wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8ec65d47",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "*The project is aimed at implementing the whole data anaysis wrangling process which is categorized into three main stages in our case*\n",
    "\n",
    "***Project Details***\n",
    ">    *  **The data gathering Phase :** The twitter_archive_enhanced.csv file was presented for the project within the Udacity classroom which was downloaded manually. As is mostly known, this data was in short both dirty and messy and hence required a great deal of skills to get it to the desired format.*\n",
    ">    *  **The Assessment Phase:** During this phase of the project, attempts were made to gather all possible Issues within each of the datasets provided. Assessment of the data were group into two main types; “Quality” and “Tidy” issues.*\n",
    ">    *  **The Cleanining Phase:** A cleaning phase where data was cleaned using three main approaches in terms of defining, Coding and Testing*\n",
    ">    *  **Storing the Cleaned Data:** The cleaned data was stored in a csv format and a copy was made for the analysis phase*\n",
    ">    *  **Analysis and Visualizations:** Analysis and Visualizations of the dataset was made using Matlabs matplotlib library and the seaborn library*\n",
    "*  ***Tools needed for this project may includes:***\n",
    ">  *  *Jupiter notebook or any python running applicatoion*\n",
    ">  *  *Doc handling softwares*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c03a085c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "import json\n",
    "import os\n",
    "import ast\n",
    "import time\n",
    "import datetime\n",
    "import random\n",
    "import tweepy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# set the option to display the full string from a pandas DataFrame\n",
    "pd.options.display.max_rows\n",
    "# get option to get maximum columns displayed\n",
    "pd.get_option(\"display.max_columns\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b84349d",
   "metadata": {},
   "source": [
    "# Gathering Data\n",
    "***Brief Description***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7ea27f",
   "metadata": {},
   "source": [
    "## *twitter Archive file*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "236ecd88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421306343426/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "\n",
       "                                                                               source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                                                                 text  \\\n",
       "0                                                       This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "\n",
       "   retweeted_status_id  retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                  NaN                       NaN                        NaN   \n",
       "1                  NaN                       NaN                        NaN   \n",
       "\n",
       "                                                     expanded_urls  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "1  https://twitter.com/dog_rates/status/892177421306343426/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator     name doggo floofer pupper puppo  \n",
       "0                13                  10  Phineas  None    None   None  None  \n",
       "1                13                  10    Tilly  None    None   None  None  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing the twitter-archive-csv file\n",
    "df_archive =pd.read_csv('twitter-archive-enhanced.csv')\n",
    "\n",
    "# displaying the first two rows of the dataset\n",
    "df_archive.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "073a62a5",
   "metadata": {},
   "source": [
    "## *Image predictions file*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "5419f490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "\n",
    "# requesting for the image predictions data from the url above\n",
    "response = requests.get(url)\n",
    "\n",
    "# displaying the response given after requesting for the url\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c4be7b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save response to file\n",
    "with open(\"image_predictions.tsv\", mode='wb') as file:\n",
    "    file.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c6aa2774",
   "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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading from the saved image_predictions dataset\n",
    "df_img_predict =pd.read_csv('image_predictions.tsv', sep='\\t')\n",
    "df_img_predict.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cebbcd9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "817d2447",
   "metadata": {},
   "source": [
    "## *Twitter Api*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f4d1799",
   "metadata": {},
   "source": [
    "***Assessing the twitter api***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "55e63ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "consumer_key = 'hidden'\n",
    "consumer_secret = 'hidden'\n",
    "access_token = 'hidden'\n",
    "access_secret = 'hidden'\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "api = tweepy.API(auth, wait_on_rate_limit = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "39193c3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'created_at': 'Mon Jul 24 17:02:04 +0000 2017',\n",
       " 'id': 889531135344209921,\n",
       " 'id_str': '889531135344209921',\n",
       " 'full_text': \"This is Stuart. He's sporting his favorite fanny pack. Secretly filled with bones only. 13/10 puppared puppo #BarkWeek https://t.co/y70o6h3isq\",\n",
       " 'truncated': False,\n",
       " 'display_text_range': [0, 118],\n",
       " 'entities': {'hashtags': [{'text': 'BarkWeek', 'indices': [109, 118]}],\n",
       "  'symbols': [],\n",
       "  'user_mentions': [],\n",
       "  'urls': [],\n",
       "  'media': [{'id': 889531127467266049,\n",
       "    'id_str': '889531127467266049',\n",
       "    'indices': [119, 142],\n",
       "    'media_url': 'http://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg',\n",
       "    'media_url_https': 'https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg',\n",
       "    'url': 'https://t.co/y70o6h3isq',\n",
       "    'display_url': 'pic.twitter.com/y70o6h3isq',\n",
       "    'expanded_url': 'https://twitter.com/dog_rates/status/889531135344209921/photo/1',\n",
       "    'type': 'photo',\n",
       "    'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},\n",
       "     'small': {'w': 510, 'h': 680, 'resize': 'fit'},\n",
       "     'medium': {'w': 901, 'h': 1200, 'resize': 'fit'},\n",
       "     'large': {'w': 1201, 'h': 1600, 'resize': 'fit'}}}]},\n",
       " 'extended_entities': {'media': [{'id': 889531127467266049,\n",
       "    'id_str': '889531127467266049',\n",
       "    'indices': [119, 142],\n",
       "    'media_url': 'http://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg',\n",
       "    'media_url_https': 'https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg',\n",
       "    'url': 'https://t.co/y70o6h3isq',\n",
       "    'display_url': 'pic.twitter.com/y70o6h3isq',\n",
       "    'expanded_url': 'https://twitter.com/dog_rates/status/889531135344209921/photo/1',\n",
       "    'type': 'photo',\n",
       "    'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},\n",
       "     'small': {'w': 510, 'h': 680, 'resize': 'fit'},\n",
       "     'medium': {'w': 901, 'h': 1200, 'resize': 'fit'},\n",
       "     'large': {'w': 1201, 'h': 1600, 'resize': 'fit'}}}]},\n",
       " 'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>',\n",
       " 'in_reply_to_status_id': None,\n",
       " 'in_reply_to_status_id_str': None,\n",
       " 'in_reply_to_user_id': None,\n",
       " 'in_reply_to_user_id_str': None,\n",
