   \n",
       "3                                                             This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ   \n",
       "4  This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f   \n",
       "\n",
       "   retweeted_status_id  retweeted_status_user_id retweeted_status_timestamp  \\\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",
       "                                                                                                                     expanded_urls  \\\n",
       "0                                                                  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "1                                                                  https://twitter.com/dog_rates/status/892177421306343426/photo/1   \n",
       "2                                                                  https://twitter.com/dog_rates/status/891815181378084864/photo/1   \n",
       "3                                                                  https://twitter.com/dog_rates/status/891689557279858688/photo/1   \n",
       "4  https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator      name doggo floofer pupper puppo  \n",
       "0                13                  10   Phineas  None    None   None  None  \n",
       "1                13                  10     Tilly  None    None   None  None  \n",
       "2                12                  10    Archie  None    None   None  None  \n",
       "3                13                  10     Darla  None    None   None  None  \n",
       "4                12                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- (doggo, floofer, pupper, puppo) should be categories of a single variable named \"dog_stage\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.b.ii image_predictions_df table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.b.iii api_df table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   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"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api_df.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- archive_df and api_df should be merged into one table on `tweet_id`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='assess_summary'></a>\n",
    "### 2.c Assessment summary<br>\n",
    "\n",
    "- **2.c.i Quality**<br><br>\n",
    "    - **archive_df table:**\n",
    "        - Erroneous datatype:\n",
    "            * timestamp and retweeted_status_timestamp should be datetime.\n",
    "            * tweet_id should be string.\n",
    "            * (doggo, floofer, pupper, and puppo) are categories.\n",
    "        - Inconsistent representation of `null` values as \"None\" strings in the (`name`, `doggo`, `floofer`, `pupper`, `puppo`) columns.\n",
    "        - Invalid records: tweets in (`in_reply_to_status_id`, `in_reply_to_user_id`, `retweeted_status_id`, `retweeted_status_user_id`, `retweeted_status_timestamp`) as they mean that they are not  original tweets and should not be included in analysis.\n",
    "        - Incorrect names in the `name` column, names weren't successfully extracted from the text. i.e. (a, an, the, very).\n",
    "        - Missing records in (`in_reply_to_status_id`, `in_reply_to_user_id`, `retweeted_status_id`, `retweeted_status_user_id`, `retweeted_status_timestamp`,`expanded_url`, `doggo`, `floofer`, `pupper`, `puppo`).\n",
    "        - Some tweets may not have any images and should be dropped (number of tweets between the archive_df dataset and the image_prediction_df).\n",
    "        - Some tweets are not original tweets, they are retweets or replies and should be dropped.\n",
    "        - Invalid or inconsistent rating data is both `rating_numerator` and `rating_denominator`<br><br>\n",
    "\n",
    "    - **image_predictions_df table**\n",
    "        - Erroneous datatypes:\n",
    "            - tweet_id should be string.\n",
    "        - Columns names are not informative. Names should be changed to be more informative.\n",
    "        - Duplicated `jpg_url` records.\n",
    "        - Missing records; indicates that there are tweets without images should be dropped.<br><br>\n",
    "\n",
    "    - **api_df table**\n",
    "        - Erroneous datatype\n",
    "            - id should be string\n",
    "        - dataframes do not have the same number of data.<br>\n",
    "____\n",
    "- **2.c.ii Tidiness**<br><br>\n",
    "    - **archive_df table:**\n",
    "        - (`doggo`, `floofer`, `pupper`, `puppo`) should be categories of a one variable named \"`dog_stage`\".\n",
    "    - **General:**\n",
    "        - `archive_df` and api_df should be merged into one table on tweet_id\n",
    "        - `image_predictions_df` can be left as-is, because the observational units are images.\n",
    "        \n",
    " ____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean'></a>\n",
    "## 3. Cleaning data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before cleaning steps, a good practice is working on a copy of each data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make copies of the dataframes\n",
    "archive_clean = archive_df.copy() \n",
    "image_predictions_clean = image_predictions_df.copy()\n",
    "api_clean = api_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean-achive'></a>\n",
    "### 3.a `archive_clean` dataset\n",
    "<a id='clean-achive-quality'></a>\n",
    "###  3.a.i Quality issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.a.i.1. archive_clean: `name` column:**\n",
    "\n",
    "- Incorrect/invalid names. (a, an, the, very)\n",
    "- Some names weren't successfully extracted trom the text. (index 391)\n",
    "- Inconsistent representation of null values as \"None\" string."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define**\n",
    "- Fix missing name in index 391. (manually)\n",
    "- Fix invalid names (a, an, the, very). Extract correct names (if availavle) from `text`. \n",
    "- Replace \"None\" string with null value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default\n",
    "# pd.set_option('display.max_colwidth', 50)\n",
    "# max\n",
    "# pd.set_option('display.max_colwidth', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>Here's a little more info on Dew, your favorite roaming doggo that went h*ckin viral. 13/10 \\nhttps://t.co/1httNYrCeW https://t.co/KvaM8j3jhX</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                              text  \\\n",
       "391  Here's a little more info on Dew, your favorite roaming doggo that went h*ckin viral. 13/10 \\nhttps://t.co/1httNYrCeW https://t.co/KvaM8j3jhX   \n",
       "\n",
       "     name  \n",
       "391  None  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking index 391\n",
    "archive_clean.loc[[391],['text', 'name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean.loc[391,'name'] = 'Dew'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2311</th>\n",
       "      <td>This is a purebred Bacardi named Octaviath. Can shoot spaghetti out of mouth. 10/10 https://t.co/uEvsGLOFHa</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>This is a Sagitariot Baklava mix. Loves her new hat. 11/10 radiant pup https://t.co/Bko5kFJYUU</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>Never forget this vine. You will not stop watching for at least 15 minutes. This is the second coveted.. 13/10 https://t.co/roqIxCvEB3</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>C'mon guys. We've been over this. We only rate dogs. This is a cow. Please only submit dogs. Thank you...... 9/10 https://t.co/WjcELNEqN2</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>This is a Slovakian Helter Skelter Feta named Leroi. Likes to skip on roofs. Good traction. Much balance. 10/10 wow! https://t.co/Dmy2mY2Qj5</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1785</th>\n",
       "      <td>This is a dog swinging. I really enjoyed it so I hope you all do as well. 11/10 https://t.co/Ozo9KHTRND</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>This is a Shotokon Macadamia mix named Cheryl. Sophisticated af. Looks like a disappointed librarian. Shh (lol) 9/10 https://t.co/J4GnJ5Swba</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2146</th>\n",
       "      <td>This is a spotted Lipitor Rumpelstiltskin named Alphred. He can't wait for the Turkey. 10/10 would pet really well https://t.co/6GUGO7azNX</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>This is a Butternut Cumberfloof. It's not windy they just look like that. 11/10 back at it again with the red socks https://t.co/hMjzhdUHaW</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097</th>\n",
       "      <td>We only rate dogs. Please stop sending in non-canines like this Alaskan Flop Turtle. This is very frustrating. 10/10 https://t.co/qXteK6Atxc</td>\n",
       "      <td>very</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>People please. This is a Deadly Mediterranean Plop T-Rex. We only rate dogs. Only send in dogs. Thanks you... 11/10 https://t.co/2ATDsgHD4n</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>This is a southwest Coriander named Klint. Hat looks expensive. Still on house arrest :(\\n9/10 https://t.co/IQTOMqDUIe</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>RT @dog_rates: We only rate dogs. Pls stop sending in non-canines like this Mongolian grass snake. This is very frustrating. 11/10 https://…</td>\n",
       "      <td>very</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>This is a Speckled Cauliflower Yosemite named Hemry. He's terrified of intruder dog. Not one bit comfortable. 9/10 https://t.co/yV3Qgjh8iN</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>This is a rare Hungarian Pinot named Jessiga. She is either mid-stroke or got stuck in the washing machine. 8/10 https://t.co/ZU0i0KJyqD</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>This is a Deciduous Trimester mix named Spork. Only 1 ear works. No seat belt. Incredibly reckless. 9/10 still cute https://t.co/CtuJoLHiDo</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>This is a Wild Tuscan Poofwiggle. Careful not to startle. Rare tongue slip. One eye magical. 12/10 would def pet https://t.co/4EnShAQjv6</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                              text  \\\n",
       "2311                                   This is a purebred Bacardi named Octaviath. Can shoot spaghetti out of mouth. 10/10 https://t.co/uEvsGLOFHa   \n",
       "1923                                                This is a Sagitariot Baklava mix. Loves her new hat. 11/10 radiant pup https://t.co/Bko5kFJYUU   \n",
       "2212        Never forget this vine. You will not stop watching for at least 15 minutes. This is the second coveted.. 13/10 https://t.co/roqIxCvEB3   \n",
       "1351                                                            Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa   \n",
       "2350  This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe   \n",
       "1877     C'mon guys. We've been over this. We only rate dogs. This is a cow. Please only submit dogs. Thank you...... 9/10 https://t.co/WjcELNEqN2   \n",
       "2191  This is a Slovakian Helter Skelter Feta named Leroi. Likes to skip on roofs. Good traction. Much balance. 10/10 wow! https://t.co/Dmy2mY2Qj5   \n",
       "1785                                       This is a dog swinging. I really enjoyed it so I hope you all do as well. 11/10 https://t.co/Ozo9KHTRND   \n",
       "2249  This is a Shotokon Macadamia mix named Cheryl. Sophisticated af. Looks like a disappointed librarian. Shh (lol) 9/10 https://t.co/J4GnJ5Swba   \n",
       "2347                  My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O   \n",
       "2333   This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL   \n",
       "2146    This is a spotted Lipitor Rumpelstiltskin named Alphred. He can't wait for the Turkey. 10/10 would pet really well https://t.co/6GUGO7azNX   \n",
       "2204   This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg   \n",
       "1361   This is a Butternut Cumberfloof. It's not windy they just look like that. 11/10 back at it again with the red socks https://t.co/hMjzhdUHaW   \n",
       "1097  We only rate dogs. Please stop sending in non-canines like this Alaskan Flop Turtle. This is very frustrating. 10/10 https://t.co/qXteK6Atxc   \n",
       "1193   People please. This is a Deadly Mediterranean Plop T-Rex. We only rate dogs. Only send in dogs. Thanks you... 11/10 https://t.co/2ATDsgHD4n   \n",
       "1004    Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R   \n",
       "2335      This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv   \n",
       "2264                        This is a southwest Coriander named Klint. Hat looks expensive. Still on house arrest :(\\n9/10 https://t.co/IQTOMqDUIe   \n",
       "773   RT @dog_rates: We only rate dogs. Pls stop sending in non-canines like this Mongolian grass snake. This is very frustrating. 11/10 https://…   \n",
       "2222       Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m   \n",
       "2128    This is a Speckled Cauliflower Yosemite named Hemry. He's terrified of intruder dog. Not one bit comfortable. 9/10 https://t.co/yV3Qgjh8iN   \n",
       "2255      This is a rare Hungarian Pinot named Jessiga. She is either mid-stroke or got stuck in the washing machine. 8/10 https://t.co/ZU0i0KJyqD   \n",
       "2116   This is a Deciduous Trimester mix named Spork. Only 1 ear works. No seat belt. Incredibly reckless. 9/10 still cute https://t.co/CtuJoLHiDo   \n",
       "1368      This is a Wild Tuscan Poofwiggle. Careful not to startle. Rare tongue slip. One eye magical. 12/10 would def pet https://t.co/4EnShAQjv6   \n",
       "\n",
       "      name  \n",
       "2311     a  \n",
       "1923     a  \n",
       "2212   the  \n",
       "1351     a  \n",
       "2350     a  \n",
       "1877     a  \n",
       "2191     a  \n",
       "1785     a  \n",
       "2249     a  \n",
       "2347     a  \n",
       "2333    an  \n",
       "2146     a  \n",
       "2204    an  \n",
       "1361     a  \n",
       "1097  very  \n",
       "1193     a  \n",
       "1004     a  \n",
       "2335    an  \n",
       "2264     a  \n",
       "773   very  \n",
       "2222     a  \n",
       "2128     a  \n",
       "2255     a  \n",
       "2116     a  \n",
       "1368     a  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Samples of names that are \"a, an, the, very\"\n",
    "archive_clean.query('(name == \"a\") | (name == \"an\") | (name == \"the\") | (name == \"very\")')[['text', 'name']].sample(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name    a\n",
       "Name: 1193, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.loc[1193,['name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the `text`; I've noticed that the correct name is usally after 'named' or 'name is', others do not have names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hemry\n"
     ]
    }
   ],
   "source": [
    "# Using regualar exepressions to extract the correct name\n",
    "# Test pattern\n",
