wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 0,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "# Project: Wrangling and Analyze Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Gathering\n",
    "In the cell below, gather **all** three pieces of data for this project and load them in the notebook. **Note:** the methods required to gather each data are different.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Using the HTML magic command to render tables with grid/borders to facilitate easy viewing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "table.dataframe td, table.dataframe th {\n",
       "    border: 1px black solid !important;\n",
       "    color: black !important;\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<style type=\"text/css\">\n",
    "table.dataframe td, table.dataframe th {\n",
    "    border: 1px black solid !important;\n",
    "    color: black !important;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The files were gathered through the alternative option provided - downloading resources directly from the classroom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive = pd.read_csv('twitter_archive_enhanced.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_predictions = pd.read_csv('image_predictions.tsv', sep = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('tweet_json.txt') as file:\n",
    "    data = [json.loads(line) for line in open ('tweet_json.txt', 'r')]\n",
    "df=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('tweet_json.txt') as file:\n",
    "    data = file.readline() \n",
    "    data = []\n",
    "    for lines in file:\n",
    "        data.append(json.loads(lines))\n",
    "tweet_json = pd.DataFrame(data)            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>id</th>\n",
       "      <th>id_str</th>\n",
       "      <th>full_text</th>\n",
       "      <th>truncated</th>\n",
       "      <th>display_text_range</th>\n",
       "      <th>entities</th>\n",
       "      <th>extended_entities</th>\n",
       "      <th>source</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>...</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>favorited</th>\n",
       "      <th>retweeted</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>possibly_sensitive_appealable</th>\n",
       "      <th>lang</th>\n",
       "      <th>retweeted_status</th>\n",
       "      <th>quoted_status_id</th>\n",
       "      <th>quoted_status_id_str</th>\n",
       "      <th>quoted_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tue Aug 01 00:17:27 +0000 2017</td>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 138]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 892177413194625024, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>33819</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mon Jul 31 00:18:03 +0000 2017</td>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 121]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 891815175371796480, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25461</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sun Jul 30 15:58:51 +0000 2017</td>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 79]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 891689552724799489, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>42908</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat Jul 29 16:00:24 +0000 2017</td>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 138]</td>\n",
       "      <td>{'hashtags': [{'text': 'BarkWeek', 'indices': ...</td>\n",
       "      <td>{'media': [{'id': 891327551943041024, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>41048</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat Jul 29 00:08:17 +0000 2017</td>\n",
       "      <td>891087950875897856</td>\n",
       "      <td>891087950875897856</td>\n",
       "      <td>Here we have a majestic great white breaching ...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 138]</td>\n",
       "      <td>{'hashtags': [{'text': 'BarkWeek', 'indices': ...</td>\n",
       "      <td>{'media': [{'id': 891087942176911360, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>20562</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>Mon Nov 16 00:24:50 +0000 2015</td>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 120]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 666049244999131136, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>111</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>Mon Nov 16 00:04:52 +0000 2015</td>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 137]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 666044217047650304, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>311</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>Sun Nov 15 23:21:54 +0000 2015</td>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 130]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 666033409081393153, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>128</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>Sun Nov 15 23:05:30 +0000 2015</td>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 139]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 666029276303482880, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>132</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>Sun Nov 15 22:32:08 +0000 2015</td>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 131]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 666020881337073664, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2535</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2353 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          created_at                  id              id_str  \\\n",
       "0     Tue Aug 01 00:17:27 +0000 2017  892177421306343426  892177421306343426   \n",
       "1     Mon Jul 31 00:18:03 +0000 2017  891815181378084864  891815181378084864   \n",
       "2     Sun Jul 30 15:58:51 +0000 2017  891689557279858688  891689557279858688   \n",
       "3     Sat Jul 29 16:00:24 +0000 2017  891327558926688256  891327558926688256   \n",
       "4     Sat Jul 29 00:08:17 +0000 2017  891087950875897856  891087950875897856   \n",
       "...                              ...                 ...                 ...   \n",
       "2348  Mon Nov 16 00:24:50 +0000 2015  666049248165822465  666049248165822465   \n",
       "2349  Mon Nov 16 00:04:52 +0000 2015  666044226329800704  666044226329800704   \n",
       "2350  Sun Nov 15 23:21:54 +0000 2015  666033412701032449  666033412701032449   \n",
       "2351  Sun Nov 15 23:05:30 +0000 2015  666029285002620928  666029285002620928   \n",
       "2352  Sun Nov 15 22:32:08 +0000 2015  666020888022790149  666020888022790149   \n",
       "\n",
       "                                              full_text  truncated  \\\n",
       "0     This is Tilly. She's just checking pup on you....      False   \n",
       "1     This is Archie. He is a rare Norwegian Pouncin...      False   \n",
       "2     This is Darla. She commenced a snooze mid meal...      False   \n",
       "3     This is Franklin. He would like you to stop ca...      False   \n",
       "4     Here we have a majestic great white breaching ...      False   \n",
       "...                                                 ...        ...   \n",
       "2348  Here we have a 1949 1st generation vulpix. Enj...      False   \n",
       "2349  This is a purebred Piers Morgan. Loves to Netf...      False   \n",
       "2350  Here is a very happy pup. Big fan of well-main...      False   \n",
       "2351  This is a western brown Mitsubishi terrier. Up...      False   \n",
       "2352  Here we have a Japanese Irish Setter. Lost eye...      False   \n",
       "\n",
       "     display_text_range                                           entities  \\\n",
       "0              [0, 138]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1              [0, 121]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2               [0, 79]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "3              [0, 138]  {'hashtags': [{'text': 'BarkWeek', 'indices': ...   \n",
       "4              [0, 138]  {'hashtags': [{'text': 'BarkWeek', 'indices': ...   \n",
       "...                 ...                                                ...   \n",
       "2348           [0, 120]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2349           [0, 137]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2350           [0, 130]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2351           [0, 139]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2352           [0, 131]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "\n",
       "                                      extended_entities  \\\n",
       "0     {'media': [{'id': 892177413194625024, 'id_str'...   \n",
       "1     {'media': [{'id': 891815175371796480, 'id_str'...   \n",
       "2     {'media': [{'id': 891689552724799489, 'id_str'...   \n",
       "3     {'media': [{'id': 891327551943041024, 'id_str'...   \n",
       "4     {'media': [{'id': 891087942176911360, 'id_str'...   \n",
       "...                                                 ...   \n",
       "2348  {'media': [{'id': 666049244999131136, 'id_str'...   \n",
       "2349  {'media': [{'id': 666044217047650304, 'id_str'...   \n",
       "2350  {'media': [{'id': 666033409081393153, 'id_str'...   \n",
       "2351  {'media': [{'id': 666029276303482880, 'id_str'...   \n",
       "2352  {'media': [{'id': 666020881337073664, 'id_str'...   \n",
       "\n",
       "                                                 source  \\\n",
       "0     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2348  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2349  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2350  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "      in_reply_to_status_id  ... favorite_count  favorited retweeted  \\\n",
       "0                       NaN  ...          33819      False     False   \n",
       "1                       NaN  ...          25461      False     False   \n",
       "2                       NaN  ...          42908      False     False   \n",
       "3                       NaN  ...          41048      False     False   \n",
       "4                       NaN  ...          20562      False     False   \n",
       "...                     ...  ...            ...        ...       ...   \n",
       "2348                    NaN  ...            111      False     False   \n",
       "2349                    NaN  ...            311      False     False   \n",
       "2350                    NaN  ...            128      False     False   \n",
       "2351                    NaN  ...            132      False     False   \n",
       "2352                    NaN  ...           2535      False     False   \n",
       "\n",
       "     possibly_sensitive possibly_sensitive_appealable lang retweeted_status  \\\n",
       "0                 False                         False   en              NaN   \n",
       "1                 False                         False   en              NaN   \n",
       "2                 False                         False   en              NaN   \n",
       "3                 False                         False   en              NaN   \n",
       "4                 False                         False   en              NaN   \n",
       "...                 ...                           ...  ...              ...   \n",
       "2348              False                         False   en              NaN   \n",
       "2349              False                         False   en              NaN   \n",
       "2350              False                         False   en              NaN   \n",
       "2351              False                         False   en              NaN   \n",
       "2352              False                         False   en              NaN   \n",
       "\n",
       "     quoted_status_id quoted_status_id_str  quoted_status  \n",
       "0                 NaN                  NaN            NaN  \n",
       "1                 NaN                  NaN            NaN  \n",
       "2                 NaN                  NaN            NaN  \n",
       "3                 NaN                  NaN            NaN  \n",
       "4                 NaN                  NaN            NaN  \n",