       " 'in_reply_to_screen_name': None,\n",
       " 'user': {'id': 4196983835,\n",
       "  'id_str': '4196983835',\n",
       "  'name': 'WeRateDogs®',\n",
       "  'screen_name': 'dog_rates',\n",
       "  'location': 'all our links ➜',\n",
       "  'description': 'Your Only Source For Professional Dog Ratings Instagram and Facebook ➜ WeRateDogs partnerships@weratedogs.com | nonprofit: @15outof10 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀',\n",
       "  'url': 'https://t.co/YPc2XqmwyC',\n",
       "  'entities': {'url': {'urls': [{'url': 'https://t.co/YPc2XqmwyC',\n",
       "      'expanded_url': 'http://links.weratedogs.com',\n",
       "      'display_url': 'links.weratedogs.com',\n",
       "      'indices': [0, 23]}]},\n",
       "   'description': {'urls': []}},\n",
       "  'protected': False,\n",
       "  'followers_count': 9285945,\n",
       "  'friends_count': 21,\n",
       "  'listed_count': 7431,\n",
       "  'created_at': 'Sun Nov 15 21:41:29 +0000 2015',\n",
       "  'favourites_count': 146725,\n",
       "  'utc_offset': None,\n",
       "  'time_zone': None,\n",
       "  'geo_enabled': True,\n",
       "  'verified': True,\n",
       "  'statuses_count': 15677,\n",
       "  'lang': None,\n",
       "  'contributors_enabled': False,\n",
       "  'is_translator': False,\n",
       "  'is_translation_enabled': False,\n",
       "  'profile_background_color': '000000',\n",
       "  'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "  'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "  'profile_background_tile': False,\n",
       "  'profile_image_url': 'http://pbs.twimg.com/profile_images/1532010275708223491/-SBRbLoH_normal.jpg',\n",
       "  'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1532010275708223491/-SBRbLoH_normal.jpg',\n",
       "  'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1617810473',\n",
       "  'profile_link_color': 'F5ABB5',\n",
       "  'profile_sidebar_border_color': '000000',\n",
       "  'profile_sidebar_fill_color': '000000',\n",
       "  'profile_text_color': '000000',\n",
       "  'profile_use_background_image': False,\n",
       "  'has_extended_profile': False,\n",
       "  'default_profile': False,\n",
       "  'default_profile_image': False,\n",
       "  'following': False,\n",
       "  'follow_request_sent': False,\n",
       "  'notifications': False,\n",
       "  'translator_type': 'none',\n",
       "  'withheld_in_countries': []},\n",
       " 'geo': None,\n",
       " 'coordinates': None,\n",
       " 'place': None,\n",
       " 'contributors': None,\n",
       " 'is_quote_status': False,\n",
       " 'retweet_count': 1882,\n",
       " 'favorite_count': 13353,\n",
       " 'favorited': False,\n",
       " 'retweeted': False,\n",
       " 'possibly_sensitive': False,\n",
       " 'possibly_sensitive_appealable': False,\n",
       " 'lang': 'en'}"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing the api with '889531135344209921'\n",
    "id_of_tweet = '889531135344209921'\n",
    "tweet = api.get_status(id_of_tweet, tweet_mode='extended')._json\n",
    "tweet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a819e02",
   "metadata": {},
   "source": [
    "## *Download jason_tweets*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "bb37e868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2356"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_tweet_ids = list(df_archive['tweet_id']) \n",
    "len(archive_tweet_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a05ad36a",
   "metadata": {},
   "source": [
    "***The first 100 archive twitter ids were used to test the efficiency of the api assessment as using all the tweet_ids generated a \"rate limit reached\" error which was taking too long to retreive in my case***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a76348e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of ids: 2356\n",
      "tweet_id: 888202515573088257 was not found\"  Error: 404 Not Found\n",
      "144 - No status found with that ID.\n",
      "loop number: 50\n",
      "tweet_id: 873697596434513921 was not found\"  Error: 404 Not Found\n",
      "144 - No status found with that ID.\n",
      "loop number: 100\n",
      "\n",
      "Time elapsed (HH:MM:SS):  00:01:35\n",
      "\n",
      "Number of Successful Id's : 98\n",
      "Number of TweetErrors     :  2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "api_json = [] # save all succesfull tweet id's in list\n",
    "failed_tweet_ids = [] # save all failed twet id's in list\n",
    "count = 0 # Setting up a counter to ensure we tell our progress due to the number of tweet ids expected\n",
    "\n",
    "print(f'total number of ids: {len(archive_tweet_ids)}')\n",
    "start_time = time.time()\n",
    "for tweet_id in archive_tweet_ids[:100]:# Using tweet_id from first 100 tweets to test code below\n",
    "    count += 1;\n",
    "    try:\n",
    "        tweet = api.get_status(tweet_id, tweet_mode='extended')._json\n",
    "        api_json.append(tweet)\n",
    "        \n",
    "    except Exception as e:\n",
    "            print(f'tweet_id: {str(tweet_id)} was not found\"  Error: {e}')\n",
    "            failed_tweet_ids.append(str(tweet_id))\n",
    "            pass\n",
    "    \n",
    "    if count % 50 == 0:\n",
    "        print(f'loop number: {str(count)}')\n",
    "\n",
    "time_elapsed = time.time() - start_time\n",
    "time_spent = time.strftime(\"%H:%M:%S\", time.gmtime(time_elapsed))\n",
    "print(\"\\nTime elapsed (HH:MM:SS):  \" + time_spent + \"\\n\")\n",
    "\n",
    "print(f\"Number of Successful Id's : {len(api_json)}\")\n",
    "print(f\"Number of TweetErrors     :  {len(failed_tweet_ids)}\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "42320a69",
   "metadata": {},
   "outputs": [
    {
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       "    'protected': False,\n",
       "    'followers_count': 9285925,\n",
       "    'friends_count': 21,\n",
       "    'listed_count': 7431,\n",
       "    'created_at': 'Sun Nov 15 21:41:29 +0000 2015',\n",
       "    'favourites_count': 146725,\n",
       "    'utc_offset': None,\n",
       "    'time_zone': None,\n",
       "    'geo_enabled': True,\n",
       "    'verified': True,\n",
       "    'statuses_count': 15677,\n",
       "    'lang': None,\n",
       "    'contributors_enabled': False,\n",
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       "    'is_translation_enabled': False,\n",
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       "    'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "    'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "    'profile_background_tile': False,\n",
       "    'profile_image_url': 'http://pbs.twimg.com/profile_images/1532010275708223491/-SBRbLoH_normal.jpg',\n",
       "    'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1532010275708223491/-SBRbLoH_normal.jpg',\n",
       "    'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1617810473',\n",
       "    'profile_link_color': 'F5ABB5',\n",
       "    'profile_sidebar_border_color': '000000',\n",
       "    'profile_sidebar_fill_color': '000000',\n",
       "    'profile_text_color': '000000',\n",
       "    'profile_use_background_image': False,\n",
       "    'has_extended_profile': False,\n",
       "    'default_profile': False,\n",
       "    'default_profile_image': False,\n",
       "    'following': False,\n",
       "    'follow_request_sent': False,\n",
       "    'notifications': False,\n",
       "    'translator_type': 'none',\n",
       "    'withheld_in_countries': []},\n",
       "   'geo': None,\n",
       "   'coordinates': None,\n",
       "   'place': None,\n",
       "   'contributors': None,\n",
       "   'is_quote_status': False,\n",
       "   'retweet_count': 1331,\n",
       "   'favorite_count': 6324,\n",
       "   'favorited': False,\n",
       "   'retweeted': False,\n",
       "   'possibly_sensitive': False,\n",
       "   'possibly_sensitive_appealable': False,\n",