    "test_text_01 = \"This is a Speckled Cauliflower Yosemite named Hemry. He's terrified of intruder dog. Not one bit comfortable. 9/10 https://t.co/yV3Qgjh8iN\"\n",
    "test_pattern = re.compile(r'(?:name(?:d)?\\s)(?:is\\s)?(\\w+)')\n",
    "\n",
    "try:\n",
    "    print(re.findall(test_pattern, test_text_01)[0])\n",
    "except Exception as e:\n",
    "    print(\"Name not found\")\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name not found\n",
      "list index out of range\n"
     ]
    }
   ],
   "source": [
    "test_text_02 = \"Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR\"\n",
    "try:\n",
    "    print(re.findall(test_pattern, test_text_02)[0])\n",
    "except Exception as e:\n",
    "    print(\"Name not found\")\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Works well!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function to correct names -if available-\n",
    "\n",
    "def correct_names(row):\n",
    "    \"\"\"Replace incorrect names with: \n",
    "        - Correct name extracted from text column using regex pattern.\n",
    "        - 'None' if name is not availabe in text column\"\"\"\n",
    "    \n",
    "    if row['name'] == \"a\" or row['name'] == \"an\" or row['name'] == \"the\" or row['name'] == \"very\":\n",
    "        pattern = re.compile(r'(?:name(?:d)?\\s)(?:is\\s)?(\\w+)')\n",
    "\n",
    "        try:\n",
    "            correct_name = re.findall(pattern, row['text'])[0]\n",
    "            \n",
    "            row['name'] = row['name'].replace('an', correct_name)\n",
    "            row['name'] = row['name'].replace('a', correct_name)\n",
    "            row['name'] = row['name'].replace('the', correct_name)\n",
    "            row['name'] = row['name'].replace('very', correct_name)\n",
    "\n",
    "        except Exception as e:\n",
    "            correct_name = \"None\"\n",
    "\n",
    "            row['name'] = row['name'].replace('an', correct_name)\n",
    "            row['name'] = row['name'].replace('a', correct_name)\n",
    "            row['name'] = row['name'].replace('the', correct_name)\n",
    "            row['name'] = row['name'].replace('very', correct_name)\n",
    "        \n",
    "        return row\n",
    "    \n",
    "    else:\n",
    "        return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying the function\n",
    "archive_clean = archive_clean.apply(correct_names, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace \"None\" Sting with `None`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean['name'] = archive_clean.name.replace({'None': None})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>Here's a little more info on Dew, your favorite roaming doggo that went h*ckin viral. 13/10 \\nhttps://t.co/1httNYrCeW https://t.co/KvaM8j3jhX</td>\n",
       "      <td>Dew</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                              text  \\\n",
       "391  Here's a little more info on Dew, your favorite roaming doggo that went h*ckin viral. 13/10 \\nhttps://t.co/1httNYrCeW https://t.co/KvaM8j3jhX   \n",
       "\n",
       "    name  \n",
       "391  Dew  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test manually fixed names\n",
    "archive_clean.loc[[391],['text', 'name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2346</th>\n",
       "      <td>Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>This is a Tuscaloosa Alcatraz named Jacob (Yacōb). Loves to sit in swing. Stellar tongue. 11/10 look at his feet https://t.co/2IslQ8ZSc7</td>\n",
       "      <td>Jacob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>This is a Coriander Baton Rouge named Alfredo. Loves to cuddle with smaller well-dressed dog. 10/10 would hug lots https://t.co/eCRdwouKCl</td>\n",
       "      <td>Alfredo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>This is a Rich Mahogany Seltzer named Cherokee. Just got destroyed by a snowball. Isn't very happy about it. 9/10 https://t.co/98ZBi6o4dj</td>\n",
       "      <td>Cherokee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg</td>\n",
       "      <td>BertBerta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>This is a northern Wahoo named Kohl. He runs this town. Chases tumbleweeds. Draws gun wicked fast. 11/10 legendary https://t.co/J4vn2rOYFk</td>\n",
       "      <td>Kohl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2198</th>\n",
       "      <td>This is a wild Toblerone from Papua New Guinea. Mouth always open. Addicted to hay. Acts blind. 7/10 handsome dog https://t.co/IGmVbz07tZ</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                             text  \\\n",
       "2346      Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p   \n",
       "2034     This is a Tuscaloosa Alcatraz named Jacob (Yacōb). Loves to sit in swing. Stellar tongue. 11/10 look at his feet https://t.co/2IslQ8ZSc7   \n",
       "2161   This is a Coriander Baton Rouge named Alfredo. Loves to cuddle with smaller well-dressed dog. 10/10 would hug lots https://t.co/eCRdwouKCl   \n",
       "2125    This is a Rich Mahogany Seltzer named Cherokee. Just got destroyed by a snowball. Isn't very happy about it. 9/10 https://t.co/98ZBi6o4dj   \n",
       "2204  This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg   \n",
       "2273   This is a northern Wahoo named Kohl. He runs this town. Chases tumbleweeds. Draws gun wicked fast. 11/10 legendary https://t.co/J4vn2rOYFk   \n",
       "2198    This is a wild Toblerone from Papua New Guinea. Mouth always open. Addicted to hay. Acts blind. 7/10 handsome dog https://t.co/IGmVbz07tZ   \n",
       "\n",
       "           name  \n",
       "2346       None  \n",
       "2034      Jacob  \n",
       "2161    Alfredo  \n",
       "2125   Cherokee  \n",
       "2204  BertBerta  \n",
       "2273       Kohl  \n",
       "2198       None  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test (a, an, the, very) names\n",
    "archive_clean.loc[[2346, 2034, 2161, 2125, 2204, 2273, 2198], ['text', 'name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [text, name]\n",
       "Index: []"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.query('(name == \"a\") | (name == \"an\") | (name == \"the\") | (name == \"very\")')[['text', 'name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [text, name]\n",
       "Index: []"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check \"None\"\n",
    "archive_clean.query('(name == \"None\")')[['text', 'name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "798"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.name.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.a.i.2. archive_clean: Erroneous datatypes:** \n",
    "\n",
    "- `tweet_id` should be a string.\n",
    "- timestamp and retweeted_status_timestamp should be `datetime`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define**\n",
    "- Convert `tweet_id` dtype to `object` using `astype()`.\n",
    "- Convert `timestamp` and `retweeted_status_timestamp` using `to_datetime()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tweet_id\n",
    "archive_clean.tweet_id = archive_clean.tweet_id.astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# timestamp and retweeted_status_timestamp\n",
    "archive_clean.timestamp = pd.to_datetime(archive_clean.timestamp)\n",
    "archive_clean.retweeted_status_timestamp = pd.to_datetime(archive_clean.retweeted_status_timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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   object             \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   datetime64[ns, UTC]\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    datetime64[ns, UTC]\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                        1558 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: datetime64[ns, UTC](2), float64(4), int64(2), object(9)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.a.i.3. archive_clean: `expanded_url` Missing values :** \n",
    "- Missing values. (2297 insted of 2356)\n",
    "- After inspecting; It turns out that these tweets have no images.\n",
    "- Since we need only tweets with images in our analysis, these rows should be dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define**\n",
    "\n",
    "- Drop `NaN` values using `dropna`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use subset\n",
    "archive_clean.dropna(subset=['expanded_urls'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.expanded_urls.isnull().all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.a.i.4. archive_clean: `doggo, floofer,...` Missing values :** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define**\n",
    "- Fix \"None\" representation for later tidiness issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2297</td>\n",
       "      <td>2297</td>\n",
       "      <td>2297</td>\n",
       "      <td>2297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2203</td>\n",
       "      <td>2287</td>\n",
       "      <td>2042</td>\n",
       "      <td>2268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       doggo floofer pupper puppo\n",
       "count   2297    2297   2297  2297\n",
       "unique     2       2      2     2\n",
       "top     None    None   None  None\n",
       "freq    2203    2287   2042  2268"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.iloc[:, -4:].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean.iloc[:, -4: ] = archive_clean.iloc[:, -4: ].replace(\"None\", '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2297</td>\n",
       "      <td>2297</td>\n",
       "      <td>2297</td>\n",
       "      <td>2297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2203</td>\n",
       "      <td>2287</td>\n",
       "      <td>2042</td>\n",
       "      <td>2268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       doggo floofer pupper puppo\n",
       "count   2297    2297   2297  2297\n",
       "unique     2       2      2     2\n",
       "top                              \n",
       "freq    2203    2287   2042  2268"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.iloc[:, -4: ].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         2203\n",
       "doggo      94\n",
       "Name: doggo, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.doggo.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean-achive-tidiness'></a>\n",
    "###  3.a.ii Tidiness issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.a.ii.1. archive_clean: Values represented as variables:** \n",
    "\n",
    "- (doggo, floofer, pupper and puppo) columns should be one category column. (dog_stage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define**\n",
    "- Create `dog_stage` column.\n",
    "- Fix double values.\n",
    "- Convert `dog_stage` dtype to `category`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean['dog_stage'] = archive_clean.doggo + archive_clean.floofer + archive_clean.pupper + archive_clean.puppo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping old columns\n",
    "archive_clean.drop(columns=['doggo', 'floofer', 'pupper', 'puppo'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2297 entries, 0 to 2355\n",
      "Data columns (total 14 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2297 non-null   object             \n",
      " 1   in_reply_to_status_id       23 non-null     float64            \n",
      " 2   in_reply_to_user_id         23 non-null     float64            \n",
      " 3   timestamp                   2297 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2297 non-null   object             \n",
      " 5   text                        2297 non-null   object             \n",
      " 6   retweeted_status_id         180 non-null    float64            \n",
      " 7   retweeted_status_user_id    180 non-null    float64            \n",
      " 8   retweeted_status_timestamp  180 non-null    datetime64[ns, UTC]\n",
      " 9   expanded_urls               2297 non-null   object             \n",
      " 10  rating_numerator            2297 non-null   int64              \n",
      " 11  rating_denominator          2297 non-null   int64              \n",
      " 12  name                        1558 non-null   object             \n",
      " 13  dog_stage                   2297 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](2), float64(4), int64(2), object(6)\n",
      "memory usage: 269.2+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                1923\n",
       "pupper           243\n",
       "doggo             80\n",
       "puppo             28\n",
       "doggopupper       12\n",
       "floofer            9\n",
       "doggopuppo         1\n",
       "doggofloofer       1\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixing double stages\n",
    "archive_clean.loc[archive_clean.dog_stage == 'doggopupper', 'dog_stage'] = 'doggo-pupper'\n",
    "archive_clean.loc[archive_clean.dog_stage == 'doggopuppo', 'dog_stage'] = 'doggo-puppo'\n",
    "archive_clean.loc[archive_clean.dog_stage == 'doggofloofer', 'dog_stage'] = 'doggo-floofer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixing empty stings values with NaN\n",
    "archive_clean.loc[archive_clean.dog_stage == \"\", 'dog_stage'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert dog_stage to category\n",
    "archive_clean.dog_stage = archive_clean.dog_stage.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN              1923\n",
       "pupper            243\n",
       "doggo              80\n",
       "puppo              28\n",
       "doggo-pupper       12\n",
       "floofer             9\n",
       "doggo-puppo         1\n",
       "doggo-floofer       1\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.dog_stage.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2297 entries, 0 to 2355\n",
      "Data columns (total 14 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2297 non-null   object             \n",
      " 1   in_reply_to_status_id       23 non-null     float64            \n",
      " 2   in_reply_to_user_id         23 non-null     float64            \n",
      " 3   timestamp                   2297 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2297 non-null   object             \n",
      " 5   text                        2297 non-null   object             \n",
      " 6   retweeted_status_id         180 non-null    float64            \n",
      " 7   retweeted_status_user_id    180 non-null    float64            \n",
      " 8   retweeted_status_timestamp  180 non-null    datetime64[ns, UTC]\n",
      " 9   expanded_urls               2297 non-null   object             \n",
      " 10  rating_numerator            2297 non-null   int64              \n",
      " 11  rating_denominator          2297 non-null   int64              \n",
      " 12  name                        1558 non-null   object             \n",
      " 13  dog_stage                   374 non-null    category           \n",
      "dtypes: category(1), datetime64[ns, UTC](2), float64(4), int64(2), object(5)\n",
      "memory usage: 253.8+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean-image'></a>\n",