       "...               ...                  ...            ...  \n",
       "2348              NaN                  NaN            NaN  \n",
       "2349              NaN                  NaN            NaN  \n",
       "2350              NaN                  NaN            NaN  \n",
       "2351              NaN                  NaN            NaN  \n",
       "2352              NaN                  NaN            NaN  \n",
       "\n",
       "[2353 rows x 31 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 28,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## Assessing Data\n",
    "In this section, detect and document at least **eight (8) quality issues and two (2) tidiness issue**. You must use **both** visual assessment\n",
    "programmatic assessement to assess the data.\n",
    "\n",
    "**Note:** pay attention to the following key points when you access the data.\n",
    "\n",
    "* You only want original ratings (no retweets) that have images. Though there are 5000+ tweets in the dataset, not all are dog ratings and some are retweets.\n",
    "* Assessing and cleaning the entire dataset completely would require a lot of time, and is not necessary to practice and demonstrate your skills in data wrangling. Therefore, the requirements of this project are only to assess and clean at least 8 quality issues and at least 2 tidiness issues in this dataset.\n",
    "* The fact that the rating numerators are greater than the denominators does not need to be cleaned. This [unique rating system](http://knowyourmeme.com/memes/theyre-good-dogs-brent) is a big part of the popularity of WeRateDogs.\n",
    "* You do not need to gather the tweets beyond August 1st, 2017. You can, but note that you won't be able to gather the image predictions for these tweets since you don't have access to the algorithm used.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>1855</th>\n",
       "      <td>675531475945709568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-12 04:23:49 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Ellie AKA Queen Slayer of the Orbs. Ve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/675531475...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Ellie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>709225125749587968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14 03:50:21 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Walker. He's a Butternut Khalifa. Appe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/709225125...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Walker</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>781955203444699136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-30 20:33:43 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Chipson. He weighed in at .3 ounces an...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/781955203...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Chipson</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>727175381690781696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-05-02 16:38:15 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Karll. He just wants to go kayaking. 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/727175381...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Karll</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",
       "1855  675531475945709568                    NaN                  NaN   \n",
       "1272  709225125749587968                    NaN                  NaN   \n",
       "729   781955203444699136                    NaN                  NaN   \n",
       "1145  727175381690781696                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "1855  2015-12-12 04:23:49 +0000   \n",
       "1272  2016-03-14 03:50:21 +0000   \n",
       "729   2016-09-30 20:33:43 +0000   \n",
       "1145  2016-05-02 16:38:15 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "1855  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1272  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "729   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1145  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "1855  This is Ellie AKA Queen Slayer of the Orbs. Ve...                  NaN   \n",
       "1272  This is Walker. He's a Butternut Khalifa. Appe...                  NaN   \n",
       "729   This is Chipson. He weighed in at .3 ounces an...                  NaN   \n",
       "1145  This is Karll. He just wants to go kayaking. 1...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "1855                       NaN                        NaN   \n",
       "1272                       NaN                        NaN   \n",
       "729                        NaN                        NaN   \n",
       "1145                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "1855  https://twitter.com/dog_rates/status/675531475...                10   \n",
       "1272  https://twitter.com/dog_rates/status/709225125...                11   \n",
       "729   https://twitter.com/dog_rates/status/781955203...                11   \n",
       "1145  https://twitter.com/dog_rates/status/727175381...                10   \n",
       "\n",
       "      rating_denominator     name doggo floofer pupper puppo  \n",
       "1855                  10    Ellie  None    None   None  None  \n",
       "1272                  10   Walker  None    None   None  None  \n",
       "729                   10  Chipson  None    None   None  None  \n",
       "1145                  10    Karll  None    None   None  None  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TWITTER ARCHIVE\n",
    "# Show random sample of dataset for visual assessment\n",
    "\n",
    "twitter_archive.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if there is any duplicated tweet_id\n",
    "# ...No duplicate tweet_id\n",
    "\n",
    "twitter_archive[twitter_archive.tweet_id.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>created_at</th>\n",
       "      <th>id</th>\n",
       "      <th>id_str</th>\n",
       "      <th>full_text</th>\n",
       "      <th>truncated</th>\n",
       "      <th>display_text_range</th>\n",
       "      <th>entities</th>\n",
       "      <th>extended_entities</th>\n",
       "      <th>source</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>...</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>favorited</th>\n",
       "      <th>retweeted</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>possibly_sensitive_appealable</th>\n",
       "      <th>lang</th>\n",
       "      <th>retweeted_status</th>\n",
       "      <th>quoted_status_id</th>\n",
       "      <th>quoted_status_id_str</th>\n",
       "      <th>quoted_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tue Aug 01 00:17:27 +0000 2017</td>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 138]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 892177413194625024, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>33819</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mon Jul 31 00:18:03 +0000 2017</td>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 121]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 891815175371796480, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25461</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sun Jul 30 15:58:51 +0000 2017</td>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>False</td>\n",
       "      <td>[0, 79]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 891689552724799489, 'id_str'...</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>42908</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       created_at                  id              id_str  \\\n",
       "0  Tue Aug 01 00:17:27 +0000 2017  892177421306343426  892177421306343426   \n",
       "1  Mon Jul 31 00:18:03 +0000 2017  891815181378084864  891815181378084864   \n",
       "2  Sun Jul 30 15:58:51 +0000 2017  891689557279858688  891689557279858688   \n",
       "\n",
       "                                           full_text  truncated  \\\n",
       "0  This is Tilly. She's just checking pup on you....      False   \n",
       "1  This is Archie. He is a rare Norwegian Pouncin...      False   \n",
       "2  This is Darla. She commenced a snooze mid meal...      False   \n",
       "\n",
       "  display_text_range                                           entities  \\\n",
       "0           [0, 138]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1           [0, 121]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2            [0, 79]  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "\n",
       "                                   extended_entities  \\\n",
       "0  {'media': [{'id': 892177413194625024, 'id_str'...   \n",
       "1  {'media': [{'id': 891815175371796480, 'id_str'...   \n",
       "2  {'media': [{'id': 891689552724799489, 'id_str'...   \n",
       "\n",
       "                                              source  in_reply_to_status_id  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...                    NaN   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...                    NaN   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...                    NaN   \n",
       "\n",
       "   ... favorite_count  favorited retweeted possibly_sensitive  \\\n",
       "0  ...          33819      False     False              False   \n",
       "1  ...          25461      False     False              False   \n",
       "2  ...          42908      False     False              False   \n",
       "\n",
       "  possibly_sensitive_appealable lang retweeted_status quoted_status_id  \\\n",
       "0                         False   en              NaN              NaN   \n",
       "1                         False   en              NaN              NaN   \n",
       "2                         False   en              NaN              NaN   \n",
       "\n",
       "  quoted_status_id_str  quoted_status  \n",
       "0                  NaN            NaN  \n",
       "1                  NaN            NaN  \n",
       "2                  NaN            NaN  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TWEET JSON\n",
    "# Conduct visual assessment\n",
    "\n",
    "tweet_json.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2353 entries, 0 to 2352\n",
      "Data columns (total 31 columns):\n",
      " #   Column                         Non-Null Count  Dtype  \n",
      "---  ------                         --------------  -----  \n",
      " 0   created_at                     2353 non-null   object \n",
      " 1   id                             2353 non-null   int64  \n",
      " 2   id_str                         2353 non-null   object \n",
      " 3   full_text                      2353 non-null   object \n",
      " 4   truncated                      2353 non-null   bool   \n",
      " 5   display_text_range             2353 non-null   object \n",
      " 6   entities                       2353 non-null   object \n",
      " 7   extended_entities              2072 non-null   object \n",
      " 8   source                         2353 non-null   object \n",
      " 9   in_reply_to_status_id          78 non-null     float64\n",
      " 10  in_reply_to_status_id_str      78 non-null     object \n",
      " 11  in_reply_to_user_id            78 non-null     float64\n",
      " 12  in_reply_to_user_id_str        78 non-null     object \n",
      " 13  in_reply_to_screen_name        78 non-null     object \n",
      " 14  user                           2353 non-null   object \n",
      " 15  geo                            0 non-null      object \n",
      " 16  coordinates                    0 non-null      object \n",
      " 17  place                          1 non-null      object \n",
      " 18  contributors                   0 non-null      object \n",
      " 19  is_quote_status                2353 non-null   bool   \n",
      " 20  retweet_count                  2353 non-null   int64  \n",
      " 21  favorite_count                 2353 non-null   int64  \n",
      " 22  favorited                      2353 non-null   bool   \n",
      " 23  retweeted                      2353 non-null   bool   \n",
      " 24  possibly_sensitive             2210 non-null   object \n",
      " 25  possibly_sensitive_appealable  2210 non-null   object \n",
      " 26  lang                           2353 non-null   object \n",
      " 27  retweeted_status               179 non-null    object \n",