       "   'lang': 'en'},\n",
       "  'is_quote_status': False,\n",
       "  'retweet_count': 1331,\n",
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       "  'favorited': False,\n",
       "  'retweeted': False,\n",
       "  'lang': 'en'},\n",
       " {'created_at': 'Fri Jun 09 16:22:42 +0000 2017',\n",
       "  'id': 873213775632977920,\n",
       "  'id_str': '873213775632977920',\n",
       "  'full_text': \"This is Sierra. She's one precious pupper. Absolute 12/10. Been in and out of ICU her whole life. Help Sierra below\\n\\nhttps://t.co/Xp01EU3qyD https://t.co/V5lkvrGLdQ\",\n",
       "  'truncated': False,\n",
       "  'display_text_range': [0, 140],\n",
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       "   'symbols': [],\n",
       "   'user_mentions': [],\n",
       "   'urls': [{'url': 'https://t.co/Xp01EU3qyD',\n",
       "     'expanded_url': 'https://www.gofundme.com/help-my-baby-sierra-get-better',\n",
       "     'display_url': 'gofundme.com/help-my-baby-s…',\n",
       "     'indices': [117, 140]}],\n",
       "   'media': [{'id': 873213769794670593,\n",
       "     'id_str': '873213769794670593',\n",
       "     'indices': [141, 164],\n",
       "     'media_url': 'http://pbs.twimg.com/media/DB5HTBGXUAE0TiK.jpg',\n",
       "     'media_url_https': 'https://pbs.twimg.com/media/DB5HTBGXUAE0TiK.jpg',\n",
       "     'url': 'https://t.co/V5lkvrGLdQ',\n",
       "     'display_url': 'pic.twitter.com/V5lkvrGLdQ',\n",
       "     'expanded_url': 'https://twitter.com/dog_rates/status/873213775632977920/photo/1',\n",
       "     'type': 'photo',\n",
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       "      'medium': {'w': 800, 'h': 533, 'resize': 'fit'},\n",
       "      'small': {'w': 680, 'h': 453, 'resize': 'fit'},\n",
       "      'large': {'w': 800, 'h': 533, 'resize': 'fit'}}}]},\n",
       "  'extended_entities': {'media': [{'id': 873213769794670593,\n",
       "     'id_str': '873213769794670593',\n",
       "     'indices': [141, 164],\n",
       "     'media_url': 'http://pbs.twimg.com/media/DB5HTBGXUAE0TiK.jpg',\n",
       "     'media_url_https': 'https://pbs.twimg.com/media/DB5HTBGXUAE0TiK.jpg',\n",
       "     'url': 'https://t.co/V5lkvrGLdQ',\n",
       "     'display_url': 'pic.twitter.com/V5lkvrGLdQ',\n",
       "     'expanded_url': 'https://twitter.com/dog_rates/status/873213775632977920/photo/1',\n",
       "     'type': 'photo',\n",
       "     'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},\n",
       "      'medium': {'w': 800, 'h': 533, 'resize': 'fit'},\n",
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       "    {'id': 873213769819795456,\n",
       "     'id_str': '873213769819795456',\n",
       "     'indices': [141, 164],\n",
       "     'media_url': 'http://pbs.twimg.com/media/DB5HTBMWsAAdrYH.jpg',\n",
       "     'media_url_https': 'https://pbs.twimg.com/media/DB5HTBMWsAAdrYH.jpg',\n",
       "     'url': 'https://t.co/V5lkvrGLdQ',\n",
       "     'display_url': 'pic.twitter.com/V5lkvrGLdQ',\n",
       "     'expanded_url': 'https://twitter.com/dog_rates/status/873213775632977920/photo/1',\n",
       "     'type': 'photo',\n",
       "     'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},\n",
       "      'medium': {'w': 1200, 'h': 900, 'resize': 'fit'},\n",
       "      'small': {'w': 680, 'h': 510, 'resize': 'fit'},\n",
       "      'large': {'w': 2048, 'h': 1536, 'resize': 'fit'}}}]},\n",
       "  'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>',\n",
       "  'in_reply_to_status_id': None,\n",
       "  'in_reply_to_status_id_str': None,\n",
       "  'in_reply_to_user_id': None,\n",
       "  'in_reply_to_user_id_str': None,\n",
       "  'in_reply_to_screen_name': None,\n",
       "  'user': {'id': 4196983835,\n",
       "   'id_str': '4196983835',\n",
       "   'name': 'WeRateDogs®',\n",
       "   'screen_name': 'dog_rates',\n",
       "   'location': 'all our links ➜',\n",
       "   'description': 'Your Only Source For Professional Dog Ratings Instagram and Facebook ➜ WeRateDogs partnerships@weratedogs.com | nonprofit: @15outof10 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀',\n",
       "   'url': 'https://t.co/YPc2XqmwyC',\n",
       "   'entities': {'url': {'urls': [{'url': 'https://t.co/YPc2XqmwyC',\n",
       "       'expanded_url': 'http://links.weratedogs.com',\n",
       "       'display_url': 'links.weratedogs.com',\n",
       "       'indices': [0, 23]}]},\n",
       "    'description': {'urls': []}},\n",
       "   'protected': False,\n",
       "   'followers_count': 9285925,\n",
       "   'friends_count': 21,\n",
       "   'listed_count': 7431,\n",
       "   'created_at': 'Sun Nov 15 21:41:29 +0000 2015',\n",
       "   'favourites_count': 146725,\n",
       "   'utc_offset': None,\n",
       "   'time_zone': None,\n",
       "   'geo_enabled': True,\n",
       "   'verified': True,\n",
       "   'statuses_count': 15677,\n",
       "   'lang': None,\n",
       "   'contributors_enabled': False,\n",
       "   'is_translator': False,\n",
       "   'is_translation_enabled': False,\n",
       "   'profile_background_color': '000000',\n",
       "   'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "   'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "   'profile_background_tile': False,\n",
       "   'profile_image_url': 'http://pbs.twimg.com/profile_images/1532010275708223491/-SBRbLoH_normal.jpg',\n",
       "   'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1532010275708223491/-SBRbLoH_normal.jpg',\n",
       "   'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1617810473',\n",
       "   'profile_link_color': 'F5ABB5',\n",
       "   'profile_sidebar_border_color': '000000',\n",
       "   'profile_sidebar_fill_color': '000000',\n",
       "   'profile_text_color': '000000',\n",
       "   'profile_use_background_image': False,\n",
       "   'has_extended_profile': False,\n",
       "   'default_profile': False,\n",
       "   'default_profile_image': False,\n",
       "   'following': False,\n",
       "   'follow_request_sent': False,\n",
       "   'notifications': False,\n",
       "   'translator_type': 'none',\n",
       "   'withheld_in_countries': []},\n",
       "  'geo': None,\n",
       "  'coordinates': None,\n",
       "  'place': None,\n",
       "  'contributors': None,\n",
       "  'is_quote_status': False,\n",
       "  'retweet_count': 1331,\n",
       "  'favorite_count': 6324,\n",
       "  'favorited': False,\n",
       "  'retweeted': False,\n",
       "  'possibly_sensitive': False,\n",
       "  'possibly_sensitive_appealable': False,\n",
       "  'lang': 'en'},\n",
       " {'created_at': 'Fri Jun 09 00:02:31 +0000 2017',\n",
       "  'id': 872967104147763200,\n",
       "  'id_str': '872967104147763200',\n",
       "  'full_text': \"Here's a very large dog. He has a date later. Politely asked this water person to check if his breath is bad. 12/10 good to go doggo https://t.co/EMYIdoblMR\",\n",
       "  'truncated': False,\n",
       "  'display_text_range': [0, 132],\n",
       "  'entities': {'hashtags': [],\n",
       "   'symbols': [],\n",
       "   'user_mentions': [],\n",
       "   'urls': [],\n",
       "   'media': [{'id': 872967099819462661,\n",
       "     'id_str': '872967099819462661',\n",
       "     'indices': [133, 156],\n",
       "     'media_url': 'http://pbs.twimg.com/media/DB1m871XkAUbCkY.jpg',\n",
       "     'media_url_https': 'https://pbs.twimg.com/media/DB1m871XkAUbCkY.jpg',\n",
       "     'url': 'https://t.co/EMYIdoblMR',\n",
       "     'display_url': 'pic.twitter.com/EMYIdoblMR',\n",
       "     'expanded_url': 'https://twitter.com/dog_rates/status/872967104147763200/photo/1',\n",
       "     'type': 'photo',\n",
       "     'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},\n",
       "      'medium': {'w': 1182, 'h': 1160, 'resize': 'fit'},\n",
       "      'small': {'w': 680, 'h': 667, 'resize': 'fit'},\n",