    "### 3.b `image_predictions_clean` dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean-image-quality'></a>\n",
    "###  3.b.i Quality issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.b.i.1. image_predictions_clean: columns' names:** \n",
    "- Columns names are not informative. Names should be changed to be more informative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define**\n",
    "- Renaming the columns to be more descriptive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tweet_id',\n",
       " 'jpg_url',\n",
       " 'img_num',\n",
       " 'p1',\n",
       " 'p1_conf',\n",
       " 'p1_dog',\n",
       " 'p2',\n",
       " 'p2_conf',\n",
       " 'p2_dog',\n",
       " 'p3',\n",
       " 'p3_conf',\n",
       " 'p3_dog']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Old columns' names\n",
    "list(image_predictions_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# New descriptive columns' names\n",
    "columns = ['tweet_id', 'jpg_url', 'img_num',\n",
    "           'prediction_1', 'confidence_1', 'is_dog_1', \n",
    "           'prediction_2', 'confidence_2', 'is_dog_2',\n",
    "           'prediction_3', 'confidence_3', 'is_dog_3',]\n",
    "\n",
    "image_predictions_clean.columns = columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tweet_id',\n",
       " 'jpg_url',\n",
       " 'img_num',\n",
       " 'prediction_1',\n",
       " 'confidence_1',\n",
       " 'is_dog_1',\n",
       " 'prediction_2',\n",
       " 'confidence_2',\n",
       " 'is_dog_2',\n",
       " 'prediction_3',\n",
       " 'confidence_3',\n",
       " 'is_dog_3']"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(image_predictions_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.b.i.2. image_predictions_clean: Erroneous datatypes:** \n",
    "\n",
    "- tweet_id should be a string."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define**\n",
    "- Convert `tweet_id` dtype to `object` using `astype()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_predictions_clean.tweet_id = image_predictions_clean.tweet_id.astype(object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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   object \n",
      " 1   jpg_url       2075 non-null   object \n",
      " 2   img_num       2075 non-null   int64  \n",
      " 3   prediction_1  2075 non-null   object \n",
      " 4   confidence_1  2075 non-null   float64\n",
      " 5   is_dog_1      2075 non-null   bool   \n",
      " 6   prediction_2  2075 non-null   object \n",
      " 7   confidence_2  2075 non-null   float64\n",
      " 8   is_dog_2      2075 non-null   bool   \n",
      " 9   prediction_3  2075 non-null   object \n",
      " 10  confidence_3  2075 non-null   float64\n",
      " 11  is_dog_3      2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(1), object(5)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "image_predictions_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean-api'></a>\n",
    "### 3.c `api_clean` dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean-api-quality'></a>\n",
    "###  3.c.i Quality issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.c.i.1. api_clean: `id` datatype**\n",
    "\n",
    "- Erroneous datatype: id should be string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define**\n",
    "- Convert `tweet_id` datatype to `object`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_clean.id = api_clean.id.astype(object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   id              2354 non-null   object\n",
      " 1   retweet_count   2354 non-null   int64 \n",
      " 2   favorite_count  2354 non-null   int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 55.3+ KB\n"
     ]
    }
   ],
   "source": [
    "api_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean-api-tidiness'></a>\n",
    "###  3.c.ii Tidiness issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  3.c.ii.1 api_clean: Should be merged with archive_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define**\n",
    "- Merge `api_clean` with `archive_clean`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean = pd.merge(left=archive_clean, right=api_clean, how='left', left_on='tweet_id', right_on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop 'id' column\n",
    "archive_clean.drop(columns=['id'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2297 entries, 0 to 2296\n",
      "Data columns (total 16 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2297 non-null   object             \n",
      " 1   in_reply_to_status_id       23 non-null     float64            \n",
      " 2   in_reply_to_user_id         23 non-null     float64            \n",
      " 3   timestamp                   2297 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2297 non-null   object             \n",
      " 5   text                        2297 non-null   object             \n",
      " 6   retweeted_status_id         180 non-null    float64            \n",
      " 7   retweeted_status_user_id    180 non-null    float64            \n",
      " 8   retweeted_status_timestamp  180 non-null    datetime64[ns, UTC]\n",
      " 9   expanded_urls               2297 non-null   object             \n",
      " 10  rating_numerator            2297 non-null   int64              \n",
      " 11  rating_denominator          2297 non-null   int64              \n",
      " 12  name                        1558 non-null   object             \n",
      " 13  dog_stage                   374 non-null    category           \n",
      " 14  retweet_count               2295 non-null   float64            \n",
      " 15  favorite_count              2295 non-null   float64            \n",
      "dtypes: category(1), datetime64[ns, UTC](2), float64(6), int64(2), object(5)\n",
      "memory usage: 289.7+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean-general'></a>\n",
    "## 3.d General cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.d.i. Tweets without images should be removed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define** \n",
    "- Use  `iamge_prediction_clean` table to idintify the tweets without images in the archive table and remove them. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2075"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of tweets with images\n",
    "tweets_with_images = list(image_predictions_clean.tweet_id.unique())\n",
    "len(tweets_with_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2075"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checkin that all tweets are in archive_clean data set\n",
    "archive_clean.tweet_id.isin(tweets_with_images).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the other tweets\n",
    "archive_clean = archive_clean[archive_clean.tweet_id.isin(tweets_with_images)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2075 entries, 0 to 2296\n",
      "Data columns (total 16 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2075 non-null   object             \n",
      " 1   in_reply_to_status_id       23 non-null     float64            \n",
      " 2   in_reply_to_user_id         23 non-null     float64            \n",
      " 3   timestamp                   2075 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2075 non-null   object             \n",
      " 5   text                        2075 non-null   object             \n",
      " 6   retweeted_status_id         81 non-null     float64            \n",
      " 7   retweeted_status_user_id    81 non-null     float64            \n",
      " 8   retweeted_status_timestamp  81 non-null     datetime64[ns, UTC]\n",
      " 9   expanded_urls               2075 non-null   object             \n",
      " 10  rating_numerator            2075 non-null   int64              \n",
      " 11  rating_denominator          2075 non-null   int64              \n",
      " 12  name                        1446 non-null   object             \n",
      " 13  dog_stage                   321 non-null    category           \n",
      " 14  retweet_count               2073 non-null   float64            \n",
      " 15  favorite_count              2073 non-null   float64            \n",
      "dtypes: category(1), datetime64[ns, UTC](2), float64(6), int64(2), object(5)\n",
      "memory usage: 261.8+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.d.ii. Non original tweets removal (retweets and replies):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define**\n",
    "- Drop tweet that are not original tweets using `in_reply_to_status_id`, `in_reply_to_user_id` and `retweeted_status_id`, `retweeted_status_user_id`, `retweeted_status_timestamp`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting retweets\n",
    "retweets = archive_clean.retweeted_status_id.notnull()\n",
    "archive_clean[retweets].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping retweets\n",
    "archive_clean = archive_clean[~retweets]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting replies\n",
    "replies = archive_clean.in_reply_to_status_id.notnull()\n",
    "archive_clean[replies].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping replies\n",
    "archive_clean = archive_clean[~replies]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping retweets and replies columns\n",
    "archive_clean.drop(columns=['in_reply_to_status_id', \n",
    "                    'in_reply_to_user_id', \n",
    "                    'retweeted_status_id', \n",
    "                    'retweeted_status_user_id', \n",
    "                    'retweeted_status_timestamp'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1971 entries, 0 to 2296\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            1971 non-null   object             \n",
      " 1   timestamp           1971 non-null   datetime64[ns, UTC]\n",
      " 2   source              1971 non-null   object             \n",
      " 3   text                1971 non-null   object             \n",
      " 4   expanded_urls       1971 non-null   object             \n",
      " 5   rating_numerator    1971 non-null   int64              \n",
      " 6   rating_denominator  1971 non-null   int64              \n",
      " 7   name                1397 non-null   object             \n",
      " 8   dog_stage           303 non-null    category           \n",
      " 9   retweet_count       1971 non-null   float64            \n",
      " 10  favorite_count      1971 non-null   float64            \n",
      "dtypes: category(1), datetime64[ns, UTC](1), float64(2), int64(2), object(5)\n",
      "memory usage: 171.7+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.d.ii. Rating numerator and denominator inaccurate and invalid values:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Define**\n",
    "1. For denominator: Check values below or above 10 and its text.\n",
    "2. For numerator: Check values below 6 and above 15 and its text.\n",
    "3. Combined manual and programmatic fixation - if possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1971.000000</td>\n",
       "      <td>1971.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.223237</td>\n",
       "      <td>10.477423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>41.634034</td>\n",
       "      <td>6.853275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rating_numerator  rating_denominator\n",
       "count       1971.000000         1971.000000\n",
       "mean          12.223237           10.477423\n",
       "std           41.634034            6.853275\n",
       "min            0.000000            2.000000\n",
       "25%           10.000000           10.000000\n",
       "50%           11.000000           10.000000\n",
       "75%           12.000000           10.000000\n",
       "max         1776.000000          170.000000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe\n",
    "archive_clean.loc[:,['rating_numerator', 'rating_denominator']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  13,   12,   14,    5,   11,    6,   10,    0,   84,   24,   75,\n",
       "         27,    3,    7,    8,    9,    4,  165, 1776,  204,   50,   99,\n",
       "         80,   45,   60,   44,  121,   26,    2,  144,   88,    1,  420],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerator\n",
    "archive_clean.rating_numerator.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      446\n",
       "10      417\n",
       "11      392\n",
       "13      253\n",
       "9       150\n",
       "8        95\n",
       "7        52\n",
       "5        33\n",
       "14       33\n",
       "6        32\n",
       "3        19\n",
       "4        16\n",
       "2         9\n",
       "1         5\n",
       "1776      1\n",
       "24        1\n",
       "26        1\n",
       "420       1\n",
       "27        1\n",
       "44        1\n",
       "45        1\n",
       "50        1\n",
       "60        1\n",
       "75        1\n",
       "80        1\n",
       "84        1\n",
       "88        1\n",
       "99        1\n",
       "121       1\n",
       "144       1\n",
       "165       1\n",
       "204       1\n",
       "0         1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.rating_numerator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10,  70,   7, 150,  11, 170,  20,  50,  90,  80,  40, 110, 120,\n",
       "         2], dtype=int64)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# denominator\n",
    "archive_clean.rating_denominator.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     1954\n",
       "50        3\n",
       "80        2\n",
       "11        2\n",
       "170       1\n",
       "150       1\n",
       "120       1\n",
       "110       1\n",
       "90        1\n",
       "70        1\n",
       "40        1\n",
       "20        1\n",
       "7         1\n",
       "2         1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Capturing outliers\n",
    "odd_numerator = np.logical_or(archive_clean.rating_numerator > 15, archive_clean.rating_numerator <= 5)\n",
    "odd_denominator = np.logical_or(archive_clean.rating_denominator > 10, archive_clean.rating_denominator < 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odd_denominator.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odd_numerator.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. There are 101 odd numerators' values and 17 odd denominators' values need to be checked along with their text.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# boolean masking \n",
    "odd_ratings = archive_clean.loc[np.logical_or(odd_denominator, odd_numerator), ['text', 'rating_numerator', 'rating_denominator']]\n",
    "odd_ratings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "2. Apparently there are odd rating values (103 values) have both odd numerator and odd denominator values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Max display\n",
    "pd.set_option('display.max_rows', None)\n",
    "# Default\n",