      " 28  quoted_status_id               29 non-null     float64\n",
      " 29  quoted_status_id_str           29 non-null     object \n",
      " 30  quoted_status                  28 non-null     object \n",
      "dtypes: bool(4), float64(3), int64(3), object(21)\n",
      "memory usage: 505.7+ KB\n"
     ]
    }
   ],
   "source": [
    "tweet_json.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As shown above, there are too many columns in the tweet_json table.\n",
    "# So we'll create a new dataframe -new_tweet_json- with the columns that we are likely to use.\n",
    "\n",
    "list_df = []\n",
    "\n",
    "with open('tweet_json.txt', 'r') as lines:\n",
    "    for line in lines:\n",
    "        data = json.loads(line)\n",
    "        tweet_id = data['id']\n",
    "        full_text = data['full_text']\n",
    "        favorite_count = data['favorite_count'] \n",
    "        retweet_count = data['retweet_count']\n",
    "        lang = data['lang']\n",
    "        \n",
    "        # appending to the list created\n",
    "        list_df.append({'tweet_id': tweet_id,\n",
    "                        'full_text': full_text,\n",
    "                        'favorite_count': favorite_count,\n",
    "                        'retweet_count': retweet_count,\n",
    "                       'lang': lang})\n",
    "        \n",
    "# Loading the tweet_json data into df_tweet_json        \n",
    "new_tweet_json = pd.DataFrame(list_df, columns= ['tweet_id', 'full_text', 'favorite_count', 'retweet_count', 'lang'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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>full_text</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>lang</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>857746408056729600</td>\n",
       "      <td>Meet Thor. He doesn't have finals because he's...</td>\n",
       "      <td>36021</td>\n",
       "      <td>11524</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612</th>\n",
       "      <td>685307451701334016</td>\n",
       "      <td>Say hello to Petrick. He's an Altostratus Floo...</td>\n",
       "      <td>2262</td>\n",
       "      <td>496</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>788178268662984705</td>\n",
       "      <td>Atlas is back and this time he's got doggles. ...</td>\n",
       "      <td>8100</td>\n",
       "      <td>2488</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>768596291618299904</td>\n",
       "      <td>Say hello to Oakley and Charlie. They're convi...</td>\n",
       "      <td>5592</td>\n",
       "      <td>1473</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          full_text  \\\n",
       "175   857746408056729600  Meet Thor. He doesn't have finals because he's...   \n",
       "1612  685307451701334016  Say hello to Petrick. He's an Altostratus Floo...   \n",
       "683   788178268662984705  Atlas is back and this time he's got doggles. ...   \n",
       "830   768596291618299904  Say hello to Oakley and Charlie. They're convi...   \n",
       "\n",
       "      favorite_count  retweet_count lang  \n",
       "175            36021          11524   en  \n",
       "1612            2262            496   en  \n",
       "683             8100           2488   en  \n",
       "830             5592           1473   en  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_tweet_json.sample(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   tweet_id        2354 non-null   int64 \n",
      " 1   full_text       2354 non-null   object\n",
      " 2   favorite_count  2354 non-null   int64 \n",
      " 3   retweet_count   2354 non-null   int64 \n",
      " 4   lang            2354 non-null   object\n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 92.1+ KB\n"
     ]
    }
   ],
   "source": [
    "new_tweet_json.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#IMAGE PREDICTIONS\n",
    "# Conduct visual assessment\n",
    "image_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "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.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, jpg_url, img_num, p1, p1_conf, p1_dog, p2, p2_conf, p2_dog, p3, p3_conf, p3_dog]\n",
       "Index: []"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No duplicate rows in tweet_id column of image_prediction table as well\n",
    "image_predictions[image_predictions.tweet_id.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.356000e+03</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>2356.000000</td>\n",
       "      <td>2356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.427716e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>7.720400e+17</td>\n",
       "      <td>1.241698e+16</td>\n",
       "      <td>13.126486</td>\n",
       "      <td>10.455433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.856705e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>6.236928e+16</td>\n",
       "      <td>9.599254e+16</td>\n",
       "      <td>45.876648</td>\n",
       "      <td>6.745237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>6.661041e+17</td>\n",
       "      <td>7.832140e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783989e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>7.186315e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.196279e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>7.804657e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.993373e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>8.203146e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>7.874618e+17</td>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "count  2.356000e+03           7.800000e+01         7.800000e+01   \n",
       "mean   7.427716e+17           7.455079e+17         2.014171e+16   \n",
       "std    6.856705e+16           7.582492e+16         1.252797e+17   \n",
       "min    6.660209e+17           6.658147e+17         1.185634e+07   \n",
       "25%    6.783989e+17           6.757419e+17         3.086374e+08   \n",
       "50%    7.196279e+17           7.038708e+17         4.196984e+09   \n",
       "75%    7.993373e+17           8.257804e+17         4.196984e+09   \n",
       "max    8.924206e+17           8.862664e+17         8.405479e+17   \n",
       "\n",
       "       retweeted_status_id  retweeted_status_user_id  rating_numerator  \\\n",
       "count         1.810000e+02              1.810000e+02       2356.000000   \n",
       "mean          7.720400e+17              1.241698e+16         13.126486   \n",
       "std           6.236928e+16              9.599254e+16         45.876648   \n",
       "min           6.661041e+17              7.832140e+05          0.000000   \n",
       "25%           7.186315e+17              4.196984e+09         10.000000   \n",
       "50%           7.804657e+17              4.196984e+09         11.000000   \n",
       "75%           8.203146e+17              4.196984e+09         12.000000   \n",
       "max           8.874740e+17              7.874618e+17       1776.000000   \n",
       "\n",
       "       rating_denominator  \n",
       "count         2356.000000  \n",
       "mean            10.455433  \n",
       "std              6.745237  \n",
       "min              0.000000  \n",
       "25%             10.000000  \n",
       "50%             10.000000  \n",
       "75%             10.000000  \n",
       "max            170.000000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# More overview/checks here show unusual values (outliers) for rating_numerator & rating_denominator with the max markedly greater than the respective means.\n",
    "\n",
    "twitter_archive.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for denominator not equals to 10 #twitter_archive\n",
    "denominator_check = twitter_archive[twitter_archive.rating_denominator != 10].rating_denominator\n",
    "denominator_check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "7    1\n",
       "2    1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The next two cells will group the data above into 2: values less than 10, and those greater than 10 to have a clarity about the values we're dealing with.\n",
    "\n",
    "# Values less than 10\n",
    "denominator_less = twitter_archive[twitter_archive.rating_denominator < 10].rating_denominator\n",
    "denominator_less.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50     3\n",
       "20     2\n",
       "80     2\n",
       "15     1\n",
       "70     1\n",
       "150    1\n",
       "170    1\n",
       "90     1\n",
       "40     1\n",
       "130    1\n",
       "110    1\n",
       "16     1\n",
       "120    1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ...values greater than 10\n",
    "denominator_more = twitter_archive[twitter_archive.rating_denominator > 13].rating_denominator\n",
    "denominator_more.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "420     2\n",
       "75      2\n",
       "99      1\n",
       "144     1\n",
       "26      1\n",
       "121     1\n",
       "143     1\n",
       "44      1\n",
       "60      1\n",
       "45      1\n",
       "80      1\n",
       "50      1\n",
       "666     1\n",
       "204     1\n",
       "1776    1\n",
       "165     1\n",
       "27      1\n",
       "24      1\n",
       "84      1\n",
       "960     1\n",
       "182     1\n",
       "88      1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For numerator, we'll watch out for values that are markedly greater than the column mean (approximately 13, with a cap at 20)\n",
    "numerator_check = twitter_archive[twitter_archive.rating_numerator > 20].rating_numerator\n",
    "numerator_check.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality issues\n",
    "\n",
    "\n",
    "_twitter archive_\n",
    " \n",
    "1. Retweet rows will not be needed.\n",
    "\n",
    "2. Too many nulls, (78/2356 for `in_reply_to_status_id` & `in_reply_to_user_id` ) as well as `retweeted_status_id` & `retweeted_status_user_id` (181/2356 each).\n",
    "   These columns, along with other columns that are not relevant to our analysis will be dropped.\n",
    "\n",
    "\n",
    "3. `Timestamp` is object datatype instead of datetime\n",
    "\n",
    "\n",
    "4. Some values in the `rating_denominator` columns do not fall within/conform to an acceptable range. \n",
    "23 instances of denominator having values less than or greater than 10.\n",
    "\n",
    "\n",
    "5. The columns `rating_numerator` and `rating_denominator` are rendered as object data type instead of integer. This will make it hard to run numerical operations on it if the need arises.\n",
    "\n",
    "6. Abnormal values in `rating numerator`.\n",
    "\n",
    "\n",
    "7. `Tweet_id` is in integer form in several tables. \n",
    "\n",
    "\n",
    " _image prediction_\n",
    " \n",
    "8. Column names are not very descriptive especially in the `image_prediction` dataset.\n",
    "\n",
    "\n",
    " _tweet json_\n",
    " \n",
    "9. `lang` column is object instead of category\n",
    "\n",
    "### Tidiness issues\n",
    "10. A (prospective) single columns `stages` spread across 4  columns (`doggo`, `floofer`, `pupper`, `puppo`)\n",
    "\n",
    "\n",
    "\n",
    "11. One unit is in separate tables (`image_predictions`, `tweet_json` and `twitter_archive`)  instead of one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 7,
        "hidden": false,
        "row": 40,
        "width": 12
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 32,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## Cleaning Data\n",
    "In this section, clean **all** of the issues you documented while assessing. \n",
    "\n",
    "**Note:** Make a copy of the original data before cleaning. Cleaning includes merging individual pieces of data according to the rules of [tidy data](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html). The result should be a high-quality and tidy master pandas DataFrame (or DataFrames, if appropriate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of original data\n",
    "\n",
    "copy_twitter_archive = twitter_archive.copy()\n",
    "copy_tweet_json = new_tweet_json.copy()\n",
    "copy_image_predictions = image_predictions.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## POSSIBLE ISSUES/ FRAMEWORKS\n",
    "\n",
    "\n",
    "### Quality\n",
    "_twitter archive_\n",
    " \n",
    "1. Retweet rows will not be needed.\n",