       "      'large': {'w': 1182, 'h': 1160, 'resize': 'fit'}}}]},\n",
       "  'extended_entities': {'media': [{'id': 872967099819462661,\n",
       "     'id_str': '872967099819462661',\n",
       "     'indices': [133, 156],\n",
       "     'media_url': 'http://pbs.twimg.com/media/DB1m871XkAUbCkY.jpg',\n",
       "     'media_url_https': 'https://pbs.twimg.com/media/DB1m871XkAUbCkY.jpg',\n",
       "     'url': 'https://t.co/EMYIdoblMR',\n",
       "     'display_url': 'pic.twitter.com/EMYIdoblMR',\n",
       "     'expanded_url': 'https://twitter.com/dog_rates/status/872967104147763200/photo/1',\n",
       "     'type': 'photo',\n",
       "     'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},\n",
       "      'medium': {'w': 1182, 'h': 1160, 'resize': 'fit'},\n",
       "      'small': {'w': 680, 'h': 667, 'resize': 'fit'},\n",
       "      'large': {'w': 1182, 'h': 1160, 'resize': 'fit'}}},\n",
       "    {'id': 872967099819446272,\n",
       "     'id_str': '872967099819446272',\n",
       "     'indices': [133, 156],\n",
       "     'media_url': 'http://pbs.twimg.com/media/DB1m871XUAAw5vZ.jpg',\n",
       "     'media_url_https': 'https://pbs.twimg.com/media/DB1m871XUAAw5vZ.jpg',\n",
       "     'url': 'https://t.co/EMYIdoblMR',\n",
       "     'display_url': 'pic.twitter.com/EMYIdoblMR',\n",
       "     'expanded_url': 'https://twitter.com/dog_rates/status/872967104147763200/photo/1',\n",
       "     'type': 'photo',\n",
       "     'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},\n",
       "      'medium': {'w': 1200, 'h': 1084, 'resize': 'fit'},\n",
       "      'small': {'w': 680, 'h': 614, 'resize': 'fit'},\n",
       "      'large': {'w': 1209, 'h': 1092, 'resize': 'fit'}}}]},\n",
       "  'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>',\n",
       "  'in_reply_to_status_id': None,\n",
       "  'in_reply_to_status_id_str': None,\n",
       "  'in_reply_to_user_id': None,\n",
       "  'in_reply_to_user_id_str': None,\n",
       "  'in_reply_to_screen_name': None,\n",
       "  'user': {'id': 4196983835,\n",
       "   'id_str': '4196983835',\n",
       "   'name': 'WeRateDogs®',\n",
       "   'screen_name': 'dog_rates',\n",
       "   'location': 'all our links ➜',\n",
       "   'description': 'Your Only Source For Professional Dog Ratings Instagram and Facebook ➜ WeRateDogs partnerships@weratedogs.com | nonprofit: @15outof10 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀',\n",
       "   'url': 'https://t.co/YPc2XqmwyC',\n",
       "   'entities': {'url': {'urls': [{'url': 'https://t.co/YPc2XqmwyC',\n",
       "       'expanded_url': 'http://links.weratedogs.com',\n",
       "       'display_url': 'links.weratedogs.com',\n",
       "       'indices': [0, 23]}]},\n",
       "    'description': {'urls': []}},\n",
       "   'protected': False,\n",
       "   'followers_count': 9285925,\n",
       "   'friends_count': 21,\n",
       "   'listed_count': 7431,\n",
       "   'created_at': 'Sun Nov 15 21:41:29 +0000 2015',\n",
       "   'favourites_count': 146725,\n",
       "   'utc_offset': None,\n",
       "   'time_zone': None,\n",
       "   'geo_enabled': True,\n",
       "   'verified': True,\n",
       "   'statuses_count': 15677,\n",
       "   'lang': None,\n",
       "   'contributors_enabled': False,\n",
       "   'is_translator': False,\n",
       "   'is_translation_enabled': False,\n",
       "   'profile_background_color': '000000',\n",
       "   'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "   'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "   'profile_background_tile': False,\n",
       "   'profile_image_url': 'http://pbs.twimg.com/profile_images/1532010275708223491/-SBRbLoH_normal.jpg',\n",
       "   'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1532010275708223491/-SBRbLoH_normal.jpg',\n",
       "   'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1617810473',\n",
       "   'profile_link_color': 'F5ABB5',\n",
       "   'profile_sidebar_border_color': '000000',\n",
       "   'profile_sidebar_fill_color': '000000',\n",
       "   'profile_text_color': '000000',\n",
       "   'profile_use_background_image': False,\n",
       "   'has_extended_profile': False,\n",
       "   'default_profile': False,\n",
       "   'default_profile_image': False,\n",
       "   'following': False,\n",
       "   'follow_request_sent': False,\n",
       "   'notifications': False,\n",
       "   'translator_type': 'none',\n",
       "   'withheld_in_countries': []},\n",
       "  'geo': None,\n",
       "  'coordinates': None,\n",
       "  'place': None,\n",
       "  'contributors': None,\n",
       "  'is_quote_status': False,\n",
       "  'retweet_count': 4563,\n",
       "  'favorite_count': 24084,\n",
       "  'favorited': False,\n",
       "  'retweeted': False,\n",
       "  'possibly_sensitive': False,\n",
       "  'possibly_sensitive_appealable': False,\n",
       "  'lang': 'en'}]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying json file received from api\n",
    "api_json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b484fd",
   "metadata": {},
   "source": [
    "## *Save Jason data as tweet_json.txt file*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d09db4",
   "metadata": {},
   "source": [
    "*The tweet_json.txt file was downloaded manually from the udacity classroom as because the json file being requested from the api was not responding for several hours after encountering a **\"rate limit reached\"** error.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "fbb55258",
   "metadata": {},
   "outputs": [],
   "source": [
    "# used a different name to avoid conflict with json file I actually used for the project\n",
    "file_name = 'my_tweet_json.txt' \n",
    "    \n",
    "with open(file_name, 'w') as text_file:\n",
    "    for json_tweet in api_json:\n",
    "        json.dump(json_tweet, text_file)\n",
    "        text_file.write('\\n')        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "aaf45e06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive D is ABBEY\n",
      " Volume Serial Number is 0A3B-0506\n",
      "\n",
      " Directory of D:\\jupiter_files\\data_wrangling\\project_2\n",
      "\n",
      "06/26/2022  12:12 PM           477,048 my_tweet_json.txt\n",
      "               1 File(s)        477,048 bytes\n",
      "               0 Dir(s)  64,458,944,512 bytes free\n"
     ]
    }
   ],
   "source": [
    "# checking existence of json file written into a txt format in local drive\n",
    "ls my_tweet_json.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6df0e6",
   "metadata": {},
   "source": [
    "***At this point the twet_json.txt file from classromm was also downloaded***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e871e410",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive D is ABBEY\n",
      " Volume Serial Number is 0A3B-0506\n",
      "\n",
      " Directory of D:\\jupiter_files\\data_wrangling\\project_2\n",
      "\n",
      "06/17/2022  02:08 PM        10,609,234 tweet_json.txt\n",
      "               1 File(s)     10,609,234 bytes\n",
      "               0 Dir(s)  64,458,944,512 bytes free\n"
     ]
    }
   ],
   "source": [
    "# checking existence of the tweet_json.txt file downloaded\n",
    "%ls tweet_json.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa291d1",
   "metadata": {},
   "source": [
    "## *Read tweet_jason.txt file created, extract data needed and convert to a DataFrame*\n",
    "*  *Rate limit was reached at different instances and was taking too long to receive the data from twitter hence used the **\"tweet_json.txt\"** provided in the udacity classroom*\n",
    "*  ***NB:*** *Reading from the json data from my own api would have provided a list containing different objects, hence the code for reading through would have been different*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "38cfd0c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "needed_from_json = [] # List to store all needed info from json\n",