    "# pd.set_option('display.max_rows', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS</td>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>Who keeps sending in pictures without dogs in them? This needs to stop. 5/10 for the mediocre road https://t.co/ELqelxWMrC</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>This is Wesley. He's clearly trespassing. Seems rather h*ckin violent too. Weaponized forehead. 3/10 wouldn't let in https://t.co/pL7wbMRW7M</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>This is Fido. He can tell the weather. Not good at fetch tho. Never comes when called. 4/10 would probably still pet https://t.co/4gOv2Q3iKP</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE</td>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>Here's another picture without a dog in it. Idk why you guys keep sending these. 4/10 just because that's a neat rug https://t.co/mOmnL19Wsl</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh</td>\n",
       "      <td>1776</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>This is Alexanderson. He's got a weird ass birth mark. Dreadful at fetch. Won't eat kibble. 3/10 wtf @Target https://t.co/FmxOpf2Sgl</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>This is Benedict. He's a feisty pup. Needs a brushing. Portable af. Looks very angry actually. 4/10 might not pet https://t.co/3oeFfHjv0Z</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1214</th>\n",
       "      <td>What hooligan sent in pictures w/out a dog in them? Churlish af. 3/10 just bc that's a neat fluffy bean bag chair https://t.co/wcwoGOkZvz</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1219</th>\n",
       "      <td>Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp;amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>This is Keurig. He's a rare dog. Laughs like an idiot tho. Head is basically a weapon. Poorly maintained goatee 4/10 https://t.co/xOrUyj7K30</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>This is Elliot. He's blocking the roadway. Downright rude as hell. Doesn't care that you're already late. 3/10 https://t.co/FMUxir5pYu</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>This is Dave. He's a tropical pup. Short lil legs (dachshund mix?) Excels underwater, but refuses to eat kibble 5/10 https://t.co/ZJnCxlIf62</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>This is Charl. He's a bully. Chucks that dumbbell around like its nothing. Sharp neck. Exceptionally unfluffy. 3/10 https://t.co/VfLoDZecJ7</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1420</th>\n",
       "      <td>This may be the greatest video I've ever been sent. 4/10 for Charles the puppy, 13/10 overall. (Vid by @stevenxx_) https://t.co/uaJmNgXR2P</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>Meet Phil. He's big af. Currently destroying this nice family home. Completely uncalled for. 3/10 not a good pupper https://t.co/fShNNhBWYx</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>When bae says they can't go out but you see them with someone else that same night. 5/10 &amp;amp; 10/10 for heartbroken pup https://t.co/aenk0KpoWM</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540</th>\n",
       "      <td>Army of water dogs here. None of them know where they're going. Have no real purpose. Aggressive barks. 5/10 for all https://t.co/A88x73TwMN</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1557</th>\n",
       "      <td>This is Hammond. He's a peculiar pup. Loves long walks. Bark barely audible. Too many legs. 3/10 must be rare https://t.co/NOIiRWr5Jf</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1573</th>\n",
       "      <td>This is Jerry. He's a neat dog. No legs (tragic). Has more horns than a dog usually does. Bark is unique af. 5/10 https://t.co/85q7xlplsJ</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1578</th>\n",
       "      <td>Here we have a basking dino pupper. Looks powerful. Occasionally shits eggs. Doesn't want the holidays to end. 5/10 https://t.co/DnNweb5eTO</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1583</th>\n",
       "      <td>This is Bobby. He doesn't give a damn about personal space. Convinced he called shotgun first. 4/10 not the best dog https://t.co/b8XW69gSaU</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1589</th>\n",
       "      <td>Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55</td>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599</th>\n",
       "      <td>This is Jiminy. He's not the brightest dog. Needs to lay off the kibble. 5/10 still petable https://t.co/omln4LOy1x</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1616</th>\n",
       "      <td>This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1633</th>\n",
       "      <td>Unique dog here. Wrinkly as hell. Weird segmented neck. Finger on fire. Doesn't seem to notice. 5/10 might still pet https://t.co/Hy9La4xNX3</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>This is Chuck. He's a neat dog. Very flexible. Trapped in a glass case of emotion. Devastatingly unfluffy 3/10 https://t.co/YqbU9xHV3p</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>This is Alice. She's an idiot. 4/10 https://t.co/VQXdwJfkyS</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1664</th>\n",
       "      <td>Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD</td>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>Meet Penelope. She's a bacon frise. Total babe (lol get it like the movie). Doesn't bark tho. 5/10 very average dog https://t.co/SDcQYg0HSZ</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>Exotic pup here. Tail long af. Throat looks swollen. Might breathe fire. Exceptionally unfluffy 2/10 would still pet https://t.co/a8SqCaSo2r</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>This is Crystal. She's a shitty fireman. No sense of urgency. People could be dying Crystal. 2/10 just irresponsible https://t.co/rtMtjSl9pz</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq</td>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1747</th>\n",
       "      <td>This is Juckson. He's totally on his way to a nascar race. 5/10 for Juckson https://t.co/IoLRvF0Kak</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>Exotic handheld dog here. Appears unathletic. Feet look deadly. Can be thrown a great distance. 5/10 might pet idk https://t.co/Avq4awulqk</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1770</th>\n",
       "      <td>This is Bubbles. He kinda resembles a fish. Always makes eye contact with u no matter what. Sneaky tongue slip. 5/10 https://t.co/Nrhvc5tLFT</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1786</th>\n",
       "      <td>Extremely rare pup here. Very religious. Always praying. Too many legs. Not overwhelmingly fluffy. Won't bark. 3/10 https://t.co/REyE5YKVBb</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw</td>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1810</th>\n",
       "      <td>Rare shielded battle dog here. Very happy about abundance of lettuce. Painfully slow fetcher. Still petable. 5/10 https://t.co/C3tlKVq7eO</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1818</th>\n",
       "      <td>What kind of person sends in a picture without a dog in it? 1/10 just because that's a nice table https://t.co/RDXCfk8hK0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1823</th>\n",
       "      <td>This is Steven. He got locked outside. Damn it Steven. 5/10 nice grill tho https://t.co/zf7Sxxjfp3</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1846</th>\n",
       "      <td>Meet Patrick. He's an exotic pup. Jumps great distances for a dog. Always gets injured when I toss him a ball. 3/10 https://t.co/Unz1uNrOzo</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>Two gorgeous dogs here. Little waddling dog is a rebel. Refuses to look at camera. Must be a preteen. 5/10 &amp;amp; 8/10 https://t.co/YPfw7oahbD</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>Rare submerged pup here. Holds breath for a long time. Frowning because that spoon ignores him. 5/10 would still pet https://t.co/EJzzNHE8bE</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>This is Henry. He's a shit dog. Short pointy ears. Leaves trail of pee. Not fluffy. Doesn't come when called. 2/10 https://t.co/Pu9RhfHDEQ</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>This is Earl. Earl is lost. Someone help Earl. He has no tags. Just trying to get home. 5/10 hang in there Earl https://t.co/1ZbfqAVDg6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>Herd of wild dogs here. Not sure what they're trying to do. No real goals in life. 3/10 find your purpose puppers https://t.co/t5ih0VrK02</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>Guys I'm getting real tired of this. We only rate dogs. Please don't send in other things like this Bulbasaur. 3/10 https://t.co/t5rQHl6W8M</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>This is a heavily opinionated dog. Loves walls. Nobody knows how the hair works. Always ready for a kiss. 4/10 https://t.co/dFiaKZ9cDl</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>Large blue dog here. Cool shades. Flipping us off w both hands. Obviously a preteen. 3/10 for rude blue preteen pup https://t.co/mcPd5AFfhA</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>Extraordinary dog here. Looks large. Just a head. No body. Rather intrusive. 5/10 would still pet https://t.co/ufHWUFA9Pu</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>Exotic underwater dog here. Very shy. Wont return tennis balls I toss him. Never been petted. 5/10 I bet he's soft https://t.co/WH7Nzc5IBA</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>This is Brad. He's a chubby lil pup. Doesn't really need the food he's trying to reach. 5/10 you've had enough Brad https://t.co/vPXKSaNsbE</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>This is Anthony. He just finished up his masters at Harvard. Unprofessional tattoos. Always looks perturbed. 5/10 https://t.co/iHLo9rGay1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>Two miniature golden retrievers here. Webbed paws. Don't walk very efficiently. Can't catch a tennis ball. 4/10s https://t.co/WzVLdSHJU7</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>After so many requests... here you go.\\n\\nGood dogg. 420/10 https://t.co/yfAAo1gdeY</td>\n",
       "      <td>420</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>Pink dogs here. Unreasonably long necks. Left guy has only 1 leg. Quite nimble. Don't bark tho 4/10s would still pet https://t.co/QY5uvMmmQk</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>Scary dog here. Too many legs. Extra tail. Not soft, let alone fluffy. Won't bark. Moves sideways. Has weapon. 2/10 https://t.co/XOPXCSXiUT</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>Flamboyant pup here. Probably poisonous. Won't eat kibble. Doesn't bark. Slow af. Petting doesn't look fun. 1/10 https://t.co/jxukeh2BeO</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036</th>\n",
       "      <td>This dude slaps your girl's ass what do you do?\\n5/10 https://t.co/6dioUL6gcP</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2053</th>\n",
       "      <td>Vibrant dog here. Fabulous tail. Only 2 legs tho. Has wings but can barely fly (lame). Rather elusive. 5/10 okay pup https://t.co/cixC0M3P1e</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2078</th>\n",
       "      <td>This is Randall. He's from Chernobyl. Built playground himself. Has been stuck up there quite a while. 5/10 good dog https://t.co/pzrvc7wKGd</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2080</th>\n",
       "      <td>This is Tommy. He's a cool dog. Hard not to step on. Won't let go of seashell. Not fast by any means. 3/10 https://t.co/0gY6XTOpn3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2083</th>\n",
       "      <td>Awesome dog here. Not sure where it is tho. Spectacular camouflage. Enjoys leaves. Not very soft. 5/10 still petable https://t.co/rOTOteKx4q</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2096</th>\n",
       "      <td>This is a brave dog. Excellent free climber. Trying to get closer to God. Not very loyal though. Doesn't bark. 5/10 https://t.co/ODnILTr4QM</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2124</th>\n",
       "      <td>Two gorgeous pups here. Both have cute fake horns(adorable). Barn in the back looks on fire. 5/10 would pet rly well https://t.co/w5oYFXi0uh</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2126</th>\n",
       "      <td>This is Bernie. He's taking his Halloween costume very seriously. Wants to be baked. 3/10 not a good idea Bernie smh https://t.co/1zBp1moFlX</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2129</th>\n",
       "      <td>Unique dog here. Oddly shaped tail. Long pink front legs. I don't think dogs breath underwater sos. 4/10 bad owner https://t.co/0EJXxE9UxW</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2144</th>\n",
       "      <td>Fascinating dog here. Loves beach. Oddly long nose for dog. Massive ass paws. Hard to cuddle w. 3/10 would still pet https://t.co/IiSdmhkC5N</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2148</th>\n",
       "      <td>Meet Zeek. He is a grey Cumulonimbus. Zeek is hungry. Someone should feed Zeek asap. 5/10 absolutely terrifying https://t.co/fvVNScw8VH</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2164</th>\n",
       "      <td>Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2179</th>\n",
       "      <td>This lil pup is Oliver. Hops around. Has wings but doesn't fly (lame). Annoying chirp. Won't catch tennis balls 2/10 https://t.co/DnhUw0aBM2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>This dog resembles a baked potato. Bed looks uncomfortable. No tail. Comes with butter tho. 3/10 petting still fun https://t.co/x89NSCEZCq</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2184</th>\n",
       "      <td>Wow. Armored dog here. Ready for battle. Face looks dangerous. Not very loyal. Lil dog on back havin a blast. 5/10 https://t.co/SyMoWrp368</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2188</th>\n",
       "      <td>This is Tedrick. He lives on the edge. Needs someone to hit the gas tho. Other than that he's a baller. 10&amp;amp;2/10 https://t.co/LvP1TTYSCN</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2203</th>\n",
       "      <td>Never seen dog like this. Breathes heavy. Tilts head in a pattern. No bark. Shitty at fetch. Not even cordless. 1/10 https://t.co/i9iSGNn3fx</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2230</th>\n",
       "      <td>These are strange dogs. All have toupees. Long neck for dogs. In a shed of sorts? Work in groups? 4/10 still petable https://t.co/PZxSarAfSN</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>My goodness. Very rare dog here. Large. Tail dangerous. Kinda fat. Only eats leaves. Doesn't come when called 3/10 https://t.co/xYGdBrMS9h</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2251</th>\n",
       "      <td>Unfamiliar with this breed. Ears pointy af. Won't let go of seashell. Won't eat kibble. Not very fast. Bad dog 2/10 https://t.co/EIn5kElY1S</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2253</th>\n",