    "\n",
    "2. Too many nulls, (78/2356 for `in_reply_to_status_id` & `in_reply_to_user_id` ) as well as `retweeted_status_id` & `retweeted_status_user_id` (181/2356 each).\n",
    "   These columns, along with other columns that are not relevant to our analysis will be dropped.\n",
    "\n",
    "\n",
    "3. `Timestamp` is object datatype instead of datetime\n",
    "\n",
    "\n",
    "4. Some values in the `rating_denominator` columns do not fall within/conform to an acceptable range. \n",
    "23 instances of denominator having values less than or greater than 10.\n",
    "\n",
    "\n",
    "5. The columns `rating_numerator` and `rating_denominator` are rendered as object data type instead of integer. This will make it hard to run numerical operations on it if the need arises.\n",
    "\n",
    "\n",
    "6. Abnormal values in `rating numerator`.\n",
    "\n",
    "\n",
    "7. `Tweet_id` is in integer form in several tables. \n",
    "\n",
    "\n",
    " _image prediction_\n",
    " \n",
    "8. Column names are not very descriptive especially in the `image_prediction` dataset.\n",
    "\n",
    "\n",
    " _tweet json_\n",
    " \n",
    "9. `lang` column is object instead of category\n",
    "\n",
    "### Tidiness\n",
    "10. A (prospective) single columns `stages` spread across 4  columns (`doggo`, `floofer`, `pupper`, `puppo`)\n",
    "\n",
    "\n",
    "11. One unit is in separate tables (`image_predictions`, `tweet_json` and `twitter_archive`)  instead of one.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QUALITY ISSUES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. \n",
    "\n",
    "\n",
    "Retweet rows will not be needed ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Define:__\n",
    "\n",
    "\n",
    "Drop rows that are not actual tweets, but retweets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Code:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#identify retweets and pass it into a variable.\n",
    "retweet_rows = twitter_archive[twitter_archive.retweeted_status_id.notnull()].index.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#permanently drop rows that are retweets.\n",
    "twitter_archive.drop(retweet_rows, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Test:__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if they're still rows with retweet data...\n",
    "twitter_archive[twitter_archive.retweeted_status_id.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 2. \n",
    "\n",
    "\n",
    "Too many nulls, (78/2356 for in_reply_to_status_id & in_reply_to_user_id ) as well as retweeted_status_id & retweeted_status_user_id (181/2356 each). These columns, along with other columns that are not relevant to our analysis will be dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Drop columns (irrelevant/unused) from the twitter archive table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Round up the columns to be dropped into a list\n",
    "list_array = ['in_reply_to_status_id', 'in_reply_to_user_id', 'source', 'retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp', 'expanded_urls']\n",
    "\n",
    "clean_twitter_archive = copy_twitter_archive.drop(list_array, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>753039830821511168</td>\n",
       "      <td>2016-07-13 01:34:21 +0000</td>\n",
       "      <td>So this just changed my life. 13/10 please enj...</td>\n",
       "      <td>13</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>868</th>\n",
       "      <td>761750502866649088</td>\n",
       "      <td>2016-08-06 02:27:27 +0000</td>\n",
       "      <td>RT @dog_rates: \"Tristan do not speak to me wit...</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>81</th>\n",
       "      <td>877201837425926144</td>\n",
       "      <td>2017-06-20 16:29:50 +0000</td>\n",
       "      <td>This is Nelly. He graduated with his dogtorate...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Nelly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>761004547850530816</td>\n",
       "      <td>2016-08-04 01:03:17 +0000</td>\n",
       "      <td>This is Bo and Ty. Bo eats paper and Ty felt l...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Bo</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1776</th>\n",
       "      <td>677961670166224897</td>\n",
       "      <td>2015-12-18 21:20:32 +0000</td>\n",
       "      <td>This is Izzy. She's showing off the dance move...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Izzy</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                  timestamp  \\\n",
       "939   753039830821511168  2016-07-13 01:34:21 +0000   \n",
       "868   761750502866649088  2016-08-06 02:27:27 +0000   \n",
       "81    877201837425926144  2017-06-20 16:29:50 +0000   \n",
       "876   761004547850530816  2016-08-04 01:03:17 +0000   \n",
       "1776  677961670166224897  2015-12-18 21:20:32 +0000   \n",
       "\n",
       "                                                   text  rating_numerator  \\\n",
       "939   So this just changed my life. 13/10 please enj...                13   \n",
       "868   RT @dog_rates: \"Tristan do not speak to me wit...                10   \n",
       "81    This is Nelly. He graduated with his dogtorate...                12   \n",
       "876   This is Bo and Ty. Bo eats paper and Ty felt l...                11   \n",
       "1776  This is Izzy. She's showing off the dance move...                11   \n",
       "\n",
       "      rating_denominator   name doggo floofer pupper puppo  \n",
       "939                   10   None  None    None   None  None  \n",
       "868                   10   None  None    None   None  None  \n",
       "81                    10  Nelly  None    None   None  None  \n",
       "876                   10     Bo  None    None   None  None  \n",
       "1776                  10   Izzy  None    None   None  None  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if the column drop has been effected\n",
    "clean_twitter_archive.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.\n",
    "\n",
    " Timestamp is object datatype instead of datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Convert `timestamp` column datatype to datetime object using pandas `to_datetime` method "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_twitter_archive['timestamp'] = pd.to_datetime(clean_twitter_archive['timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime64[ns, UTC]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the datatype of the timestamp column\n",
    "clean_twitter_archive['timestamp'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2356 non-null   int64              \n",
      " 1   timestamp           2356 non-null   datetime64[ns, UTC]\n",
      " 2   text                2356 non-null   object             \n",
      " 3   rating_numerator    2356 non-null   int64              \n",
      " 4   rating_denominator  2356 non-null   int64              \n",
      " 5   name                2356 non-null   object             \n",
      " 6   doggo               2356 non-null   object             \n",
      " 7   floofer             2356 non-null   object             \n",
      " 8   pupper              2356 non-null   object             \n",
      " 9   puppo               2356 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), int64(3), object(6)\n",
      "memory usage: 184.2+ KB\n"
     ]
    }
   ],
   "source": [
    "clean_twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.\n",
    "\n",
    "\n",
    "Some values in the `rating_denominator` columns do not fall within/conform to an acceptable range. \n",
    "\n",
    "- 23 instances of denominator having values less than or greater than 10 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "- Replace odd denominator values with 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50     3\n",
       "11     3\n",
       "80     2\n",
       "20     2\n",
       "120    1\n",
       "16     1\n",
       "110    1\n",
       "130    1\n",
       "40     1\n",
       "0      1\n",
       "90     1\n",
       "15     1\n",
       "170    1\n",
       "150    1\n",
       "7      1\n",
       "70     1\n",
       "2      1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "denominator_check = clean_twitter_archive[clean_twitter_archive.rating_denominator != 10].rating_denominator\n",
    "denominator_check.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary for values gotten from denominator_check variable, then replace values will nulls\n",
    "dict_denomm = {0, 2, 7, 11, 15, 16, 20, 40, 50, 70, 80, 90, 110, 120, 130, 150, 170}\n",
    "clean_twitter_archive.rating_denominator.replace(dict_denomm, np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for nulls...\n",
    "sum(clean_twitter_archive.rating_denominator.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace nan with constant denominator, 10.\n",
    "nan_fill = {np.nan: '10'}\n",
    "clean_twitter_archive.replace({\"rating_denominator\": nan_fill}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nulls replaced with 10. No nulls left\n",
    "sum(clean_twitter_archive.rating_denominator.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2356 non-null   int64              \n",
      " 1   timestamp           2356 non-null   datetime64[ns, UTC]\n",
      " 2   text                2356 non-null   object             \n",
      " 3   rating_numerator    2356 non-null   int64              \n",
      " 4   rating_denominator  2356 non-null   object             \n",
      " 5   name                2356 non-null   object             \n",
      " 6   doggo               2356 non-null   object             \n",
      " 7   floofer             2356 non-null   object             \n",
      " 8   pupper              2356 non-null   object             \n",
      " 9   puppo               2356 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), int64(2), object(7)\n",
      "memory usage: 184.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#Just noticed, rating denominator and numerator are not integers. We'll change that next.\n",
    "clean_twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. \n",
    "\n",
    "\n",
    "The columns `rating_numerator` and `rating_denominator` are rendered as object data type instead of integer. This will make it hard to run numerical operations on it if the need arises."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define:\n",
    "Use the `.astype()` method to convert object to integer data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_twitter_archive[['rating_numerator']] = clean_twitter_archive[['rating_numerator']].astype('int64')\n",
    "clean_twitter_archive[['rating_denominator']] = clean_twitter_archive[['rating_denominator']].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2356 non-null   int64              \n",
      " 1   timestamp           2356 non-null   datetime64[ns, UTC]\n",
      " 2   text                2356 non-null   object             \n",
      " 3   rating_numerator    2356 non-null   int64              \n",
      " 4   rating_denominator  2356 non-null   int64              \n",
      " 5   name                2356 non-null   object             \n",
      " 6   doggo               2356 non-null   object             \n",
      " 7   floofer             2356 non-null   object             \n",
      " 8   pupper              2356 non-null   object             \n",
      " 9   puppo               2356 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), int64(3), object(6)\n",
      "memory usage: 184.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#the rating_numerator and rating_denominator have been converted to integers.\n",
    "clean_twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6.\n",
    "\n",
    "\n",
    "Abnormal values in rating numerator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Use Numpy to replace values (adjuged to be outliers) with the mean __(culled from the df.describe output)__ of the column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "420     2\n",
       "75      2\n",
       "99      1\n",
       "144     1\n",
       "26      1\n",
       "121     1\n",
       "143     1\n",
       "44      1\n",
       "60      1\n",
       "45      1\n",
       "80      1\n",
       "50      1\n",
       "666     1\n",
       "204     1\n",
       "1776    1\n",
       "165     1\n",
       "27      1\n",