    "\n",
    "jason_file = open('tweet_json.txt', 'r') # opens an existing txt file without ovewritting it\n",
    "for each_line in jason_file: #reads eachline within the txt file\n",
    "    json_data = json.loads(each_line) #reads the txt into python and parses the JSON\n",
    "\n",
    "    # extracting variables from the JSON data\n",
    "    json_id = json_data['id']\n",
    "    json_retweet_count = json_data['retweet_count']\n",
    "    json_favorite_count = json_data['favorite_count']\n",
    "\n",
    "    # creating a dictionary with the JSON data, then adding to the \"needed_from_json\" list\n",
    "    json_extracted_columns = {'tweet_id': json_id, \n",
    "                 'retweet_count': json_retweet_count, \n",
    "                 'favorite_count': json_favorite_count\n",
    "                }\n",
    "    needed_from_json.append(json_extracted_columns)\n",
    "\n",
    "jason_file.close()\n",
    "\n",
    "# convert the needed_from_jsom list to a DataFrame\n",
    "df_api_json = pd.DataFrame(needed_from_json, columns = ['tweet_id', 'retweet_count', 'favorite_count'])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "963de19b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading from my own api json saved in a list\n",
    "needed_from_json_mine = []\n",
    "with open('my_tweet_json.txt', 'r') as my_json_file:\n",
    "    each_line = my_json_file.readline() #readline method reads eachline of the file\n",
    "    while each_line:\n",
    "        json_data = json.loads(each_line) #reads the txt into python and parses the JSON\n",
    "        \n",
    "        # extracting variables from the JSON data\n",
    "        data_id = json_data['id']\n",
    "        data_retweet_count = json_data['retweet_count']\n",
    "        data_favorite_count = json_data['favorite_count']\n",
    "        \n",
    "        # creating a dictionary with the JSON data, then adding to the \"needed_from_json_mine\" list\n",
    "        json_extracted_columns = {'tweet_id': data_id, \n",
    "                     'retweet_count': data_retweet_count, \n",
    "                     'favorite_count': data_favorite_count\n",
    "                    }\n",
    "        needed_from_json_mine.append(json_extracted_columns)\n",
    "\n",
    "        # read the next line of JSON data\n",
    "        each_line = my_json_file.readline()\n",
    "        # ----- while -----\n",
    "\n",
    "# convert the needed_from_json_mine list to a DataFrame\n",
    "my_df_api_json = pd.DataFrame(needed_from_json_mine, columns = ['tweet_id', 'retweet_count', 'favorite_count'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "2a9f2fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of json data present in my own api\n",
    "len(my_df_api_json.tweet_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "6963f97f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>879862464715927552</td>\n",
       "      <td>2929</td>\n",
       "      <td>19708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>877611172832227328</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>882762694511734784</td>\n",
       "      <td>4097</td>\n",
       "      <td>24798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>890006608113172480</td>\n",
       "      <td>6153</td>\n",
       "      <td>27040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>887343217045368832</td>\n",
       "      <td>8815</td>\n",
       "      <td>29593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id  retweet_count  favorite_count\n",
       "62  879862464715927552           2929           19708\n",
       "77  877611172832227328             70               0\n",
       "48  882762694511734784           4097           24798\n",
       "10  890006608113172480           6153           27040\n",
       "23  887343217045368832           8815           29593"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a look at the data from from my own api used\n",
    "my_df_api_json.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "96cd1ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2354"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking nuber of json data present in the tweet_json.txt file in downloaded in classroom\n",
    "len(df_api_json.tweet_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "286b1ed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>759159934323924993</td>\n",
       "      <td>1359</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>831262627380748289</td>\n",
       "      <td>2350</td>\n",
       "      <td>13066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>765222098633691136</td>\n",
       "      <td>3914</td>\n",
       "      <td>12902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1058</th>\n",
       "      <td>741438259667034112</td>\n",
       "      <td>937</td>\n",
       "      <td>4026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2131</th>\n",
       "      <td>670073503555706880</td>\n",
       "      <td>874</td>\n",
       "      <td>1674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  retweet_count  favorite_count\n",
       "893   759159934323924993           1359               0\n",
       "353   831262627380748289           2350           13066\n",
       "852   765222098633691136           3914           12902\n",
       "1058  741438259667034112            937            4026\n",
       "2131  670073503555706880            874            1674"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify json data\n",
    "df_api_json.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85ed043",
   "metadata": {},
   "source": [
    "## Assessing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3b632b",
   "metadata": {},
   "source": [
    "*Access the gathered data by grouping them into two main categories*\n",
    "*  **Quality Assessments:** Issues regarding this form of assessment was defined as being dirty. Four main criteria was looked at the most with regards to how dirty the data;*\n",
    ">  *  *Completness in terms of the wholeness of the dataset*\n",
    ">  *  *Validity in terms of having the right info at the right place. A value may be accurate but not valid*\n",
    ">  *  *Accuracy which is the right info in this sense*\n",
    ">  *  *Consistency in terms of uniformity in values of datasets*\n",
    "*   **Tidy Assessments:** Issues regarding how messy the data is.\n",
    ">  *  *structural issues it terms of missing columns, unwanted columns and many others regarding the shape of the dataset*\n",
    "* Visual Assessments on the data was done by using external softwares like Excel with it filter and sorting functionalities as well as skimming through the first few row of the pandas dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa9c63a",
   "metadata": {},
   "source": [
    "***Save all DataFrames in the form of csv files to make visual assessments using Excell***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "f087aaa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the img_predictions and af_api_json dataset to a csv format \n",
    "# df_arch already has a csv format in the name of 'twitter-archive-enhanced.csv' at the initial stages\n",
    "\n",
    "df_img_pred.to_csv(\"image_predictions_udacity\", sep='\\t', index=False)\n",
    "df_api_json.to_csv(\"twitter_api_json\", sep=',', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "08b775a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421306343426/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181378084864/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557279858688/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                                                               source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                                                                 text  \\\n",
       "0                                                       This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "2                   This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB   \n",