       "      <td>This is Josep. He is a Rye Manganese mix. Can drive w eyes closed. Very irresponsible. Menace on the roadways. 5/10 https://t.co/XNGeDwrtYH</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2257</th>\n",
       "      <td>Cool dog. Enjoys couch. Low monotone bark. Very nice kicks. Pisses milk (must be rare). Can't go down stairs. 4/10 https://t.co/vXMKrJC81s</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td>This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2276</th>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2279</th>\n",
       "      <td>Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2290</th>\n",
       "      <td>This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292</th>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                           text  \\\n",
       "44                                     This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948   \n",
       "298                                                           When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag   \n",
       "410                                                                         The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd   \n",
       "491   Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx   \n",
       "667                                            This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS   \n",
       "700                                                  Who keeps sending in pictures without dogs in them? This needs to stop. 5/10 for the mediocre road https://t.co/ELqelxWMrC   \n",
       "733           This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq   \n",
       "735                                This is Wesley. He's clearly trespassing. Seems rather h*ckin violent too. Weaponized forehead. 3/10 wouldn't let in https://t.co/pL7wbMRW7M   \n",
       "851                                This is Fido. He can tell the weather. Not good at fetch tho. Never comes when called. 4/10 would probably still pet https://t.co/4gOv2Q3iKP   \n",
       "870                                                                                              Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE   \n",
       "880                                Here's another picture without a dog in it. Idk why you guys keep sending these. 4/10 just because that's a neat rug https://t.co/mOmnL19Wsl   \n",
       "924                                    Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8   \n",
       "946                                                                                              This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh   \n",
       "971                                  Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R   \n",
       "1034                               After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ   \n",
       "1085                                                  Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv   \n",
       "1130                                                                                                           Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a   \n",
       "1154                                       This is Alexanderson. He's got a weird ass birth mark. Dreadful at fetch. Won't eat kibble. 3/10 wtf @Target https://t.co/FmxOpf2Sgl   \n",
       "1167                                                      This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq   \n",
       "1184                                  This is Benedict. He's a feisty pup. Needs a brushing. Portable af. Looks very angry actually. 4/10 might not pet https://t.co/3oeFfHjv0Z   \n",
       "1193                                                                              Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1   \n",
       "1214                                  What hooligan sent in pictures w/out a dog in them? Churlish af. 3/10 just bc that's a neat fluffy bean bag chair https://t.co/wcwoGOkZvz   \n",
       "1219                                                               Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12   \n",
       "1239                         From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK   \n",
       "1267                               This is Keurig. He's a rare dog. Laughs like an idiot tho. Head is basically a weapon. Poorly maintained goatee 4/10 https://t.co/xOrUyj7K30   \n",
       "1278                                     This is Elliot. He's blocking the roadway. Downright rude as hell. Doesn't care that you're already late. 3/10 https://t.co/FMUxir5pYu   \n",
       "1314                                                                                         Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa   \n",
       "1362                               This is Dave. He's a tropical pup. Short lil legs (dachshund mix?) Excels underwater, but refuses to eat kibble 5/10 https://t.co/ZJnCxlIf62   \n",
       "1369                                This is Charl. He's a bully. Chucks that dumbbell around like its nothing. Sharp neck. Exceptionally unfluffy. 3/10 https://t.co/VfLoDZecJ7   \n",
       "1396                                                                               Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ   \n",
       "1420                                 This may be the greatest video I've ever been sent. 4/10 for Charles the puppy, 13/10 overall. (Vid by @stevenxx_) https://t.co/uaJmNgXR2P   \n",
       "1438                                Meet Phil. He's big af. Currently destroying this nice family home. Completely uncalled for. 3/10 not a good pupper https://t.co/fShNNhBWYx   \n",
       "1466                           When bae says they can't go out but you see them with someone else that same night. 5/10 &amp; 10/10 for heartbroken pup https://t.co/aenk0KpoWM   \n",
       "1540                               Army of water dogs here. None of them know where they're going. Have no real purpose. Aggressive barks. 5/10 for all https://t.co/A88x73TwMN   \n",
       "1557                                      This is Hammond. He's a peculiar pup. Loves long walks. Bark barely audible. Too many legs. 3/10 must be rare https://t.co/NOIiRWr5Jf   \n",
       "1573                                  This is Jerry. He's a neat dog. No legs (tragic). Has more horns than a dog usually does. Bark is unique af. 5/10 https://t.co/85q7xlplsJ   \n",
       "1578                                Here we have a basking dino pupper. Looks powerful. Occasionally shits eggs. Doesn't want the holidays to end. 5/10 https://t.co/DnNweb5eTO   \n",
       "1583                               This is Bobby. He doesn't give a damn about personal space. Convinced he called shotgun first. 4/10 not the best dog https://t.co/b8XW69gSaU   \n",
       "1589                               Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55   \n",
       "1599                                                        This is Jiminy. He's not the brightest dog. Needs to lay off the kibble. 5/10 still petable https://t.co/omln4LOy1x   \n",
       "1616                                This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5   \n",
       "1633                               Unique dog here. Wrinkly as hell. Weird segmented neck. Finger on fire. Doesn't seem to notice. 5/10 might still pet https://t.co/Hy9La4xNX3   \n",
       "1644                                     This is Chuck. He's a neat dog. Very flexible. Trapped in a glass case of emotion. Devastatingly unfluffy 3/10 https://t.co/YqbU9xHV3p   \n",
       "1653                                                                                                                This is Alice. She's an idiot. 4/10 https://t.co/VQXdwJfkyS   \n",
       "1664                                                                 Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD   \n",
       "1679                                Meet Penelope. She's a bacon frise. Total babe (lol get it like the movie). Doesn't bark tho. 5/10 very average dog https://t.co/SDcQYg0HSZ   \n",
       "1713                               Exotic pup here. Tail long af. Throat looks swollen. Might breathe fire. Exceptionally unfluffy 2/10 would still pet https://t.co/a8SqCaSo2r   \n",
       "1716                               This is Crystal. She's a shitty fireman. No sense of urgency. People could be dying Crystal. 2/10 just irresponsible https://t.co/rtMtjSl9pz   \n",
       "1730                                                                                                     IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq   \n",
       "1747                                                                        This is Juckson. He's totally on his way to a nascar race. 5/10 for Juckson https://t.co/IoLRvF0Kak   \n",
       "1759                                 Exotic handheld dog here. Appears unathletic. Feet look deadly. Can be thrown a great distance. 5/10 might pet idk https://t.co/Avq4awulqk   \n",
       "1770                               This is Bubbles. He kinda resembles a fish. Always makes eye contact with u no matter what. Sneaky tongue slip. 5/10 https://t.co/Nrhvc5tLFT   \n",
       "1786                                Extremely rare pup here. Very religious. Always praying. Too many legs. Not overwhelmingly fluffy. Won't bark. 3/10 https://t.co/REyE5YKVBb   \n",
       "1793                                                                Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw   \n",
       "1810                                  Rare shielded battle dog here. Very happy about abundance of lettuce. Painfully slow fetcher. Still petable. 5/10 https://t.co/C3tlKVq7eO   \n",
       "1818                                                  What kind of person sends in a picture without a dog in it? 1/10 just because that's a nice table https://t.co/RDXCfk8hK0   \n",
       "1823                                                                         This is Steven. He got locked outside. Damn it Steven. 5/10 nice grill tho https://t.co/zf7Sxxjfp3   \n",
       "1846                                Meet Patrick. He's an exotic pup. Jumps great distances for a dog. Always gets injured when I toss him a ball. 3/10 https://t.co/Unz1uNrOzo   \n",
       "1849                              Two gorgeous dogs here. Little waddling dog is a rebel. Refuses to look at camera. Must be a preteen. 5/10 &amp; 8/10 https://t.co/YPfw7oahbD   \n",
       "1852                               Rare submerged pup here. Holds breath for a long time. Frowning because that spoon ignores him. 5/10 would still pet https://t.co/EJzzNHE8bE   \n",
       "1866                                 This is Henry. He's a shit dog. Short pointy ears. Leaves trail of pee. Not fluffy. Doesn't come when called. 2/10 https://t.co/Pu9RhfHDEQ   \n",
       "1871                                    This is Earl. Earl is lost. Someone help Earl. He has no tags. Just trying to get home. 5/10 hang in there Earl https://t.co/1ZbfqAVDg6   \n",
       "1874                                  Herd of wild dogs here. Not sure what they're trying to do. No real goals in life. 3/10 find your purpose puppers https://t.co/t5ih0VrK02   \n",
       "1884                                Guys I'm getting real tired of this. We only rate dogs. Please don't send in other things like this Bulbasaur. 3/10 https://t.co/t5rQHl6W8M   \n",
       "1886                                     This is a heavily opinionated dog. Loves walls. Nobody knows how the hair works. Always ready for a kiss. 4/10 https://t.co/dFiaKZ9cDl   \n",
       "1892                                Large blue dog here. Cool shades. Flipping us off w both hands. Obviously a preteen. 3/10 for rude blue preteen pup https://t.co/mcPd5AFfhA   \n",
       "1924                                                  Extraordinary dog here. Looks large. Just a head. No body. Rather intrusive. 5/10 would still pet https://t.co/ufHWUFA9Pu   \n",
       "1958                                 Exotic underwater dog here. Very shy. Wont return tennis balls I toss him. Never been petted. 5/10 I bet he's soft https://t.co/WH7Nzc5IBA   \n",
       "1971                                This is Brad. He's a chubby lil pup. Doesn't really need the food he's trying to reach. 5/10 you've had enough Brad https://t.co/vPXKSaNsbE   \n",
       "2007                                  This is Anthony. He just finished up his masters at Harvard. Unprofessional tattoos. Always looks perturbed. 5/10 https://t.co/iHLo9rGay1   \n",
       "2014                                   Two miniature golden retrievers here. Webbed paws. Don't walk very efficiently. Can't catch a tennis ball. 4/10s https://t.co/WzVLdSHJU7   \n",
       "2018                                                                                        After so many requests... here you go.\\n\\nGood dogg. 420/10 https://t.co/yfAAo1gdeY   \n",
       "2020                               Pink dogs here. Unreasonably long necks. Left guy has only 1 leg. Quite nimble. Don't bark tho 4/10s would still pet https://t.co/QY5uvMmmQk   \n",
       "2023                                Scary dog here. Too many legs. Extra tail. Not soft, let alone fluffy. Won't bark. Moves sideways. Has weapon. 2/10 https://t.co/XOPXCSXiUT   \n",
       "2035                                   Flamboyant pup here. Probably poisonous. Won't eat kibble. Doesn't bark. Slow af. Petting doesn't look fun. 1/10 https://t.co/jxukeh2BeO   \n",
       "2036                                                                                              This dude slaps your girl's ass what do you do?\\n5/10 https://t.co/6dioUL6gcP   \n",
       "2053                               Vibrant dog here. Fabulous tail. Only 2 legs tho. Has wings but can barely fly (lame). Rather elusive. 5/10 okay pup https://t.co/cixC0M3P1e   \n",
       "2078                               This is Randall. He's from Chernobyl. Built playground himself. Has been stuck up there quite a while. 5/10 good dog https://t.co/pzrvc7wKGd   \n",
       "2080                                         This is Tommy. He's a cool dog. Hard not to step on. Won't let go of seashell. Not fast by any means. 3/10 https://t.co/0gY6XTOpn3   \n",
       "2083                               Awesome dog here. Not sure where it is tho. Spectacular camouflage. Enjoys leaves. Not very soft. 5/10 still petable https://t.co/rOTOteKx4q   \n",
       "2096                                This is a brave dog. Excellent free climber. Trying to get closer to God. Not very loyal though. Doesn't bark. 5/10 https://t.co/ODnILTr4QM   \n",
       "2124                               Two gorgeous pups here. Both have cute fake horns(adorable). Barn in the back looks on fire. 5/10 would pet rly well https://t.co/w5oYFXi0uh   \n",
       "2126                               This is Bernie. He's taking his Halloween costume very seriously. Wants to be baked. 3/10 not a good idea Bernie smh https://t.co/1zBp1moFlX   \n",
       "2129                                 Unique dog here. Oddly shaped tail. Long pink front legs. I don't think dogs breath underwater sos. 4/10 bad owner https://t.co/0EJXxE9UxW   \n",