       "24      1\n",
       "84      1\n",
       "960     1\n",
       "182     1\n",
       "88      1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write code into variable, numerator_check, to filter out values considered outliers.\n",
    "numerator_check = clean_twitter_archive[clean_twitter_archive.rating_numerator > 20].rating_numerator\n",
    "numerator_check.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary containing outlier values for numerator_ratings.\n",
    "dict_numerator = {2, 1}\n",
    "clean_twitter_archive.rating_numerator.replace(dict_numerator, np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(clean_twitter_archive.rating_numerator.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace nan with the column mean of the raring_numerator column, 13(approximately).\n",
    "nan_fill = {np.nan: '13'}\n",
    "clean_twitter_archive.replace({\"rating_numerator\": nan_fill}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_twitter_archive[['rating_numerator']] = clean_twitter_archive[['rating_numerator']].astype('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nulls replaced with 13. No nulls left\n",
    "sum(clean_twitter_archive.rating_numerator.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. \n",
    "\n",
    "\n",
    "Tweet_id is in integer form in several tables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Covert `tweet_id` to string data type (as we won't be conducting numerical operations on it)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the numbers that make the tweet_id are not going to be used for calculation or rendering any statistical inference, then it makes sense to store it as a string.\n",
    "clean_twitter_archive[['tweet_id']] = clean_twitter_archive[['tweet_id']].astype('str')\n",
    "new_tweet_json[['tweet_id']] = new_tweet_json[['tweet_id']].astype('str')\n",
    "copy_image_predictions[['tweet_id']] = copy_image_predictions[['tweet_id']].astype('str')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2356 non-null   object             \n",
      " 1   timestamp           2356 non-null   datetime64[ns, UTC]\n",
      " 2   text                2356 non-null   object             \n",
      " 3   rating_numerator    2356 non-null   int64              \n",
      " 4   rating_denominator  2356 non-null   int64              \n",
      " 5   name                2356 non-null   object             \n",
      " 6   doggo               2356 non-null   object             \n",
      " 7   floofer             2356 non-null   object             \n",
      " 8   pupper              2356 non-null   object             \n",
      " 9   puppo               2356 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), int64(2), object(7)\n",
      "memory usage: 184.2+ KB\n"
     ]
    }
   ],
   "source": [
    "clean_twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. \n",
    "\n",
    "\n",
    "Column names are not very descriptive especially in the image_prediction dataset.\n",
    "\n",
    "**#image_prediction table**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Use the rename function to change some column names to more descriptive ones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_predictions.rename(index = int, columns={\"jpg_url\" : \"image_url\"}, inplace = True)\n",
    "image_predictions.rename(index = int, columns={\"p1_conf\" : \"p1_confidence\"}, inplace = True)\n",
    "image_predictions.rename(index = int, columns={\"p2_conf\" : \"p2_confidence\"}, inplace = True)\n",
    "image_predictions.rename(index = int, columns={\"p3_conf\" : \"p3_confidence\"}, inplace = True)\n",
    "new_tweet_json.rename(index = int, columns={\"full_text\" : \"text\"}, inplace = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "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>tweet_id</th>\n",
       "      <th>image_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_confidence</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_confidence</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_confidence</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2070</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2075 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                        image_url  \\\n",
       "0     666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1     666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2     666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3     666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4     666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "...                  ...                                              ...   \n",
       "2070  891327558926688256  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "2071  891689557279858688  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "2072  891815181378084864  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "2073  892177421306343426  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2074  892420643555336193  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num                      p1  p1_confidence  p1_dog  \\\n",
       "0           1  Welsh_springer_spaniel       0.465074    True   \n",
       "1           1                 redbone       0.506826    True   \n",
       "2           1         German_shepherd       0.596461    True   \n",
       "3           1     Rhodesian_ridgeback       0.408143    True   \n",
       "4           1      miniature_pinscher       0.560311    True   \n",
       "...       ...                     ...            ...     ...   \n",
       "2070        2                  basset       0.555712    True   \n",
       "2071        1             paper_towel       0.170278   False   \n",
       "2072        1               Chihuahua       0.716012    True   \n",
       "2073        1               Chihuahua       0.323581    True   \n",
       "2074        1                  orange       0.097049   False   \n",
       "\n",
       "                      p2  p2_confidence  p2_dog                           p3  \\\n",
       "0                 collie       0.156665    True            Shetland_sheepdog   \n",
       "1     miniature_pinscher       0.074192    True          Rhodesian_ridgeback   \n",
       "2               malinois       0.138584    True                   bloodhound   \n",
       "3                redbone       0.360687    True           miniature_pinscher   \n",
       "4             Rottweiler       0.243682    True                     Doberman   \n",
       "...                  ...            ...     ...                          ...   \n",
       "2070    English_springer       0.225770    True  German_short-haired_pointer   \n",
       "2071  Labrador_retriever       0.168086    True                      spatula   \n",
       "2072            malamute       0.078253    True                       kelpie   \n",
       "2073            Pekinese       0.090647    True                     papillon   \n",
       "2074               bagel       0.085851   False                       banana   \n",
       "\n",
       "      p3_confidence  p3_dog  \n",
       "0          0.061428    True  \n",
       "1          0.072010    True  \n",
       "2          0.116197    True  \n",
       "3          0.222752    True  \n",
       "4          0.154629    True  \n",
       "...             ...     ...  \n",
       "2070       0.175219    True  \n",
       "2071       0.040836   False  \n",
       "2072       0.031379    True  \n",
       "2073       0.068957    True  \n",
       "2074       0.076110   False  \n",
       "\n",
       "[2075 rows x 12 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. \n",
    "\n",
    "\n",
    "`lang` column is object instead of a category datatype\n",
    "\n",
    "**#tweet_json table**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Use the .astype() method to chang the `lang` column data type from object to category datatype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_tweet_json[['lang']] = new_tweet_json[['lang']].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2354 entries, 0 to 2353\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype   \n",
      "---  ------          --------------  -----   \n",
      " 0   tweet_id        2354 non-null   object  \n",
      " 1   text            2354 non-null   object  \n",
      " 2   favorite_count  2354 non-null   int64   \n",
      " 3   retweet_count   2354 non-null   int64   \n",
      " 4   lang            2354 non-null   category\n",
      "dtypes: category(1), int64(2), object(2)\n",
      "memory usage: 94.6+ KB\n"
     ]
    }
   ],
   "source": [
    "new_tweet_json.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## TIDINESS ISSUES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10. \n",
    "\n",
    "- A (prospective) single columns `stages` spread across 4  columns (`doggo`, `floofer`, `pupper`, `puppo`)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "- Concatenate the 4 columns into a new column `stages`.\n",
    "- Drop the now redundant 4 columns\n",
    "- Modify data where necessary/relevant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoneNoneNoneNone        1976\n",
       "NoneNonepupperNone       245\n",
       "doggoNoneNoneNone         83\n",
       "NoneNoneNonepuppo         29\n",
       "doggoNonepupperNone       12\n",
       "NoneflooferNoneNone        9\n",
       "doggoNoneNonepuppo         1\n",
       "doggoflooferNoneNone       1\n",
       "Name: stage, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatenate the four columns\n",
    "clean_twitter_archive['stage'] = clean_twitter_archive.doggo + clean_twitter_archive.floofer + clean_twitter_archive.pupper + clean_twitter_archive.puppo\n",
    "\n",
    "clean_twitter_archive['stage'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop redundant columns\n",
    "d_stages = ['doggo', 'floofer', 'pupper', 'puppo']\n",
    "clean_twitter_archive.drop(d_stages, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NoneNoneNoneNone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NoneNoneNoneNone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>NoneNoneNoneNone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>NoneNoneNoneNone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>NoneNoneNoneNone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>2015-11-16 00:24:50+00:00</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>NoneNoneNoneNone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>2015-11-16 00:04:52+00:00</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NoneNoneNoneNone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>2015-11-15 23:21:54+00:00</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NoneNoneNoneNone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>2015-11-15 23:05:30+00:00</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>NoneNoneNoneNone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08+00:00</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>NoneNoneNoneNone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2356 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                 timestamp  \\\n",
       "0     892420643555336193 2017-08-01 16:23:56+00:00   \n",
       "1     892177421306343426 2017-08-01 00:17:27+00:00   \n",
       "2     891815181378084864 2017-07-31 00:18:03+00:00   \n",
       "3     891689557279858688 2017-07-30 15:58:51+00:00   \n",
       "4     891327558926688256 2017-07-29 16:00:24+00:00   \n",
       "...                  ...                       ...   \n",
       "2351  666049248165822465 2015-11-16 00:24:50+00:00   \n",
       "2352  666044226329800704 2015-11-16 00:04:52+00:00   \n",
       "2353  666033412701032449 2015-11-15 23:21:54+00:00   \n",
       "2354  666029285002620928 2015-11-15 23:05:30+00:00   \n",
       "2355  666020888022790149 2015-11-15 22:32:08+00:00   \n",
       "\n",
       "                                                   text  rating_numerator  \\\n",
       "0     This is Phineas. He's a mystical boy. Only eve...                13   \n",
       "1     This is Tilly. She's just checking pup on you....                13   \n",
       "2     This is Archie. He is a rare Norwegian Pouncin...                12   \n",
       "3     This is Darla. She commenced a snooze mid meal...                13   \n",
       "4     This is Franklin. He would like you to stop ca...                12   \n",