       "3                                                             This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ   \n",
       "4  This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f   \n",
       "\n",
       "   retweeted_status_id  retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                  NaN                       NaN                        NaN   \n",
       "1                  NaN                       NaN                        NaN   \n",
       "2                  NaN                       NaN                        NaN   \n",
       "3                  NaN                       NaN                        NaN   \n",
       "4                  NaN                       NaN                        NaN   \n",
       "\n",
       "                                                                                                                     expanded_urls  \\\n",
       "0                                                                  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "1                                                                  https://twitter.com/dog_rates/status/892177421306343426/photo/1   \n",
       "2                                                                  https://twitter.com/dog_rates/status/891815181378084864/photo/1   \n",
       "3                                                                  https://twitter.com/dog_rates/status/891689557279858688/photo/1   \n",
       "4  https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator      name doggo floofer pupper puppo  \n",
       "0                13                  10   Phineas  None    None   None  None  \n",
       "1                13                  10     Tilly  None    None   None  None  \n",
       "2                12                  10    Archie  None    None   None  None  \n",
       "3                13                  10     Darla  None    None   None  None  \n",
       "4                12                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "9d459f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "cb57bff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>rating_denominator</th>\n",
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       "  <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",
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       "    <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",
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       "  </tbody>\n",
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       "</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": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "210a54dc",
   "metadata": {},
   "source": [
    "* *The difference between the **\"75th percentile values\"** and the **\"maximum values\"** for both **ratings columns** seems upsurd, hence, would have to be looked into. Also minimum values of ratings being as low as \"0\" seems disturbing. Could this be because **\"they love their dogs Brent?\"**.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "6fe5b153",
   "metadata": {},
   "outputs": [
    {
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       "      <td>132</td>\n",
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       "      <td>532</td>\n",
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       "                tweet_id  retweet_count  favorite_count\n",
       "0     892420643555336193           8853           39467\n",
       "1     892177421306343426           6514           33819\n",
       "2     891815181378084864           4328           25461\n",
       "3     891689557279858688           8964           42908\n",
       "4     891327558926688256           9774           41048\n",
       "...                  ...            ...             ...\n",
       "2349  666049248165822465             41             111\n",
       "2350  666044226329800704            147             311\n",
       "2351  666033412701032449             47             128\n",
       "2352  666029285002620928             48             132\n",
       "2353  666020888022790149            532            2535\n",
       "\n",
       "[2354 rows x 3 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_api_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "fe9c91d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2354"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_api_json.tweet_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "12440fcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   tweet_id        2354 non-null   int64\n",
      " 1   retweet_count   2354 non-null   int64\n",
      " 2   favorite_count  2354 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 55.3 KB\n"
     ]
    }
   ],
   "source": [
    "df_api_json.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "7a0895aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>Rottweiler</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2075 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "0     666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1     666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2     666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3     666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4     666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "...                  ...                                              ...   \n",
       "2070  891327558926688256  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "2071  891689557279858688  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "2072  891815181378084864  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "2073  892177421306343426  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2074  892420643555336193  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0           1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1           1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2           1         German_shepherd  0.596461    True            malinois   \n",
       "3           1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4           1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "...       ...                     ...       ...     ...                 ...   \n",
       "2070        2                  basset  0.555712    True    English_springer   \n",
       "2071        1             paper_towel  0.170278   False  Labrador_retriever   \n",
       "2072        1               Chihuahua  0.716012    True            malamute   \n",
       "2073        1               Chihuahua  0.323581    True            Pekinese   \n",
       "2074        1                  orange  0.097049   False               bagel   \n",
       "\n",
       "       p2_conf  p2_dog                           p3   p3_conf  p3_dog  \n",
       "0     0.156665    True            Shetland_sheepdog  0.061428    True  \n",
       "1     0.074192    True          Rhodesian_ridgeback  0.072010    True  \n",
       "2     0.138584    True                   bloodhound  0.116197    True  \n",
       "3     0.360687    True           miniature_pinscher  0.222752    True  \n",
       "4     0.243682    True                     Doberman  0.154629    True  \n",
       "...        ...     ...                          ...       ...     ...  \n",
       "2070  0.225770    True  German_short-haired_pointer  0.175219    True  \n",
       "2071  0.168086    True                      spatula  0.040836   False  \n",
       "2072  0.078253    True                       kelpie  0.031379    True  \n",
       "2073  0.090647    True                     papillon  0.068957    True  \n",
       "2074  0.085851   False                       banana  0.076110   False  \n",
       "\n",