       "2144                               Fascinating dog here. Loves beach. Oddly long nose for dog. Massive ass paws. Hard to cuddle w. 3/10 would still pet https://t.co/IiSdmhkC5N   \n",
       "2148                                    Meet Zeek. He is a grey Cumulonimbus. Zeek is hungry. Someone should feed Zeek asap. 5/10 absolutely terrifying https://t.co/fvVNScw8VH   \n",
       "2164                                    Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m   \n",
       "2179                               This lil pup is Oliver. Hops around. Has wings but doesn't fly (lame). Annoying chirp. Won't catch tennis balls 2/10 https://t.co/DnhUw0aBM2   \n",
       "2181                                 This dog resembles a baked potato. Bed looks uncomfortable. No tail. Comes with butter tho. 3/10 petting still fun https://t.co/x89NSCEZCq   \n",
       "2184                                 Wow. Armored dog here. Ready for battle. Face looks dangerous. Not very loyal. Lil dog on back havin a blast. 5/10 https://t.co/SyMoWrp368   \n",
       "2188                                This is Tedrick. He lives on the edge. Needs someone to hit the gas tho. Other than that he's a baller. 10&amp;2/10 https://t.co/LvP1TTYSCN   \n",
       "2203                               Never seen dog like this. Breathes heavy. Tilts head in a pattern. No bark. Shitty at fetch. Not even cordless. 1/10 https://t.co/i9iSGNn3fx   \n",
       "2230                               These are strange dogs. All have toupees. Long neck for dogs. In a shed of sorts? Work in groups? 4/10 still petable https://t.co/PZxSarAfSN   \n",
       "2246                                 My goodness. Very rare dog here. Large. Tail dangerous. Kinda fat. Only eats leaves. Doesn't come when called 3/10 https://t.co/xYGdBrMS9h   \n",
       "2251                                Unfamiliar with this breed. Ears pointy af. Won't let go of seashell. Won't eat kibble. Not very fast. Bad dog 2/10 https://t.co/EIn5kElY1S   \n",
       "2253                                This is Josep. He is a Rye Manganese mix. Can drive w eyes closed. Very irresponsible. Menace on the roadways. 5/10 https://t.co/XNGeDwrtYH   \n",
       "2257                                 Cool dog. Enjoys couch. Low monotone bark. Very nice kicks. Pisses milk (must be rare). Can't go down stairs. 4/10 https://t.co/vXMKrJC81s   \n",
       "2267                               This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t   \n",
       "2275                                 This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0   \n",
       "2276                                   This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv   \n",
       "2279                                     Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX   \n",
       "2290                                 This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc   \n",
       "2292                                                   Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq   \n",
       "\n",
       "      rating_numerator  rating_denominator  \n",
       "44                   5                  10  \n",
       "298                  0                  10  \n",
       "410                 84                  70  \n",
       "491                 24                   7  \n",
       "667                 75                  10  \n",
       "700                  5                  10  \n",
       "733                 27                  10  \n",
       "735                  3                  10  \n",
       "851                  4                  10  \n",
       "870                165                 150  \n",
       "880                  4                  10  \n",
       "924                  5                  10  \n",
       "946               1776                  10  \n",
       "971                  4                  10  \n",
       "1034                 9                  11  \n",
       "1085               204                 170  \n",
       "1130                 4                  20  \n",
       "1154                 3                  10  \n",
       "1167                50                  50  \n",
       "1184                 4                  10  \n",
       "1193                99                  90  \n",
       "1214                 3                  10  \n",
       "1219                80                  80  \n",
       "1239                45                  50  \n",
       "1267                 4                  10  \n",
       "1278                 3                  10  \n",
       "1314                60                  50  \n",
       "1362                 5                  10  \n",
       "1369                 3                  10  \n",
       "1396                44                  40  \n",
       "1420                 4                  10  \n",
       "1438                 3                  10  \n",
       "1466                 5                  10  \n",
       "1540                 5                  10  \n",
       "1557                 3                  10  \n",
       "1573                 5                  10  \n",
       "1578                 5                  10  \n",
       "1583                 4                  10  \n",
       "1589               121                 110  \n",
       "1599                 5                  10  \n",
       "1616                 7                  11  \n",
       "1633                 5                  10  \n",
       "1644                 3                  10  \n",
       "1653                 4                  10  \n",
       "1664                26                  10  \n",
       "1679                 5                  10  \n",
       "1713                 2                  10  \n",
       "1716                 2                  10  \n",
       "1730               144                 120  \n",
       "1747                 5                  10  \n",
       "1759                 5                  10  \n",
       "1770                 5                  10  \n",
       "1786                 3                  10  \n",
       "1793                88                  80  \n",
       "1810                 5                  10  \n",
       "1818                 1                  10  \n",
       "1823                 5                  10  \n",
       "1846                 3                  10  \n",
       "1849                 5                  10  \n",
       "1852                 5                  10  \n",
       "1866                 2                  10  \n",
       "1871                 5                  10  \n",
       "1874                 3                  10  \n",
       "1884                 3                  10  \n",
       "1886                 4                  10  \n",
       "1892                 3                  10  \n",
       "1924                 5                  10  \n",
       "1958                 5                  10  \n",
       "1971                 5                  10  \n",
       "2007                 5                  10  \n",
       "2014                 4                  10  \n",
       "2018               420                  10  \n",
       "2020                 4                  10  \n",
       "2023                 2                  10  \n",
       "2035                 1                  10  \n",
       "2036                 5                  10  \n",
       "2053                 5                  10  \n",
       "2078                 5                  10  \n",
       "2080                 3                  10  \n",
       "2083                 5                  10  \n",
       "2096                 5                  10  \n",
       "2124                 5                  10  \n",
       "2126                 3                  10  \n",
       "2129                 4                  10  \n",
       "2144                 3                  10  \n",
       "2148                 5                  10  \n",
       "2164                 4                  10  \n",
       "2179                 2                  10  \n",
       "2181                 3                  10  \n",
       "2184                 5                  10  \n",
       "2188                 2                  10  \n",
       "2203                 1                  10  \n",
       "2230                 4                  10  \n",
       "2246                 3                  10  \n",
       "2251                 2                  10  \n",
       "2253                 5                  10  \n",
       "2257                 4                  10  \n",
       "2267                 2                  10  \n",
       "2275                 3                  10  \n",
       "2276                 1                   2  \n",
       "2279                 1                  10  \n",
       "2290                 2                  10  \n",
       "2292                 5                  10  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odd_ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**After visual inspecting and observations:**\n",
    "\n",
    "**We can summarize these odd values into two categories as below**\n",
    "- ***Cat I:***\n",
    "    - Ratings are floats (fraction numerator) and aren't succefully captuerd from the text.\n",
    "    - We could develop a regex pattern to capture the whole rating.\n",
    "    - on indexes: (44, 667, 733, 1664).<br><br>\n",
    "- ***Cat II:***\n",
    "    - Captured the wrong rating as the text have other numbers other than the rating.\n",
    "    - There are more than one dog in the image.\n",
    "    - We could manually fix these.\n",
    "    - on indexes:\n",
    "        - **410:** `84/70` there are 7 dogs, the correct rating is 12/10.\n",
    "        - **491:** `...smiles 24/7..` is an expression not a rating, there is no rating in the text.\n",
    "        - **870:** `165/150` there are 15 dogs, the correct rating is 11/10.\n",
    "        - **946:** `1776/10` is an abnormal value should be deleted.\n",
    "        - **1034:** `..surviving 9/11..` is a date not a rating, correct rating is 14/10.\n",
    "        - **1085:** `204/170` there are 17 dogs, the correct rating is 12/10.\n",
    "        - **1130:** `Happy 4/20..` is a date not a rating, correct rating is 13/10.\n",
    "        - **1167:** `... split 50/50` is a percentage not a rating, correct rating is 11/10.\n",
    "        - **1193:** `..9 puppers ... 99/90..` there are 9 puppers, the correct rating is 11/10.\n",
    "        - **1219:** `80/80` there are 8 dogs, the correct rating is 10/10.\n",
    "        - **1239:** `45/50` there are 5 dogs, the correct rating is 9/10.\n",
    "        - **1314:** `60/50` there are 5 dogs, the correct rating is 12/10.\n",
    "        - **1396:** `44/40` there are 4 dogs, the correct rating is 11/10.\n",
    "        - **1466:** ` 5/10 & 10/10` there are two ratings we could calculate the average. 7.5/10\n",
    "        - **1589:** `121/110` there are 11 dogs, the correct rating is 11/10.\n",
    "        - **1730:** `144/120 ` there are 12 dogs, the correct rating is 12/10.\n",
    "        - **1793:** `88/8 ` there are 8 dogs, the correct rating is 11/10.\n",
    "        - **1849:** ` 5/10 & 8/10` there are two ratings we could calculate the average. 6.5/10\n",
    "        - **2018:** `420/10` snoop dogg deserves more ;). will be deleted.\n",
    "         \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cat I - Code** \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([('13.5', '10')], [('11.26', '10')])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test regex pattern\n",
    "test_pattern = re.compile(r'(\\d+\\.?\\d?\\d?)\\/(\\d{1,3})')\n",
    "\n",
    "test_text_01 = \"This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948\"\n",
    "test_text_02 = \"Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD\"\n",
    "\n",
    "match_01 = re.findall(test_pattern, test_text_01)\n",
    "match_02 = re.findall(test_pattern, test_text_02)\n",
    "\n",
    "match_01, match_02"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Works well!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fixing\n",
    "pattern = r'(\\d+\\.?\\d?\\d?)\\/(\\d{1,3})'\n",
    "\n",
    "archive_clean['rating_numerator'] = archive_clean.text.str.extract(pattern, expand=False).astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cat I - Test** \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948</td>\n",
       "      <td>13.50</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS</td>\n",
       "      <td>9.75</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq</td>\n",
       "      <td>11.27</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1664</th>\n",
       "      <td>Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD</td>\n",
       "      <td>11.26</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                   text  \\\n",
       "44                             This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948   \n",
       "667                                    This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS   \n",
       "733   This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq   \n",
       "1664                                                         Here we have uncovered an entire battalion of holiday puppers. Average of 11.26/10 https://t.co/eNm2S6p9BD   \n",
       "\n",
       "      rating_numerator  rating_denominator  \n",
       "44               13.50                  10  \n",
       "667               9.75                  10  \n",
       "733              11.27                  10  \n",
       "1664             11.26                  10  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.loc[[44, 667, 733, 1664], ['text', 'rating_numerator', 'rating_denominator']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cat II - Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 410: correct rating is 12/10\n",
    "archive_clean.loc[410, ['rating_numerator', 'rating_denominator']] =  [12, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 491: there is no rating in the text\n",
    "archive_clean.loc[491, ['rating_numerator', 'rating_denominator']] =  [np.nan, np.nan]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 870: correct rating is 11/10\n",
    "archive_clean.loc[870, ['rating_numerator', 'rating_denominator']] =  [11, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 946: abnormal value should be deleted\n",
    "archive_clean.drop(946, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1034: correct rating is 14/10\n",
    "archive_clean.loc[1034, ['rating_numerator', 'rating_denominator']] =  [14, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1085: correct rating is 12/10\n",
    "archive_clean.loc[1085, ['rating_numerator', 'rating_denominator']] =  [12, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1130: correct rating is 13/10\n",
    "archive_clean.loc[1130, ['rating_numerator', 'rating_denominator']] =  [13, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1167: correct rating is 11/10\n",