       "...                                                 ...               ...   \n",
       "2351  Here we have a 1949 1st generation vulpix. Enj...                 5   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                 6   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                 9   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                 7   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                 8   \n",
       "\n",
       "      rating_denominator      name             stage  \n",
       "0                     10   Phineas  NoneNoneNoneNone  \n",
       "1                     10     Tilly  NoneNoneNoneNone  \n",
       "2                     10    Archie  NoneNoneNoneNone  \n",
       "3                     10     Darla  NoneNoneNoneNone  \n",
       "4                     10  Franklin  NoneNoneNoneNone  \n",
       "...                  ...       ...               ...  \n",
       "2351                  10      None  NoneNoneNoneNone  \n",
       "2352                  10         a  NoneNoneNoneNone  \n",
       "2353                  10         a  NoneNoneNoneNone  \n",
       "2354                  10         a  NoneNoneNoneNone  \n",
       "2355                  10      None  NoneNoneNoneNone  \n",
       "\n",
       "[2356 rows x 7 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_twitter_archive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                1976\n",
       "pupper           245\n",
       "doggo             83\n",
       "puppo             29\n",
       "doggopupper       12\n",
       "floofer            9\n",
       "doggopuppo         1\n",
       "doggofloofer       1\n",
       "Name: stage, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#modify and strip instances of string, 'None'.\n",
    "clean_twitter_archive['stage'] = clean_twitter_archive['stage'].map(lambda find:\n",
    "find.replace(\"None\", \"\"))\n",
    "\n",
    "clean_twitter_archive['stage'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                  1976\n",
       "pupper             245\n",
       "doggo               83\n",
       "puppo               29\n",
       "doggo, pupper       12\n",
       "floofer              9\n",
       "doggo, puppo         1\n",
       "doggo, floofer       1\n",
       "Name: stage, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It was stated in dataset description that it is possible to have a dog exhibit \n",
    "# characteristics of two dog stages. These instances will be separeted using a comma.\n",
    "\n",
    "clean_twitter_archive.loc[clean_twitter_archive.stage == 'doggopupper', 'stage'] = 'doggo, pupper'\n",
    "clean_twitter_archive.loc[clean_twitter_archive.stage == 'doggopuppo', 'stage'] = 'doggo, puppo'\n",
    "clean_twitter_archive.loc[clean_twitter_archive.stage == 'doggofloofer', 'stage'] = 'doggo, floofer'\n",
    "\n",
    "clean_twitter_archive['stage'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['', 'doggo', 'puppo', 'pupper', 'floofer', 'doggo, puppo',\n",
       "       'doggo, floofer', 'doggo, pupper'], dtype=object)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check components of the column\n",
    "pd.unique(clean_twitter_archive['stage'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "11. \n",
    "\n",
    "\n",
    "- One unit is in separate tables (`image_predictions`, `tweet_json` and `twitter_archive`)  instead of one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge clean_twitter_archive and new_tweet_json, call new variable 'merge_1'\n",
    "clean_twitter_archive = pd.merge(clean_twitter_archive , new_tweet_json,\n",
    "                            on=['tweet_id'], how='inner')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2354 entries, 0 to 2353\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2354 non-null   object             \n",
      " 1   timestamp           2354 non-null   datetime64[ns, UTC]\n",
      " 2   text_x              2354 non-null   object             \n",
      " 3   rating_numerator    2354 non-null   int64              \n",
      " 4   rating_denominator  2354 non-null   int64              \n",
      " 5   name                2354 non-null   object             \n",
      " 6   stage               2354 non-null   object             \n",
      " 7   text_y              2354 non-null   object             \n",
      " 8   favorite_count      2354 non-null   int64              \n",
      " 9   retweet_count       2354 non-null   int64              \n",
      " 10  lang                2354 non-null   category           \n",
      "dtypes: category(1), datetime64[ns, UTC](1), int64(4), object(5)\n",
      "memory usage: 205.0+ KB\n"
     ]
    }
   ],
   "source": [
    "clean_twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#next, add image prediction table to the merge to form a tentatative 'master.csv' file called twitter_archive_master.\n",
    "\n",
    "#briefly render tweet_id as 'int' to facilitate merge\n",
    "clean_twitter_archive[['tweet_id']] = clean_twitter_archive[['tweet_id']].astype('int64')\n",
    "twitter_archive_master = pd.merge(clean_twitter_archive , image_predictions,\n",
    "                            on=['tweet_id'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2354 entries, 0 to 2353\n",
      "Data columns (total 22 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2354 non-null   int64              \n",
      " 1   timestamp           2354 non-null   datetime64[ns, UTC]\n",
      " 2   text_x              2354 non-null   object             \n",
      " 3   rating_numerator    2354 non-null   int64              \n",
      " 4   rating_denominator  2354 non-null   int64              \n",
      " 5   name                2354 non-null   object             \n",
      " 6   stage               2354 non-null   object             \n",
      " 7   text_y              2354 non-null   object             \n",
      " 8   favorite_count      2354 non-null   int64              \n",
      " 9   retweet_count       2354 non-null   int64              \n",
      " 10  lang                2354 non-null   category           \n",
      " 11  image_url           2073 non-null   object             \n",
      " 12  img_num             2073 non-null   float64            \n",
      " 13  p1                  2073 non-null   object             \n",
      " 14  p1_confidence       2073 non-null   float64            \n",
      " 15  p1_dog              2073 non-null   object             \n",
      " 16  p2                  2073 non-null   object             \n",
      " 17  p2_confidence       2073 non-null   float64            \n",
      " 18  p2_dog              2073 non-null   object             \n",
      " 19  p3                  2073 non-null   object             \n",
      " 20  p3_confidence       2073 non-null   float64            \n",
      " 21  p3_dog              2073 non-null   object             \n",
      "dtypes: category(1), datetime64[ns, UTC](1), float64(4), int64(5), object(11)\n",
      "memory usage: 407.3+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_archive_master.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text_x</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>stage</th>\n",
       "      <th>text_y</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_confidence</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_confidence</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_confidence</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td></td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>39467</td>\n",
       "      <td>8853</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td></td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>33819</td>\n",
       "      <td>6514</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td></td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>25461</td>\n",
       "      <td>4328</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td></td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>42908</td>\n",
       "      <td>8964</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td></td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>41048</td>\n",
       "      <td>9774</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                 timestamp  \\\n",
       "0  892420643555336193 2017-08-01 16:23:56+00:00   \n",
       "1  892177421306343426 2017-08-01 00:17:27+00:00   \n",
       "2  891815181378084864 2017-07-31 00:18:03+00:00   \n",
       "3  891689557279858688 2017-07-30 15:58:51+00:00   \n",
       "4  891327558926688256 2017-07-29 16:00:24+00:00   \n",
       "\n",
       "                                              text_x  rating_numerator  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                13   \n",
       "1  This is Tilly. She's just checking pup on you....                13   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                12   \n",
       "3  This is Darla. She commenced a snooze mid meal...                13   \n",
       "4  This is Franklin. He would like you to stop ca...                12   \n",
       "\n",
       "   rating_denominator      name stage  \\\n",
       "0                  10   Phineas         \n",
       "1                  10     Tilly         \n",
       "2                  10    Archie         \n",
       "3                  10     Darla         \n",
       "4                  10  Franklin         \n",
       "\n",
       "                                              text_y  favorite_count  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...           39467   \n",
       "1  This is Tilly. She's just checking pup on you....           33819   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...           25461   \n",
       "3  This is Darla. She commenced a snooze mid meal...           42908   \n",
       "4  This is Franklin. He would like you to stop ca...           41048   \n",
       "\n",
       "   retweet_count  ... img_num           p1  p1_confidence p1_dog  \\\n",
       "0           8853  ...     1.0       orange       0.097049  False   \n",
       "1           6514  ...     1.0    Chihuahua       0.323581   True   \n",
       "2           4328  ...     1.0    Chihuahua       0.716012   True   \n",
       "3           8964  ...     1.0  paper_towel       0.170278  False   \n",
       "4           9774  ...     2.0       basset       0.555712   True   \n",
       "\n",
       "                   p2 p2_confidence p2_dog                           p3  \\\n",
       "0               bagel      0.085851  False                       banana   \n",
       "1            Pekinese      0.090647   True                     papillon   \n",
       "2            malamute      0.078253   True                       kelpie   \n",
       "3  Labrador_retriever      0.168086   True                      spatula   \n",
       "4    English_springer      0.225770   True  German_short-haired_pointer   \n",
       "\n",
       "  p3_confidence p3_dog  \n",
       "0      0.076110  False  \n",
       "1      0.068957   True  \n",
       "2      0.031379   True  \n",
       "3      0.040836  False  \n",
       "4      0.175219   True  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive_master.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trim final dataframe\n",
    "- Drop columns/rows.\n",
    "- Rename columns.\n",
    "- Select appropriate datatypes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop columns\n",
    "trim = ['image_url', 'p2', 'p2_confidence', 'p2_dog', 'p3', 'p3_confidence', 'p3_dog']\n",
    "twitter_archive_master = twitter_archive_master.drop(trim, axis=1)\n",
    "\n",
    "#twitter_archive_master.drop(\"stage\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename columns\n",
    "twitter_archive_master.rename(index = int, columns={\"img_num\" : \"number_of_images\"}, inplace = True)\n",
    "twitter_archive_master.rename(index = int, columns={\"p1\" : \"dog_breed\"}, inplace = True)\n",
    "twitter_archive_master.rename(index = int, columns={\"p1_confidence\" : \"prediction_confidence\"}, inplace = True)\n",
    "twitter_archive_master.rename(index = int, columns={\"p1_dog\" : \"is_dog\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop rows\n",
    "twitter_archive_master[\"is_dog\"].replace(False, np.nan, inplace=True)\n",
    "twitter_archive_master.is_dog.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select appropriate datatypes\n",