       "[2075 rows x 12 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_img_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "7aa3c8c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_img_predict.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "858d7786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2075"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding out unique values in the image prediction table\n",
    "df_img_predict.tweet_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "523fb1b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2e6bb6f9",
   "metadata": {},
   "source": [
    "*The hash(**\"#\"**) symbol in front of the issues represents the order in which they were addressed*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf840acf",
   "metadata": {},
   "source": [
    "### ***Quality Issues(completeness, validity, accuracy, and consistency)***\n",
    "*  *.**(#6)**. the **\"tweet ids\"** are integers in all the datasets*\n",
    "*  *.**(#7)**. 3 **\"retweets\"** columns in the df_archive with 181 non-null values each*\n",
    "*  *.**(#10)**. strange values in **\"rating numerator\"** of df_archive table*\n",
    "*  *.**(#9)**. strange values in **\"rating denominator\"** of df_archive table*\n",
    "*  *.**(#11)**. **\"wierd names\"** in the names columns in df_archive table such as **\"a, an, e,t,c\"**.*\n",
    "*  *.**(#12)**.the **\"+0000\"** in timestamp column in df_archive table*\n",
    "*  *.**(#13)**. datatype of **\"timestamp\"** column of df_archive*\n",
    "*  *.**(#14)**. href's in **\"source\"** column of df_archive*\n",
    "*  *.**(#15)**. datatypes of some columns not valid*\n",
    "*  *.**(#16)**. undiscriptive column names and unwanted columns*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b927be1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3821b54f",
   "metadata": {},
   "source": [
    "## ***Tidyness (Structural Issues)***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60894265",
   "metadata": {},
   "source": [
    "*  *.**(#5)**. doggo, floofer, pupper and puppo occupying 4 separate columns*\n",
    "*  *.**(#1)**. the **3** separate **data tables** bearing similar info*\n",
    "*  *.**(#2)**. df_archive has **\"2356\"** entries with df_img_pred having **\"2075\"** entries*\n",
    "*  *.**(#4)**.**\"img_num\"** column may not be relevant for analysis*\n",
    "*  *.**(#8)**. 2 **\"reply_retweets\"** columns in the df_archive with 78 non-null values each*\n",
    "*  *.**(#3)**. the **p1,,p2 and p3** columns,  **p1_conf,p2_conf and p3_conf** columns as well as **p1_dog,p2_dog,p3_dog** columns all discusses same information respectively in  df_img_pred table*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3e7278e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "52d7f8f8",
   "metadata": {},
   "source": [
    "# Cleaning Data\n",
    "*The concepts applied in addressing each of the issues found was applied in two main steps:*\n",
    "*  **Defining the problem:** *This involves clearly stating how you intentd to solve the problem*\n",
    "*  **Coding and Testing:** *Laying down your definition in the form of a code as well as running the code to verify whether it yields the result expected*\n",
    "\n",
    "*Before cleaning the dataset, It is advisable to make a copy of the orginal data. In Cleaning of data, It is deemed a great deal to start with issues related to the structure of the datasets before tackling those regarding the quality aspect.*\n",
    "\n",
    "*During the data cleaning process we would be checking  in many occassions the existence of columns within dataframes hence, the function below to make that a bit easy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "56becfa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to check the presence of a column in a dataframe, applies for only a single column name\n",
    "def check(column,df):\n",
    "    if column in df.columns:\n",
    "        print(True)\n",
    "    else:\n",
    "        print(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12eb0b96",
   "metadata": {},
   "source": [
    "## Make Copy of original DataSets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "ca0c31dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make copies of the original datasets\n",
    "df_arch = df_archive.copy()\n",
    "df_img_pred = df_img_predict.copy()\n",
    "df_api = df_api_json.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0630f45",
   "metadata": {},
   "source": [
    "## Issue #1\n",
    "*the **3** separate **data tables** bearing similar info*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b6be03",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *  *Merge all datasets to the df_archive table using the merge function*\n",
    ">  *  *First merge json data to twitter achive to a new dataframe*\n",
    ">  *  *Then merge the image predictions table to the merged dataframe then rename the latest dataframe*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8f917c0",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "326eaf5d",
   "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",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>8853.0</td>\n",
       "      <td>39467.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421306343426/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>6514.0</td>\n",
       "      <td>33819.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "\n",
       "                                                                               source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                                                                 text  \\\n",
       "0                                                       This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "\n",
       "   retweeted_status_id  retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                  NaN                       NaN                        NaN   \n",
       "1                  NaN                       NaN                        NaN   \n",
       "\n",
       "                                                     expanded_urls  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "1  https://twitter.com/dog_rates/status/892177421306343426/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator     name doggo floofer pupper puppo  \\\n",
       "0                13                  10  Phineas  None    None   None  None   \n",
       "1                13                  10    Tilly  None    None   None  None   \n",
       "\n",
       "   retweet_count  favorite_count  \n",
       "0         8853.0         39467.0  \n",
       "1         6514.0         33819.0  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First merge the jsons data to the twitter archive data\n",
    "df_archive_1 = pd.merge(df_arch, df_api, on='tweet_id', how='left')\n",
    "\n",
    "# checking changes made\n",
    "df_archive_1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "76fc590f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2356, 19)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the shape of the new dataframe\n",
    "df_archive_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "52962302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2075, 12)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the shape of the image prediction datframe\n",
    "df_img_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "de955375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "281"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the difference in the number of rows of the dataframes to be merged\n",
    "df_archive_1.shape[0]-df_img_pred.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae386a02",
   "metadata": {},
   "source": [
    "*It is worth nothing that, the **df_img_pred** table has **\"281\"** rows less than the **df_archive_1** table. This shall be delved into as the next issue*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "b5edfaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2356, 30)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge image prediction dataframe to the merged dataframe\n",