    "archive_clean.loc[1167, ['rating_numerator', 'rating_denominator']] =  [11, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1193: the correct rating is 11/10\n",
    "archive_clean.loc[1193, ['rating_numerator', 'rating_denominator']] =  [11, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1219: correct rating is 10/10\n",
    "archive_clean.loc[1219, ['rating_numerator', 'rating_denominator']] =  [10, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1239: correct rating is 9/10\n",
    "archive_clean.loc[1239, ['rating_numerator', 'rating_denominator']] =  [9, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1314: correct rating is 12/10\n",
    "archive_clean.loc[1314, ['rating_numerator', 'rating_denominator']] =  [12, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1396: correct rating is 11/10\n",
    "archive_clean.loc[1396, ['rating_numerator', 'rating_denominator']] =  [11, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1466: the average rating: 7.5/10\n",
    "archive_clean.loc[1466, ['rating_numerator', 'rating_denominator']] =  [7.5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1589: the correct rating is 11/10\n",
    "archive_clean.loc[1589, ['rating_numerator', 'rating_denominator']] =  [11, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1730: the correct rating is 12/10\n",
    "archive_clean.loc[1730, ['rating_numerator', 'rating_denominator']] =  [12, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1793: the correct rating is 11/10\n",
    "archive_clean.loc[1793, ['rating_numerator', 'rating_denominator']] =  [11, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1849: the average rating: 6.5/10\n",
    "archive_clean.loc[1849, ['rating_numerator', 'rating_denominator']] =  [6.5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2018: not a dog\n",
    "archive_clean.drop(2018, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cat II - Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1219</th>\n",
       "      <td>Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp;amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>When bae says they can't go out but you see them with someone else that same night. 5/10 &amp;amp; 10/10 for heartbroken pup https://t.co/aenk0KpoWM</td>\n",
       "      <td>7.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1589</th>\n",
       "      <td>Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>Two gorgeous dogs here. Little waddling dog is a rebel. Refuses to look at camera. Must be a preteen. 5/10 &amp;amp; 8/10 https://t.co/YPfw7oahbD</td>\n",
       "      <td>6.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                           text  \\\n",
       "410                                                                         The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd   \n",
       "491   Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx   \n",
       "870                                                                                              Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE   \n",
       "1034                               After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ   \n",
       "1085                                                  Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv   \n",
       "1130                                                                                                           Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a   \n",
       "1167                                                      This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq   \n",
       "1193                                                                              Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1   \n",
       "1219                                                               Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12   \n",
       "1239                         From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK   \n",
       "1314                                                                                         Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa   \n",
       "1396                                                                               Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ   \n",
       "1466                           When bae says they can't go out but you see them with someone else that same night. 5/10 &amp; 10/10 for heartbroken pup https://t.co/aenk0KpoWM   \n",
       "1589                               Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55   \n",
       "1730                                                                                                     IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq   \n",
       "1793                                                                Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw   \n",
       "1849                              Two gorgeous dogs here. Little waddling dog is a rebel. Refuses to look at camera. Must be a preteen. 5/10 &amp; 8/10 https://t.co/YPfw7oahbD   \n",
       "\n",
       "      rating_numerator  rating_denominator  \n",
       "410               12.0                10.0  \n",
       "491                NaN                 NaN  \n",
       "870               11.0                10.0  \n",
       "1034              14.0                10.0  \n",
       "1085              12.0                10.0  \n",
       "1130              13.0                10.0  \n",
       "1167              11.0                10.0  \n",
       "1193              11.0                10.0  \n",
       "1219              10.0                10.0  \n",
       "1239               9.0                10.0  \n",
       "1314              12.0                10.0  \n",
       "1396              11.0                10.0  \n",
       "1466               7.5                10.0  \n",
       "1589              11.0                10.0  \n",
       "1730              12.0                10.0  \n",
       "1793              11.0                10.0  \n",
       "1849               6.5                10.0  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.loc[[410, 491, 870, 1034, 1085, 1130, 1167, 1193, 1219, 1239, 1314, 1396,1466, 1589, 1730, 1793, 1849], ['text', 'rating_numerator', 'rating_denominator']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='store'></a>\n",
    "## 4. Storing cleaned data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exporting cleaned data to CSV files, and SQL databases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To CSV Files**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to CSV files\n",
    "archive_clean.to_csv('twitter_archive_master.csv', index=False, encoding='utf-8')\n",
    "image_predictions_clean.to_csv('image_predictions_master.csv', index=False, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To database**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to database\n",
    "# Create SQLAlchemy Engine\n",
    "engine = create_engine('sqlite:///master_df.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean.to_sql('twitter_archive_master', engine, index=False)\n",
    "image_predictions_clean.to_sql('image_predictions_master', engine, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='analysis_vsualization'></a>\n",
    "## 5. Analysis and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='rate'></a>\n",
    "## 5.a Ratings analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1968.000000\n",
       "mean       10.544604\n",
       "std         2.173267\n",
       "min         0.000000\n",
       "25%        10.000000\n",
       "50%        11.000000\n",
       "75%        12.000000\n",
       "max        14.000000\n",
       "Name: rating_numerator, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.rating_numerator.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 6))\n",
    "plt.style.use('seaborn-pastel')\n",
    "\n",
    "bins = np.arange(0, 16, 1)\n",
    "plt.hist(x = archive_clean.rating_numerator, linewidth=1, edgecolor='white', bins=bins)\n",
    "\n",
    "plt.title('Dog ratings counts', color='darkblue', fontsize=15)\n",
    "plt.xlabel('Ratings', fontsize=12)\n",
    "plt.ylabel('Dogs count', fontsize=12)\n",
    "\n",
    "plt.savefig('.\\img\\Dogs_ratings_counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Histogram is heavily skewed right.**\n",
    "\n",
    "**It seems that the majority rating of dogs are obove 10.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='fav-rt'></a>\n",
    "### 5.b Favorites and retweets analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1969.000000</td>\n",
       "      <td>1969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2783.673946</td>\n",
       "      <td>8951.310818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4699.921113</td>\n",
       "      <td>12273.776070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>628.000000</td>\n",
       "      <td>1985.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1367.000000</td>\n",
       "      <td>4144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3238.000000</td>\n",
       "      <td>11411.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79515.000000</td>\n",
       "      <td>132810.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       retweet_count  favorite_count\n",
       "count    1969.000000     1969.000000\n",
       "mean     2783.673946     8951.310818\n",
       "std      4699.921113    12273.776070\n",
       "min        16.000000       81.000000\n",
       "25%       628.000000     1985.000000\n",
       "50%      1367.000000     4144.000000\n",
       "75%      3238.000000    11411.000000\n",
       "max     79515.000000   132810.000000"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean[['retweet_count' , 'favorite_count']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "plt.hist(archive_clean.favorite_count, alpha=.4, label='Favorites')\n",
    "plt.hist(archive_clean.retweet_count, alpha=.4, label='Retweets')\n",
    "\n",
    "plt.title('Distribution of Favorites and Retweets Counts', color='darkblue', fontsize=15)\n",
    "plt.xlabel('Number of Favorites - Retweets', fontsize=12)\n",
    "plt.ylabel('Counts', fontsize=12)\n",
    "\n",
    "plt.xlim(-1, 80000)\n",
    "\n",
    "plt.legend(prop={'size': 15})\n",
    "\n",
    "plt.savefig('.\\img\\Favs_retweets_counts')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Distribution of favorites counts and retweets count follow the same pattern.**\n",
    "\n",
    "**Let's check correlation.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot\n",
    "plt.figure(figsize=(14, 10))\n",
    "plt.style.use('seaborn-white')\n",
    "\n",
    "plt.plot(archive_clean.favorite_count, archive_clean.retweet_count,'bo', markersize=1.5)\n",
    "plt.title('Scatter plot of Favorites and Retweets counts', color='darkred', fontsize=15)\n",
    "plt.xlabel('Favorites count', fontsize=12)\n",
    "plt.ylabel('Retweets count', fontsize=12)\n",
    "\n",
    "plt.xlim(0, 50000)\n",
    "plt.ylim(0, 10000)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('.\\img\\scatter_favs_retweets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Good correlation.**\n",
    "\n",
    "**Check log<sub>10</sub>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make copies of dataframes to apply log10\n",
    "log_df = archive_clean.copy()\n",
    "log_df = log_df[['favorite_count', 'retweet_count']]\n",
    "log_df['favorite_count'] = archive_clean.favorite_count.apply(lambda x: np.log10(x))\n",
    "log_df['retweet_count'] = archive_clean.retweet_count.apply(lambda x: np.log10(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(12, 8), dpi=300)\n",
    "plt.style.use('seaborn-white')\n",
    "\n",
    "\n",
    "plt.plot(log_df.favorite_count, log_df.retweet_count, 'bo', markersize=1.5)\n",
    "\n",
    "plt.title('Scatter plot of Log10 of Favorites and Retweets',color='darkred', fontsize=15)\n",
    "plt.xlabel('Favorites count', fontsize=12)\n",
    "plt.ylabel('Retweets count', fontsize=12)\n",
    "\n",
    "\n",
    "plt.savefig('.\\img\\Scatter_log-favs_log-retweets')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**People who likes the tweets almost retweets it.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='month-day'></a>\n",
    "### 5.c Favorites and retweets analysis by months and days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>timestamp</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>2017-01-21 18:26:02+00:00</td>\n",
       "      <td>48265.0</td>\n",
       "      <td>132810.0</td>\n",
       "      <td>January</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>2016-12-21 01:44:13+00:00</td>\n",
       "      <td>7444.0</td>\n",
       "      <td>23302.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>2017-03-03 01:14:41+00:00</td>\n",
       "      <td>2631.0</td>\n",
       "      <td>13967.0</td>\n",
       "      <td>March</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>2016-11-14 17:03:50+00:00</td>\n",
       "      <td>2954.0</td>\n",
       "      <td>11548.0</td>\n",
       "      <td>November</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>2015-11-30 15:59:17+00:00</td>\n",
       "      <td>157.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>November</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>2016-11-05 15:37:24+00:00</td>\n",
       "      <td>2697.0</td>\n",
       "      <td>11492.0</td>\n",
       "      <td>November</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>2016-08-13 00:38:30+00:00</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>6718.0</td>\n",
       "      <td>August</td>\n",
       "      <td>Saturday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2214</th>\n",
       "      <td>2015-11-19 23:53:52+00:00</td>\n",
       "      <td>242.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>November</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2016-03-10 20:26:26+00:00</td>\n",
       "      <td>2166.0</td>\n",
       "      <td>4799.0</td>\n",
       "      <td>March</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1932</th>\n",
       "      <td>2015-12-04 20:38:19+00:00</td>\n",
       "      <td>405.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>December</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     timestamp  retweet_count  favorite_count     month  \\\n",
       "391  2017-01-21 18:26:02+00:00        48265.0        132810.0   January   \n",
       "490  2016-12-21 01:44:13+00:00         7444.0         23302.0  December   \n",
       "279  2017-03-03 01:14:41+00:00         2631.0         13967.0     March   \n",
       "580  2016-11-14 17:03:50+00:00         2954.0         11548.0  November   \n",
       "2000 2015-11-30 15:59:17+00:00          157.0           426.0  November   \n",
       "600  2016-11-05 15:37:24+00:00         2697.0         11492.0  November   \n",
       "825  2016-08-13 00:38:30+00:00         1745.0          6718.0    August   \n",