    "twitter_archive_master['timestamp'] = pd.to_datetime(twitter_archive_master['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2354 entries, 0 to 2353\n",
      "Data columns (total 15 columns):\n",
      " #   Column                 Non-Null Count  Dtype              \n",
      "---  ------                 --------------  -----              \n",
      " 0   tweet_id               2354 non-null   int64              \n",
      " 1   timestamp              2354 non-null   datetime64[ns, UTC]\n",
      " 2   text_x                 2354 non-null   object             \n",
      " 3   rating_numerator       2354 non-null   int64              \n",
      " 4   rating_denominator     2354 non-null   int64              \n",
      " 5   name                   2354 non-null   object             \n",
      " 6   stage                  2354 non-null   object             \n",
      " 7   text_y                 2354 non-null   object             \n",
      " 8   favorite_count         2354 non-null   int64              \n",
      " 9   retweet_count          2354 non-null   int64              \n",
      " 10  lang                   2354 non-null   category           \n",
      " 11  number_of_images       2073 non-null   float64            \n",
      " 12  dog_breed              2073 non-null   object             \n",
      " 13  prediction_confidence  2073 non-null   float64            \n",
      " 14  is_dog                 1531 non-null   object             \n",
      "dtypes: category(1), datetime64[ns, UTC](1), float64(2), int64(5), object(6)\n",
      "memory usage: 278.5+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_archive_master.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing Data\n",
    "Save gathered, assessed, and cleaned master dataset to a CSV file named \"twitter_archive_master.csv\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive_master.to_csv('twitter_archive_master.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing and Visualizing Data\n",
    "In this section, analyze and visualize your wrangled data. You must produce at least **three (3) insights and one (1) visualization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:\n",
    "1. __Which day of the week tend to have the highest tweet engagement?__\n",
    "> Across retweet counts and favorite counts analysis, Wednesdays appear to see the highest activities on the page. Monday and Tuesday follows closely. This will suggest that Mondays through Wednesdays are the the periods with high engagements. The twitter page can therefore act on this information by either encouraging followers to post during this period, or other find other ways leveraging on this information to drive engagements.\n",
    "\n",
    "2. __Which dog breed appears most (i.e is most popular) in the WeRateDog twitter page?__\n",
    "> Golden retriever appear to be the most popular dog breed among followers of the page, since it is the most frequently posted breed by users.\n",
    "\n",
    "3. __Which dog stages tend to rack up high number of retweets?__\n",
    "> Dogs that fall within the doggo stage appear to be popular amongst followers, as they have the highest average retweets of the other dog stages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight 1:\n",
    "Which day of the week tend to have the highest tweet engagement?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use the argument '%A' in string formatter method '.strftime()' to return days of the week (as strings), and then pass it into a column, `day_of_the_weeek`.\n",
    "twitter_archive_master[\"day_of_the_week\"] = twitter_archive_master[\"timestamp\"].dt.strftime(\"%A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\NELSON\\AppData\\Local\\Temp/ipykernel_9596/1286942292.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  archive_master_insight= twitter_archive_master.groupby([\"day_of_the_week\"], as_index=False) [\"favorite_count\", \"retweet_count\"].sum()\n"
     ]
    },
    {
     "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>day_of_the_week</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sunday</td>\n",
       "      <td>2357196</td>\n",
       "      <td>864866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Saturday</td>\n",
       "      <td>2477944</td>\n",
       "      <td>991830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>2508595</td>\n",
       "      <td>960236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Friday</td>\n",
       "      <td>2588285</td>\n",
       "      <td>1030952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2846694</td>\n",
       "      <td>1179251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>3063019</td>\n",
       "      <td>1116120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wednesday</td>\n",
       "      <td>3180867</td>\n",
       "      <td>1306678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  day_of_the_week  favorite_count  retweet_count\n",
       "3          Sunday         2357196         864866\n",
       "2        Saturday         2477944         991830\n",
       "4        Thursday         2508595         960236\n",
       "0          Friday         2588285        1030952\n",
       "5         Tuesday         2846694        1179251\n",
       "1          Monday         3063019        1116120\n",
       "6       Wednesday         3180867        1306678"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#then group day_of_the_week by favorite count and retweet count.\n",
    "archive_master_insight= twitter_archive_master.groupby([\"day_of_the_week\"], as_index=False) [\"favorite_count\", \"retweet_count\"].sum()\n",
    "\n",
    "#test (with favorite_count)\n",
    "archive_master_insight.sort_values(by=[\"favorite_count\"]).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define a function to create plots that show relationship between days of the week and tweet engagements (favorite counts and retweet counts)\n",
    "def twt_engagement(engagement_type, day, order):\n",
    "    input_ = archive_master_insight.sort_values(by=[\"favorite_count\"]).head(10)\n",
    "    viz = sns.barplot(x=engagement_type, y=day, data=input_, orient='h', color='blue')\n",
    "    title=plt.title(\"Tweet Interaction Days Ranking\")\n",
    "    Y=plt.ylabel(\"{}\".format(day))\n",
    "    X=plt.xlabel(\"{}\".format(engagement_type))\n",
    "    return input_, viz, title, Y, X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(  day_of_the_week  favorite_count  retweet_count\n",
       " 3          Sunday         2357196         864866\n",
       " 2        Saturday         2477944         991830\n",
       " 4        Thursday         2508595         960236\n",
       " 0          Friday         2588285        1030952\n",
       " 5         Tuesday         2846694        1179251\n",
       " 1          Monday         3063019        1116120\n",
       " 6       Wednesday         3180867        1306678,\n",
       " <AxesSubplot:title={'center':'Tweet Interaction Days Ranking'}, xlabel='favorite_count', ylabel='day_of_the_week'>,\n",
       " Text(0.5, 1.0, 'Tweet Interaction Days Ranking'),\n",
       " Text(0, 0.5, 'day_of_the_week'),\n",
       " Text(0.5, 0, 'favorite_count'))"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for favorite counts\n",
    "twt_engagement('favorite_count', 'day_of_the_week', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(  day_of_the_week  favorite_count  retweet_count\n",
       " 3          Sunday         2357196         864866\n",
       " 2        Saturday         2477944         991830\n",
       " 4        Thursday         2508595         960236\n",
       " 0          Friday         2588285        1030952\n",
       " 5         Tuesday         2846694        1179251\n",
       " 1          Monday         3063019        1116120\n",
       " 6       Wednesday         3180867        1306678,\n",
       " <AxesSubplot:title={'center':'Tweet Interaction Days Ranking'}, xlabel='retweet_count', ylabel='day_of_the_week'>,\n",
       " Text(0.5, 1.0, 'Tweet Interaction Days Ranking'),\n",
       " Text(0, 0.5, 'day_of_the_week'),\n",
       " Text(0.5, 0, 'retweet_count'))"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAawAAAEXCAYAAADxxXAaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAoL0lEQVR4nO3de7hUZfn/8fdHhA0GgoKZmLrJ8wFEQFPzgGYHTTPT0qIS8oqszPzaOa3wq6V+s6wsf4bkATNPmYWZilkIoZIbBTZqJgaeS1FRPKSB9++P9WxZDvsww57ZMws+r+uaa6/Ds551P7Nn5p5nrWfWUkRgZmbW6NardwBmZmblcMIyM7NCcMIyM7NCcMIyM7NCcMIyM7NCcMIyM7NCcMIyW8tI2lfSA/WOo6gkLZF0UAfr7pU0tmcjsjZOWFY3kl7MPV6X9EpuflyN993hh1JaP1bSY2XW1SwpJK1fvQjLl/a9Tdt8RMyKiO1rsJ+2drb9j/4t6Q+S3lPtfZURS0h6KcXxuKQfSepV6/1GxM4RMaPW+7H2OWFZ3URE/7YH8AhwWG7Z5fWOr6fUK9F1w6D0P9sVuAW4TtL4OsSxa4pjf+Bo4NN1iMF6kBOWNRRJfVNPa0iaP1XSCkkbpvkzJP04TTdJOkfSI+nb/gWS+uXqOlTSPEnLJN0uaURafhmwJXB9+ob+tTLimiHpdEmzJS2XNL0tRmBm+rss1bdX2ubTku6X9JykmyVtlasvJH1B0oPAg2nZTyQ9KukFSXMl7Zsr30vStyQ9lPY/V9IWktr2PT/t++jS3qGkHVP8y9IhrQ/m1l0i6eeSbkj1zpG0dTn/q4j4V0T8BJgEnC1pvVTnN3Jx3ifpiNz/61lJw3P7f2v6f28iaUjqsS1L5Wa11dlFHIuA2cDIXL2dPZeTJF0taWqK8V5JY9qrW9IOkhZLOibNv9Ez76oeSaMk3ZPWXSPpKklnlPPcWgciwg8/6v4AlgAHpemZwJFpejrwEHBwbt0RafrHwDRgY2AAcD1wZlo3CngKeCfQCzg27aOpdH8dxDMWeCw3PyPFsR3QL82fldY1AwGsnyv/IWARsCOwPnAqcHtufZD1TjYG+qVlnwAGp/JfBv4F9E3rvgq0AtsDIuvdDM7VtU17sQO9UxzfAvoABwLLge3T+kuAZ4E90n4vB67s4DlZrZ1p+TvS8h3T/EeAoWRfiI8GXgI2S+vOB87Obfsl4Po0fSZwQYq5N7AvoA5ieaPNwA7Ak8D/5NZ39lxOAv4DHJJeG2cCd5a+FsleQ48Ah3bwOu2wnvRcP5za1xv4MPAacEa932tFftQ9AD/8iFjtg+B04Kfpw+Zf6U1/FtAXeAUYkj60XwK2ztWxF7A4Tf8/4PSSfTwA7F+6vw7ieeNDP83PAE7NzX8euClNr/ZBDtwIHJebXw94GdgqzQdwYBfPyXNkh73aYj+8g3KdJax903O4Xm79FcCkNH0JMCW37hDg7x3sZ7V2puV90/J3dbDdvLbYyb5APNoWD9ACfDRN/y/w+3xbOnluAnghvQYitampzOdyEvCn3LqdgFdKXounAY8BB3TyOu2wHmA/4HFyCRf4K05Y3Xr4kKA1otvIPnRHkfUqbiE7T7EnsCgilgKbABsAc9MhpGXATWk5wFbAl9vWpfVbkH3zX1P/yk2/DPTvpOxWwE9y+36WLMlunivzaH4DSV9OhxCfT9sMJEvOpNgfWoOYhwKPRsTruWUPl8RRSbva01bXswCSPpU7FLsM2IXUjoiYQ5Zk9pe0A7ANWS8Z4AdkvcHpkv4p6Rtd7HdUivVoskT4lrYVXTyXsHqb++rN5xKPJ+sR/6WLGDqqZyjweKRMlbzp/22Vc8KyRnQ72aGvI4DbIuI+snNOHyBLZgBLyXpbO0fEoPQYGNlJeMg+HL6XWzcoIjaIiCvS+mrepqC9uh4FPluy/34RcXt726VzLF8HPgpsFBGDgOfJklxbfWWdWyrxBLBFybmgLcm+/VfLEWSHXx9I5+kuBE4gO2Q5CFjIqnYAXEp2yO6TwG8i4j8AEbE8Ir4cEe8ADgNOlvTuznYcmauBO4DvQFnPZTmOB7aUdG4F2+Q9CWwuKb/PLdawLkucsKzhRMTLwFzgC6xKULcDn22bTz2GC4FzJb0VQNLmkt6Xyl8IHC/pncq8RdIHJA1I6/9Ndu6lGp4GXi+p7wLgm5J2TrENlPSRTuoYAKxIda0v6TvAhrn1U4DTJW2b2jNC0uAy2tLWo/mapN7KfkN0GHBlJQ1sj6RNJZ0AfBf4ZvqfvIUsET+dykwg62HlXUaW5D4BTM3Vd6ikbdKH/AvAyvQox1nARElvo+vnshzLgfcD+0k6q8JtIUugK4ETJK0v6XCy84TWDU5Y1qhuIztZ/bfc/ABWjciD7Fv0IuBOSS8AfyLrmRERLcBngJ+Rnb9YBIzPbXsmcGo6bPWV7gSaEuz3gNmpvj0j4jrgbODKFNtC4OBOqrmZ7LzXP8gO2f2HNx9C+hFwNdkglBeAX5IN/oDsXMqlad8fLYntNeCDad9LyQY9fCoi/r7mLWaZpJfIDtceAnwkIi5K+7sP+CHZB/a/geFkI/jyMT0G3E2W2GblVm1L9j98MW1/fpT5m6eIaCV7jXyVrp/LskTEMuA9wMGSTq9w29fIBlocBywjS85/AF6tNA5bRW8+xGpmVnuSLgKeiIhT6x1LT5E0B7ggIi6udyxFVbQfLJpZwUlqJut97FbnUGpK0v5kozuXAuOAEWQDg2wN+ZCgmfWYdGhtIfCDiFhc73hqbHtgPtmAjy8DR0XEk/UNqdh8SNDMzArBPSwzMysEJywzMysED7qooSFDhkRzc3O9wzAzK5S5c+cujYhNSpc7YdVQc3MzLS0t9Q7DzKxQJD3c3nIfEjQzs0JwD6uGWltfY9iwJfUOw6zwFi9urncI1gDcwzIzs0JwwjIzs0JwwjIzs0JwwjIzs0JwwjIzs0JwwjIzs0JwwjIzs0JwwjIzs0IoXMKSdIqkeyUtkDRP0jurUOek7t4m3czMaqtQV7qQtBdwKDAqIl6VNAToU+ewzMysBxSth7UZsDQiXgWIiKUR8YSkJSl5IWmMpBlpepKkiyTNkPRPSSe2VZR6ag9I+hPZnUHbln9G0l2S5ku6VtIGkgZIWiypdyqzYdpn7x5su5nZOq1oCWs6sIWkf0g6X9L+ZWyzA/A+YA/gu5J6SxoNHAPsBnwY2D1X/rcRsXtE7ArcDxwXEcuBGcAHUpljgGsj4r+lO5M0UVKLpJaVK59Zw2aamVmpQiWsiHgRGA1MBJ4GrpI0vovNboiIVyNiKfAUsCmwL3BdRLwcES8A03Lld5E0S1IrMA7YOS2fAkxI0xOAizuIcXJEjImIMb16Da68kWZm1q5CncMCiIiVZL2dGSmpHAusYFXy7Vuyyau56ZWsanN0sItLgA9FxPyUDMem/c6W1Jx6db0iYmH3WmJmZpUoVA9L0vaSts0tGgk8DCwh63kBHFlGVTOBIyT1kzQAOCy3bgDwZDo/Na5ku6nAFXTQuzIzs9opWg+rP3CepEFkvapFZIcHdwR+KelbwJyuKomIuyVdBcwjS3izcqu/nep4GGglS2BtLgfOIEtaZmbWgxTR0ZExKyXpKODwiPhkOeWbmkbE0KHTui5oZp3yDRzXLZLmRsSY0uVF62HVjaTzgIOBQ+odi5nZusgJq0wR8cV6x2Bmti4r1KALMzNbdzlhmZlZIThhmZlZIThhmZlZIThhmZlZIXiUYA0NH96HlpbmeodhZrZWcA/LzMwKwQnLzMwKwQnLzMwKwQnLzMwKwQnLzMwKwaMEa6i19TWGDVtS7zDMrBt8pfjG4R6WmZkVghOWmZkVghOWmZkVghOWmZkVghOWmZkVghOWmZkVghOWmZkVghOWmZkVQkMmLEmnSLpX0gJJ8yS9s5Oy4yUNrcI+l0ga0t16zMysNhruSheS9gIOBUZFxKspifTpZJPxwELgiQr2sX5ErOhWoGZm1qMasYe1GbA0Il4FiIilEfGEpO9IukvSQkmTlTkKGANcnnpi/fI9JUljJM1I05PSdtOBqZIGS5ou6R5JvwDUFoCk30mam3p5E9Oy4ySdmyvzGUk/6qknxcxsXdeICWs6sIWkf0g6X9L+afnPImL3iNgF6AccGhG/AVqAcRExMiJe6aLu0cDhEfFx4LvAXyNiN2AasGWu3KcjYjRZMjxR0mDgSuCDknqnMhOAi6vQXjMzK0PDJayIeJEssUwEngaukjQeOEDSHEmtwIHAzmtQ/bRcUtsP+FXa5w3Ac7lyJ0qaD9wJbAFsGxEvAX8GDpW0A9A7IlpLdyBpoqQWSS0rVz6zBiGamVl7Gu4cFkBErARmADNSgvosMAIYExGPSpoE9O1g8xWsSsSlZV4q3VXpxpLGAgcBe0XEy+mQYls9U4BvAX+ng95VREwGJgM0NY1YrX4zM1szDdfDkrS9pG1zi0YCD6TppZL6A0fl1i8HBuTml5D10ACO7GRXM4FxaZ8HAxul5QOB51Ky2gHYs22DiJhD1uP6OHBF+a0yM7PuasQeVn/gPEmDyHpLi8gODy4DWskS0l258pcAF0h6BdgLOA34paRvAXM62c9pwBWS7gZuAx5Jy28Cjpe0gCxR3lmy3dXAyIh4DjMz6zGK8FGrSkj6A3BuRNzaVdmmphExdOi0HojKzGrFN3DseZLmRsSY0uUNd0iwUUkaJOkfwCvlJCszM6uuRjwk2JAiYhmwXb3jMDNbV7mHZWZmheCEZWZmheCEZWZmheCEZWZmheCEZWZmheBRgjU0fHgfWlqa6x2GmdlawT0sMzMrBCcsMzMrBCcsMzMrBCcsMzMrBCcsMzMrBI8SrKHW1tcYNmxJvcMws074auzF4R6WmZkVghOWmZkVQtkJS9LG7SwbVt1wzMzM2ldJD+t6SRu2zUjaCbi++iGZmZmtrpKE9X2ypNVf0mjgGuATtQnLzMzszcoeJRgRN0jqDUwHBgAfiogHaxaZmZlZTpcJS9J5QOQWbQj8E/iiJCLixFoFZ2Zm1qacHlZLyfzcWgRiZmbWmS4TVkRc2jYtqR+wZUQ8UM0gJA0Gbk2zbwNWAk8DzcATEbFTNffXzv7HA2Mi4oRa7sfMzNZcJcPaDwPmATel+ZGSplUjiIh4JiJGRsRI4ALg3DQ9Enh9TeuV5Ct5mJmtJSoZJTgJ2ANYBhAR84Ce+B1WL0kXSrpX0vTUy0PSDElj0vQQSUvS9HhJ10i6HpguaTNJMyXNk7RQ0r6p3ARJ/5B0G/Cutp1JOkzSHEn3SPqTpE0lrSfpQUmbpDLrSVokaUgPtN/MzKgsYa2IiOdLlkW7JatrW+DnEbEzWbI8soxt9gKOjYgDgY8DN6ce267APEmbAaeRJar3APlDjn8F9oyI3YArga9FxOvAr4BxqcxBwPyIWNrNtpmZWZkqOWS2UNLHyXo82wInArfXJqw3WZx6c5AN+GguY5tbIuLZNH0XcFEakv+7iJgn6d3AjIh4GkDSVcB2qfzbgatSUusDLE7LLwJ+D/wY+DRwcXs7ljQRmAjQq9fQMptoZmZdqaSH9UVgZ+BV4NfA88BJNYip1Ku56ZWsSrIrWBV/35JtXmqbiIiZwH7A48Blkj7VtqqD/Z0H/CwihgOfbas7Ih4F/i3pQOCdwI3tbRwRkyNiTESM6dVrcBnNMzOzcpSdsCLi5Yg4BRgbEbtHxKkR8Z8axtaVJcDoNH1UR4UkbQU8FREXAr8ERgFzgLGSBqee10dymwwkS24Ax5ZUN4Xs0ODVEbGy2y0wM7OyVTJKcG9J9wH3p/ldJZ1fs8i6dg7wOUm3A50NfhhLdt7qHrLzXz+JiCfJBpHcAfwJuDtXfhJwjaRZQOk5qmlAfzo4HGhmZrWjiPLGTUiaQ9aTmZYGJCBpYUTsUsP4GkoalXhuROxbTvmmphExdGhVRv6bWY34Bo6NR9LciBhTuryi3ylFxKOS8ovWmcNikr4BfI5VIwXNzKwHVTLo4lFJewMhqY+kr5AOD64LIuKsiNgqIv5a71jMzNZFlSSs44EvAJsDj5FdheILNYjJzMxsNZUcEnwxInw4zMzM6qLSHw7/G5gFzARmt3PlCzMzs5qo5HdY2wAfA1qBQ4H5kubVKC4zM7M3KbuHJentZNfe25fsmnz3kl13z8zMrOYqOST4CNl1+b4fEcfXKJ61yvDhfWhpaa53GGZma4VKRgnuBkwFPi7pDklTJR1Xo7jMzMzepOweVkTMl/QQ8BDZYcFPkF1U9pc1is3MzOwNlZzDagGayG4p8ldgv4h4uFaBmZmZ5VVyDuvgtvtHtUfSsRFxaRViMjMzW00lw9o7TFbJl7oZi5mZWYcquvhtF9R1kXVLa+trDBu2pN5hmFkP8FXfa6+SUYJdKe8+JWZmZmugmgnLPSwzM6uZaias2VWsy8zM7E3KTliSNpX0S0k3pvmd8j8cjogTahGgmZkZVNbDugS4GRia5v8BnFTleMzMzNpVScIaEhFXA68DRMQKYGVNojIzMytRScJ6SdJg0mhASXsCvh+WmZn1iEp+h3UyMA3YWtJsYBPgqJpEZWZmVqKSi9/eLWl/YHuyIewPRMR/axbZGpC0kuwGk20+FBFLSsr8Efh4RCwrWT4JeDEizqlxmGZmtgYqvdLFHkBz2m6UJCJiatWjWnOvRMTI9lZIEqCIOKRnQzIzs2qoZFj7ZcA5wD7A7ukxpkZxVYWkZkn3SzofuBvYQtISSUPS+lMkPSDpT2Q9x7btPiPpLknzJV0raQNJAyQtltQ7ldkw1dW7Lo0zM1vHVNLDGgPsFBGNfAmmfpLmpenFwP+QJaIJEfF5gKyjBZJGA8eQ3ZhyfbKENjdt+9uIuDCVOwM4LiLOkzQD+ADwu7TttY12WNTMbG1VScJaCLwNeLJGsVTDmw4JSmoGHo6IO9spuy9wXUS8nMpOy63bJSWqQUB/st+fAUwBvkaWsCYAnymtVNJEYCJAr15DS1ebmdka6jJhSbqebCj7AOA+SX8DXm1bHxEfrF14VfFSJ+s66i1eQjZgY76k8cBYgIiYnQ4z7g/0ioiFq1UYMRmYDNDUNKKRe6NmZoVSTg9rbR01NxO4RNJZZM/DYcAv0roBwJPp/NQ44PHcdlOBK4DTezBWM7N1XpcJKyJuA5B0dkR8Pb9O0tnAbTWKrabSMP2rgHnAw8Cs3OpvA3PS8layBNbmcuAMsqRlZmY9ROWOoZB0d0SMKlm2ICJG1CSyBiXpKODwiPhkV2WbmkbE0KHTuipmZmsB38CxeiTNjYjVRqGXcw7rc8DngXdIWpBbNYB17JYiks4DDgb8Wy4zsx5WzjmsXwM3AmcC38gtXx4Rz7bNSNooIp6rcnwNJSK+WO8YzMzWVeWcw3qe7CK3H+ui6K3AqC7KmJmZrZFq3nFYVazLzMzsTaqZsPybIzMzq5lqJiwzM7Oa6TJhSRpWZl0+JGhmZjVTzijB3wCjJd0aEe/upFxn69ZJw4f3oaWlud5hmJmtFcpJWOtJ+i6wnaSTS1dGxI/S32dX29LMzKxKyjmHdQzwH7LkNqCdh5mZWc2V8zusB4Cz02WYbuyBmMzMzFZTySjB2yX9SFJLevxQ0sCaRWZmZpZTScK6CFgOfDQ9XgAurkVQZmZmpSq54/DWEXFkbv603O3orR2tra8xbNiSeodhZuugtfHq8ZX0sF6RtE/bjKR3Aa9UPyQzM7PVVdLDOh6Ymjtv9RxwbPVDMjMzW13ZCSsi5gO7Stowzb+QXy/p2Ii4tMrxmZmZAWtwLcGIeKE0WSVfqkI8ZmZm7fLtRczMrBB8exEzMysE97DMzKwQyk5Yknp1UWR2N2MxMzPrUCU9rEWSfiBpp/ZWRsQJVYrpDZIGS5qXHv+S9Hhuvk8V9zNW0h+qVZ+ZmVVfJb/DGkF25fYpktYju1TTlR2MGKyKiHgGGAkgaRLwYkScU6v9mZlZ4yq7hxURyyPiwojYG/ga8F3gSUmXStqmZhGWkHSJpKNy8y/mpr8q6S5JCySdlpa9RdINkuZLWijp6LT8/ZL+LumvwIdzdewh6XZJ96S/26flsySNzJWbLWlE7VtsZmZQQQ8rncP6ADABaAZ+CFwO7Av8EdiuBvGVTdJ7gW2BPcgGgEyTtB+wCfBERHwglRsoqS9wIXAgsAi4KlfV34H9ImKFpIOA7wNHAlOA8cBJkrYDmiJiQY80zszMKjqH9SBwOPCDiNgtIn4UEf+OiN8AN9UmvIq8Nz3uAe4GdiBLYK3AQZLOlrRvRDyf1i2OiAcjIoBf5eoZCFwjaSFwLrBzWn4NcKik3sCngUvaC0LSxLZbsKxc+UzVG2lmtq6q6BxWRLzY3oqIOLFK8ZRjBSnRShLQNvhCwJkR8YvSDSSNBg4BzpQ0HZhGx78bOx34S0QcIakZmAEQES9LuoUsaX8UGNPexhExGZgM0NQ0wr9NMzOrkkoS1gpJXyDrcfRtWxgRn656VJ1bAowGriZLHr3T8puB0yVdHhEvStoc+C9ZG5+NiF+l813jgf8DhknaOiIeAj6Wq38g8HiaHl+y7ynA9cCsiHi22g0zM7OOVXJI8DLgbcD7gNuAt5Pd0LGnXQjsL+lvwDuBlwAiYjrwa+AOSa3Ab4ABwHDgb+neXacAZ0TEf4CJwA1p0MXDufr/j6wnNht402/PImIuvnGlmVldKDuFU0ZB6Z6I2E3SgogYkc7l3BwRB9Y2xMYhaSjZIcIdIuL1rso3NY2IoUOn1TwuM7NSRb6Bo6S5EbHaaZdKelj/TX+XSdqF7NBZcxViKwRJnwLmAKeUk6zMzKy6KjmHNVnSRsCpZIMW+gPfrklUDSgipgJT6x2Hmdm6qsuEJenk3OyE9Pfn6e9bqh6RmZlZO8rpYQ1If7cHdifrXQEcBsysRVBmZmalukxYEdF2iaPpwKiIWJ7mJ5H9mNbMzKzmKhl0sSXwWm7+NdahQRdmZlZflQy6uIzs90zXkV0l4gjg0ppEZWZmVqLshBUR35N0I9nFbgEmRMQ9tQlr7TB8eB9aWprrHYaZ2Vqhkh4WEXE32YVlzczMelQl57DMzMzqxgnLzMwKwQnLzMwKwQnLzMwKoaJBF1aZ1tbXGDZsSb3DMLMCKvLV1mvFPSwzMysEJywzMysEJywzMysEJywzMysEJywzMysEJywzMysEJywzMysEJywzMyuEwiQsSSHpstz8+pKelvSHKtU/SdJXqlGXmZlVX2ESFvASsIukfmn+PcDjdYzHzMx6UJESFsCNwAfS9MeAK9pWSNpY0u8kLZB0p6QRafkkSRdJmiHpn5JOzG1ziqQHJP0J2D63/DOS7pI0X9K1kjaQNEDSYkm9U5kNJS1pmzczs9oqWsK6EjhGUl9gBDAnt+404J6IGAF8C5iaW7cD8D5gD+C7knpLGg0cA+wGfBjYPVf+txGxe0TsCtwPHBcRy4EZrEqYxwDXRsR/q9xGMzNrR6ESVkQsAJrJeld/LFm9D3BZKvdnYLCkgWndDRHxakQsBZ4CNgX2Ba6LiJcj4gVgWq6uXSTNktQKjAN2TsunABPS9ATg4tIYJU2U1CKpZeXKZ7rXYDMze0OhElYyDTiH3OHARO2UjfT31dyylay6Sn3QvkuAEyJiOFnPrS9ARMwGmiXtD/SKiIWr7TBickSMiYgxvXoNLqM5ZmZWjiImrIuA/42I1pLlM8l6Q0gaCyxNPaeOzASOkNRP0gDgsNy6AcCT6fzUuJLtppIly9V6V2ZmVjuFux9WRDwG/KSdVZOAiyUtAF4Gju2inrslXQXMAx4GZuVWf5vs/NjDQCtZAmtzOXAGq/fwzMyshhTR0VExa4+ko4DDI+KTXZVtahoRQ4dO66qYmdlq1uUbOEqaGxFjSpcXrodVT5LOAw4GDql3LGZm6xonrApExBfrHYOZ2bqqiIMuzMxsHeSEZWZmheCEZWZmheCEZWZmheCEZWZmheBRgjU0fHgfWlqa6x2GmdlawT0sMzMrBCcsMzMrBCcsMzMrBCcsMzMrBCcsMzMrBI8SrKHW1tcYNmxJvcMwM+tRtbrSvHtYZmZWCE5YZmZWCE5YZmZWCE5YZmZWCE5YZmZWCE5YZmZWCE5YZmZWCE5YZmZWCDVLWJLOlXRSbv5mSVNy8z+UdHIZ9TRLWlijMNv28WIt6zczs+6rZQ/rdmBvAEnrAUOAnXPr9wZm13D/Zma2FqllwppNSlhkiWohsFzSRpKagB0BJN0maW7qgW2Wlo2WNF/SHcAX2iqUNF7SbyXdJOlBSf+XW/deSXdIulvSNZL6p+VnSbpP0gJJ56Rlw1LZuySdnqujv6RbUx2tkg5Py0+X9KVcue9JOrE2T5uZmbWnZgkrIp4AVkjakixx3QHMAfYCxgD3A+cCR0XEaOAi4Htp84uBEyNir3aqHgkcDQwHjpa0haQhwKnAQRExCmgBTpa0MXAEsHNEjADOSHX8BPh/EbE78K9c3f8Bjkh1HAD8UJKAXwLHwhu9xWOAy7vz/JiZWWVqffHbtl7W3sCPgM3T9PPA48B7gVuynEAv4ElJA4FBEXFbquMy4OBcnbdGxPMAku4DtgIGATsBs1NdfcgS5AtkSWiKpBuAP6Q63gUcmav/7DQt4PuS9gNeT/FuGhFLJD0jaTdgU+CeiHimvQZLmghMBOjVa2glz5WZmXWi1gmr7TzWcLJDgo8CXyZLJH8GNi/tRUkaBEQndb6am15J1gYBt0TEx0oLS9oDeDdZr+gE4MC0qr19jAM2AUZHxH8lLQH6pnVTgPHA28h6g+2KiMnAZICmphGdtcPMzCpQ62Hts4FDgWcjYmVEPEvWG9oLuArYRNJeAJJ6S9o5IpYBz0vaJ9Uxroz93Am8S9I2qa4NJG2XzmMNjIg/AieRHU5si+uYduofCDyVktUBZL23NtcB7wd2B24us/1mZlYlte5htZKNDvx1ybL+EfGUpKOAn6bDgOsDPwbuBSYAF0l6mTKSQ0Q8LWk8cEUa0AHZOa3lwO8l9SXrhf1PWvcl4NdpIMW1uaouB66X1ALMA/6e28drkv4CLIuIleU/BWZmVg2K8FGrcqTBFncDH4mIB8vZpqlpRAwdOq22gZmZNZju3sBR0tyIGFO63Fe6KIOknYBFZAM+ykpWZmZWXbU+JLhWiIj7gHfUOw4zs3WZe1hmZlYITlhmZlYITlhmZlYITlhmZlYITlhmZlYIHiVYQ8OH96GlpbneYZiZrRXcwzIzs0JwwjIzs0LwpZlqSNJy4IF6x9FNQ4Cl9Q6im4rehqLHD25DoyhKG7aKiE1KF/ocVm090N71sIpEUovbUF9Fjx/chkZR9Db4kKCZmRWCE5aZmRWCE1ZtTa53AFXgNtRf0eMHt6FRFLoNHnRhZmaF4B6WmZkVghOWmZkVghNWFUh6v6QHJC2S9I121kvST9P6BZJG1SPOjpQR/7gU9wJJt0vatR5xdqarNuTK7S5ppaSjejK+cpTTBkljJc2TdK+k23o6xq6U8VoaKOl6SfNTGybUI86OSLpI0lOSFnawvqHfy1BWGxr+/dyhiPCjGw+gF/AQ2R2J+wDzgZ1KyhwC3AgI2BOYU++4K4x/b2CjNH1wI8Vfbhty5f4M/BE4qt5xr8H/YRBwH7Blmn9rveNegzZ8Czg7TW8CPAv0qXfsufj2A0YBCztY37Dv5Qra0NDv584e7mF13x7Aooj4Z0S8BlwJHF5S5nBgamTuBAZJ2qynA+1Al/FHxO0R8VyavRN4ew/H2JVy/gcAXwSuBZ7qyeDKVE4bPg78NiIeAYiIRmtHOW0IYIAkAf3JEtaKng2zYxExkyymjjTyexnoug0FeD93yAmr+zYHHs3NP5aWVVqmXiqN7Tiyb5iNpMs2SNocOAK4oAfjqkQ5/4ftgI0kzZA0V9Kneiy68pTThp8BOwJPAK3AlyLi9Z4Jryoa+b28Jhrx/dwhX5qp+9TOstLfCpRTpl7Kjk3SAWQv8H1qGlHlymnDj4GvR8TK7Mt9wymnDesDo4F3A/2AOyTdGRH/qHVwZSqnDe8D5gEHAlsDt0iaFREv1Di2amnk93JFGvj93CEnrO57DNgiN/92sm+PlZapl7JikzQCmAIcHBHP9FBs5SqnDWOAK1OyGgIcImlFRPyuRyLsWrmvo6UR8RLwkqSZwK5AoySsctowATgrshMoiyQtBnYA/tYzIXZbI7+Xy9bg7+cO+ZBg990FbCtpmKQ+wDHAtJIy04BPpRFGewLPR8STPR1oB7qMX9KWwG+BTzbQt/m8LtsQEcMiojkimoHfAJ9voGQF5b2Ofg/sK2l9SRsA7wTu7+E4O1NOGx4h6yEiaVNge+CfPRpl9zTye7ksBXg/d8g9rG6KiBWSTgBuJhsldVFE3Cvp+LT+ArJRaYcAi4CXyb5lNoQy4/8OMBg4P/VQVkQDXfG5zDY0tHLaEBH3S7oJWAC8DkyJiHaHLtdDmf+H04FLJLWSHV77ekQ0zO0uJF0BjAWGSHoM+C7QGxr/vdymjDY09Pu5M740k5mZFYIPCZqZWSE4YZmZWSE4YZmZWSE4YZmZWSE4YZk1GEmDJH2+xvs4KQ2NrxtJ4yUNrWcMVl1dXXi3nfIflXRfuhDyr7sq74RlVifptzztvQcHATVNWMBJQF0TFjAecMJau1wCvL+cgpK2Bb4JvCsidiZ7TXbKCcusB0lqlnS/pPOBu4FvS7or3erhtFTsLGDrdBuRH0g6X9IH0/bXSbooTR8n6Yw0/QlJf0vb/EJSr7T8vZLukHS3pGsk9Zd0Ilmi+Iukv3QS6/vTdvMl3ZqWbSzpdyneO9MVE5A0SdJXctsuTG1ta++F6Vv0dEn9lN3eZQxweYq5X3WfaauH9i68K2lrSTcpu/7lLEk7pFWfAX7ediHeci7m7IRl1vO2B6YCXye7cOoewEhgtKT9gG8AD0XEyIj4KjAT2DdtuzmwU5reB5glaUfgaLJvqiOBlcA4SUOAU4GDImIU0AKcHBE/Jbuc0AERcUB7AUraBLgQODIidgU+kladBtwTESPIbhUytYz2bkv2wbQzsCzV+ZsUz7jUzlfKqMeKaTLwxYgYDXwFOD8t3w7YTtLs9OWny56Zr3Rh1vMejog7JZ0DvBe4Jy3vT/bh/khJ+VnASZJ2Irsf1kbKbmmxF3AicCzZRXHvSlcu6Ed2C5U9yZLb7LS8D3BHmTHuCcyMiMUAEdH2rXkf4Mi07M+SBksa2EVdiyNiXpqeCzSXGYMVnKT+ZPffukarLjrdlP6uT/Z6H0t2TcZZknaJiGUd1eeEZdbzXkp/BZwZEb/Ir5TUnJ+PiMclbUR2bmAmsDHwUeDFiFiu7JPg0oj4Zkk9hwG3RMTH1iBG0f5VyDu6WvkK3nzEpm9u+tXc9EqyhGrrhvWAZannX+ox4M6I+C+wWNIDZAnsrs4qM7P6uBn4dPoWiqTNJb0VWA4MKCl7B9lJ6ZlkPa6vpL8AtwJHpW3bzjNtRXZzvndJ2iYt30DSdmmb9vZRur/9JQ1rqzMtnwmMS8vGkl09/gVgCdldblF22/hhZbS/qxis4NJrY7Gkj8AbA412Tat/BxyQlg8hO0TY6YWQnbDM6iQipgO/JruvVSvZVeQHpNs9zE4DF36Qis8C1o+IRWSDNTZOy4iI+8jOVU2XtAC4BdgsIp4mG4l3RVp+J9mtPCA7r3BjR4Mu0rYTgd9Kmg9clVZNAsak+s4iOxwJ2Z2cN5Y0D/gc5d3y5BLgAg+6WHsou/DuHcD2kh6TdBzZF5zj0uvoXlbdhfpm4BlJ9wF/Ab7a1a1OfPFbMzMrBPewzMysEDzowmwdJ2kOq0ZutflkRLTWIx6zjviQoJmZFYIPCZqZWSE4YZmZWSE4YZmZWSE4YZmZWSE4YZmZWSE4YZmZWSH8f79U8hX1k+LwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for retweet cuonts...\n",
    "twt_engagement('retweet_count', 'day_of_the_week', False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> __Across retweet counts and favorite counts analysis, Wednesdays appear to see the highest activities on the page. Monday and Tuesday follows closely. This will suggest that Mondays through Wednesdays are the the periods with high engagements. The twitter page can therefore act on this information by either encouraging followers to post during this period, or other find other ways leveraging on this information to drive engagements.__\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight 2:\n",
    "Which dog breed appears most (i.e is most popular) in the WeRateDog twitter page?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MOST POPULAR DOG BREEDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "golden_retriever      150\n",
       "Labrador_retriever    100\n",
       "Pembroke               88\n",
       "Chihuahua              83\n",
       "pug                    57\n",
       "Name: dog_breed, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive_master['dog_breed'].value_counts()[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> __Golden retriever appear to be the most popular dog breed among followers of the page, since it is the most frequently posted breed by users.__\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight 3\n",
    "Which dog breeds have high number of retweets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Average retweet counts by stages of dogs'}, xlabel='stages'>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Call up rows with dog stages (while disregarding instances of null values)\n",
    "dog_stages = twitter_archive_master[twitter_archive_master[\"stage\"].isnull()==False]\n",
    "\n",
    "#Query dog stages and find dog stages. Pass it into variable 'category'\n",
    "category = dog_stages.query('stage==\"pupper\" or stage==\"doggo\" or stage==\"puppo\" or stage==\"floofer\"' )\n",
    "\n",
    "category_count = pd.DataFrame(category.groupby('stage').retweet_count.mean().sort_values())\n",
    "category_count['stages'] = category_count.index\n",
    "category_count.plot(x=\"stages\", y=\"retweet_count\", kind='bar', title=\"Average retweet counts by stages of dogs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> __Dogs that fall within the doggo stage appear to be popular amongst followers, as they have the highest average retweets of the other dog stages.__\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "extensions": {
   "jupyter_dashboards": {
    "activeView": "report_default",
    "version": 1,
    "views": {
     "grid_default": {
      "cellMargin": 10,
      "defaultCellHeight": 20,
      "maxColumns": 12,
      "name": "grid",
      "type": "grid"
     },
     "report_default": {
      "name": "report",
      "type": "report"
     }
    }
   }
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}