       "2214 2015-11-19 23:53:52+00:00          242.0           559.0  November   \n",
       "1258 2016-03-10 20:26:26+00:00         2166.0          4799.0     March   \n",
       "1932 2015-12-04 20:38:19+00:00          405.0          1096.0  December   \n",
       "\n",
       "            day  \n",
       "391    Saturday  \n",
       "490   Wednesday  \n",
       "279      Friday  \n",
       "580      Monday  \n",
       "2000     Monday  \n",
       "600    Saturday  \n",
       "825    Saturday  \n",
       "2214   Thursday  \n",
       "1258   Thursday  \n",
       "1932     Friday  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Copy\n",
    "date_df = archive_clean.copy()\n",
    "date_df = date_df[['timestamp', 'retweet_count' ,'favorite_count']]\n",
    "\n",
    "# extract 'month' and 'day' columns from timestamp\n",
    "date_df['month'] = date_df.timestamp.dt.month_name()\n",
    "date_df['day'] = date_df.timestamp.dt.day_name()\n",
    "\n",
    "date_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>320884.0</td>\n",
       "      <td>1178300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>232007.0</td>\n",
       "      <td>681425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>837739.0</td>\n",
       "      <td>2142175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>457818.0</td>\n",
       "      <td>1664466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>677532.0</td>\n",
       "      <td>1965498.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>552250.0</td>\n",
       "      <td>2166996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>616602.0</td>\n",
       "      <td>2033679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>424441.0</td>\n",
       "      <td>1514301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>440206.0</td>\n",
       "      <td>1561307.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>411448.0</td>\n",
       "      <td>1132428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>277828.0</td>\n",
       "      <td>864121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>232299.0</td>\n",
       "      <td>720435.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           retweet_count  favorite_count\n",
       "month                                   \n",
       "April           320884.0       1178300.0\n",
       "August          232007.0        681425.0\n",
       "December        837739.0       2142175.0\n",
       "February        457818.0       1664466.0\n",
       "January         677532.0       1965498.0\n",
       "July            552250.0       2166996.0\n",
       "June            616602.0       2033679.0\n",
       "March           424441.0       1514301.0\n",
       "May             440206.0       1561307.0\n",
       "November        411448.0       1132428.0\n",
       "October         277828.0        864121.0\n",
       "September       232299.0        720435.0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of favorites and retweets by month\n",
    "month_df = date_df.groupby('month').sum()\n",
    "month_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "ax = month_df[['retweet_count','favorite_count']].plot(kind='bar', title =\"Retweets - Favorites counts over months\" ,figsize=(14, 8), legend=True, fontsize=15)\n",
    "\n",
    "ax.set_xlabel(\"Months\", fontsize=20)\n",
    "ax.set_yticklabels([])\n",
    "\n",
    "plt.legend(prop={'size':15})\n",
    "\n",
    "plt.savefig('.\\img\\Retweets-favs-months')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Highest number of Retweets and Favorites were in January and December.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>782096.0</td>\n",
       "      <td>2439293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>857298.0</td>\n",
       "      <td>2762536.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>742796.0</td>\n",
       "      <td>2248024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>676283.0</td>\n",
       "      <td>2212329.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>711520.0</td>\n",
       "      <td>2347013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>796491.0</td>\n",
       "      <td>2666200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>914570.0</td>\n",
       "      <td>2949736.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           retweet_count  favorite_count\n",
       "day                                     \n",
       "Friday          782096.0       2439293.0\n",
       "Monday          857298.0       2762536.0\n",
       "Saturday        742796.0       2248024.0\n",
       "Sunday          676283.0       2212329.0\n",
       "Thursday        711520.0       2347013.0\n",
       "Tuesday         796491.0       2666200.0\n",
       "Wednesday       914570.0       2949736.0"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of favorites and retweets by day\n",
    "day_df = date_df.groupby('day').sum()\n",
    "day_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "ax = day_df[['retweet_count','favorite_count']].plot(kind='bar', title =\"Retweets - Favorites counts over days\", figsize=(14, 8), legend=True, fontsize=18)\n",
    "\n",
    "ax.set_xlabel(\"days\", fontsize=20)\n",
    "ax.set_yticklabels([])\n",
    "\n",
    "plt.legend(prop={'size':15})\n",
    "\n",
    "plt.savefig('.\\img\\Retweets-favs-day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Highest number of Retweets and Favorites were on Wednesday.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <a id='stage'></a>\n",
    " ### 5.d Dog stage analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is the dog stage has received the largest number of retweets and favorites?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog_stage</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>doggo</th>\n",
       "      <td>11.888889</td>\n",
       "      <td>7125.698413</td>\n",
       "      <td>19356.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doggo-floofer</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>3433.000000</td>\n",
       "      <td>17169.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doggo-pupper</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>4397.250000</td>\n",
       "      <td>13219.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doggo-puppo</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>19196.000000</td>\n",
       "      <td>47844.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>floofer</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>4968.714286</td>\n",
       "      <td>13206.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pupper</th>\n",
       "      <td>10.638159</td>\n",
       "      <td>2382.502488</td>\n",
       "      <td>7250.527363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>puppo</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>6473.954545</td>\n",
       "      <td>21582.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               rating_numerator  retweet_count  favorite_count\n",
       "dog_stage                                                     \n",
       "doggo                 11.888889    7125.698413    19356.380952\n",
       "doggo-floofer         11.000000    3433.000000    17169.000000\n",
       "doggo-pupper          11.000000    4397.250000    13219.875000\n",
       "doggo-puppo           13.000000   19196.000000    47844.000000\n",
       "floofer               12.000000    4968.714286    13206.000000\n",
       "pupper                10.638159    2382.502488     7250.527363\n",
       "puppo                 12.000000    6473.954545    21582.090909"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df copy\n",
    "stage_df = archive_clean.groupby('dog_stage').mean()\n",
    "stage_df = stage_df[['rating_numerator','retweet_count', 'favorite_count']]\n",
    "stage_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "ax = stage_df[['retweet_count' , 'favorite_count']].plot(kind='bar', title =\" Ratings - Retweets - Favorites mean of dog's stage\", figsize=(15, 10), legend=True, fontsize=18)\n",
    "\n",
    "ax.set_xlabel(\"Dog stage\", fontsize=15)\n",
    "ax.set_ylabel(\"Count\", fontsize=15)\n",
    "\n",
    "plt.legend(prop={'size':15})\n",
    "\n",
    "\n",
    "plt.savefig('.\\img\\Dog-stage-retweets-favs-rating')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**'puppo' & 'doggo' have received the largest number of retweets and favorites.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='breed'></a>\n",
    "### 5.e Dog breed analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's figure out which breeds get favorited the most.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2075, 4)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# copy of images\n",
    "images_copy = image_predictions_clean.copy()\n",
    "images_copy = images_copy[['tweet_id' ,'prediction_1' ,'confidence_1', 'is_dog_1']]\n",
    "images_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1969, 4)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# copy of archive \n",
    "archive_copy = archive_clean.copy()\n",
    "archive_copy = archive_copy[['tweet_id' , 'rating_numerator' , 'retweet_count' , 'favorite_count']]\n",
    "archive_copy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1969, 7)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# left merge the datasets\n",
    "breed_clean = pd.merge(left=archive_copy, right=images_copy, on='tweet_id')\n",
    "breed_clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>rating_numerator</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>prediction_1</th>\n",
       "      <th>confidence_1</th>\n",
       "      <th>is_dog_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8853.0</td>\n",
       "      <td>39467.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6514.0</td>\n",
       "      <td>33819.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4328.0</td>\n",
       "      <td>25461.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8964.0</td>\n",
       "      <td>42908.0</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9774.0</td>\n",
       "      <td>41048.0</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  rating_numerator  retweet_count  favorite_count  \\\n",
       "0  892420643555336193              13.0         8853.0         39467.0   \n",
       "1  892177421306343426              13.0         6514.0         33819.0   \n",
       "2  891815181378084864              12.0         4328.0         25461.0   \n",
       "3  891689557279858688              13.0         8964.0         42908.0   \n",
       "4  891327558926688256              12.0         9774.0         41048.0   \n",
       "\n",
       "  prediction_1  confidence_1  is_dog_1  \n",
       "0       orange      0.097049     False  \n",
       "1    Chihuahua      0.323581      True  \n",
       "2    Chihuahua      0.716012      True  \n",
       "3  paper_towel      0.170278     False  \n",
       "4       basset      0.555712      True  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breed_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only dogs == true\n",
    "breed_clean = breed_clean[breed_clean['is_dog_1'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>prediction_1</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>1693475.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>1116811.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>994755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>673526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Samoyed</td>\n",
       "      <td>542991.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>465738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>chow</td>\n",
       "      <td>404241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>pug</td>\n",
       "      <td>320223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>315825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>299114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Cardigan</td>\n",
       "      <td>260287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>250648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>malamute</td>\n",
       "      <td>249179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>232997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>216028.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>203093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Lakeland_terrier</td>\n",
       "      <td>198198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>187889.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Staffordshire_bullterrier</td>\n",
       "      <td>187099.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Great_Pyrenees</td>\n",
       "      <td>181048.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  prediction_1  favorite_count\n",
       "87            golden_retriever       1693475.0\n",
       "35          Labrador_retriever       1116811.0\n",
       "47                    Pembroke        994755.0\n",
       "15                   Chihuahua        673526.0\n",
       "53                     Samoyed        542991.0\n",
       "22              French_bulldog        465738.0\n",
       "79                        chow        404241.0\n",
       "99                         pug        320223.0\n",
       "81              cocker_spaniel        315825.0\n",
       "48                  Pomeranian        299114.0\n",
       "13                    Cardigan        260287.0\n",
       "106                 toy_poodle        250648.0\n",
       "93                    malamute        249179.0\n",
       "14    Chesapeake_Bay_retriever        232997.0\n",
       "21                  Eskimo_dog        216028.0\n",
       "23             German_shepherd        203093.0\n",
       "36            Lakeland_terrier        198198.0\n",
       "95          miniature_pinscher        187889.0\n",
       "59   Staffordshire_bullterrier        187099.0\n",
       "27              Great_Pyrenees        181048.0"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most favs\n",
    "breed_fav = breed_clean.groupby('prediction_1').sum().reset_index()\n",
    "breed_fav = breed_fav[['prediction_1', 'favorite_count']]\n",
    "breed_fav = breed_fav.sort_values('favorite_count', ascending = False)[:20]\n",
    "breed_fav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.barh(range(len(breed_fav.prediction_1)), breed_fav.favorite_count) \n",
    "\n",
    "plt.yticks(range(len(breed_fav.prediction_1)), breed_fav.prediction_1)\n",
    "\n",
    "plt.title('Bar chart of dogs\\' breeds with the top favorites counts', color = 'darkblue', fontsize=18)\n",
    "\n",
    "plt.ylabel('Dog Breeds', fontsize=15)\n",
    "plt.xlabel('Total number of Favorites',fontsize=15)\n",
    "\n",
    "\n",
    "plt.savefig('.\\img\\Dog-breed-favs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Golden Retriever has the most amount of favorites, followed by the Labrador Retriever and Pembroke.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![img](https://i0.kym-cdn.com/entries/icons/original/000/008/342/ihave.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}