    "df_archive_2 = pd.merge(df_archive_1, df_img_pred, on='tweet_id', how='left')\n",
    "df_archive_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "cc526678",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of all the tables merged together\n",
    "df_all = df_archive_2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "36b9430e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2356, 30)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Findout the shape of the dataset\n",
    "df_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "76a82090",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2356 entries, 0 to 2355\n",
      "Data columns (total 30 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      " 17  retweet_count               2354 non-null   float64\n",
      " 18  favorite_count              2354 non-null   float64\n",
      " 19  jpg_url                     2075 non-null   object \n",
      " 20  img_num                     2075 non-null   float64\n",
      " 21  p1                          2075 non-null   object \n",
      " 22  p1_conf                     2075 non-null   float64\n",
      " 23  p1_dog                      2075 non-null   object \n",
      " 24  p2                          2075 non-null   object \n",
      " 25  p2_conf                     2075 non-null   float64\n",
      " 26  p2_dog                      2075 non-null   object \n",
      " 27  p3                          2075 non-null   object \n",
      " 28  p3_conf                     2075 non-null   float64\n",
      " 29  p3_dog                      2075 non-null   object \n",
      "dtypes: float64(10), int64(3), object(17)\n",
      "memory usage: 570.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# find make a quick check throught the dataset\n",
    "df_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0df5d923",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c715e835",
   "metadata": {},
   "source": [
    "# Issue #2\n",
    "*df_arch has **\"2356\"** entries with df_img_pred having **\"2075\"** entries*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c7dc45",
   "metadata": {},
   "source": [
    "*Since the tables have now been merged together at this point, the difference in row numbers which was **281** basically implies there are rows with missing image predictions url. Lets now find those rows and drop them*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13814c30",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *  *Exclude all rows with missing jpg_url values*\n",
    ">  *  *Query the data for rows with no jpg_url values*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a3d73f",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "dc7dfa40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excluding all rows without jpg_url values\n",
    "df_all = df_all[df_all.jpg_url.notna()]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50a5703a",
   "metadata": {},
   "source": [
    "*Lets now query the data for rows with no jpg_url values*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "8840f0f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>...</th>\n",
       "      <th>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",
       "<p>0 rows × 30 columns</p>\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, retweet_count, favorite_count, jpg_url, img_num, p1, p1_conf, p1_dog, p2, p2_conf, p2_dog, p3, p3_conf, p3_dog]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 30 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying changes made to the dataset\n",
    "df_all[df_all.jpg_url.isna()] # gets data for row without jpg_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b92248e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dc5411df",
   "metadata": {},
   "source": [
    "## Issue #3\n",
    "*the **p1,p2 and p3** columns,  **p1_conf,p2_conf and p3_conf** columns as well as **p1_dog,p2_dog,p3_dog** columns all discusses same information respectively in  df_img_pred table*\n",
    ">   *  *With the 3 modules used, any of whose prediction is **\"True\"**, we consider the name attached to it a being a dog. As a result, we would try and loop over all the prediction modules to find out those predictions are considered as **\"True\"**.*\n",
    ">   *  *Our aim then, is to come up with 3 columns with one being the **\"name of the dog\"**, the other being **\"whether or not it is a dog\"** as well as the **\"confidence level for that prediction\"**.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b044e81f",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *  *Create a list that contains the conditions needed to be met*\n",
    ">  *  *Create another list with choices for the breed of dog based on the conditions provided*\n",
    ">  *  *Create a third list with choices for the confidence level for the list of conditions*\n",
    ">  *  *Set the default value for the breed to be **\"no_breed\"** and the default for the confidence level to be **\"0\"**. This may occur when a dog is not categorized under any of the breeds*\n",
    ">  *  *With **\"numpy's .select\"** function, loop through the various conditions and make a choice based on that condition.*\n",
    ">  *  *Drop columns **'p1','p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog'**.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2823b56",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "e422e5c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421306343426/photo/1</td>\n",
       "      <td>...</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "\n",
       "                                                                               source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                                                                 text  \\\n",
       "0                                                       This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "\n",
       "   retweeted_status_id  retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                  NaN                       NaN                        NaN   \n",
       "1                  NaN                       NaN                        NaN   \n",
       "\n",
       "                                                     expanded_urls  ...  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643555336193/photo/1  ...   \n",
       "1  https://twitter.com/dog_rates/status/892177421306343426/photo/1  ...   \n",
       "\n",
       "   img_num         p1   p1_conf p1_dog        p2   p2_conf p2_dog        p3  \\\n",
       "0      1.0     orange  0.097049  False     bagel  0.085851  False    banana   \n",
       "1      1.0  Chihuahua  0.323581   True  Pekinese  0.090647   True  papillon   \n",
       "\n",
       "    p3_conf p3_dog  \n",
       "0  0.076110  False  \n",
       "1  0.068957   True  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets have a look at the first two rows of the data again\n",
    "df_all.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "5f454f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# source https://stackoverflow.com/questions/19913659/pandas-conditional-creation-of-a-series-dataframe-column\n",
    "\n",
    "# setting up conditions and choices to make for the new data frames\n",
    "conditions = [(df_all['p1_dog'] == True), (df_all['p2_dog'] == True), (df_all['p3_dog'] == True)]\n",
    "breed_choices = [df_all['p1'], df_all['p2'], df_all['p3']]\n",
    "confidence_choices = [df_all['p1_conf'], df_all['p2_conf'], df_all['p3_conf']]\n",
    "\n",
    "# Assign new columns based on the choices and selections given above\n",
    "df_all['breed'] = np.select(conditions, breed_choices, default='no_breed') # the index within the conditions corresponds to the index within the choices\n",
    "df_all['confidence_level'] = np.select(conditions, confidence_choices, default= 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "6bf726ac",
   "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>breed</th>\n",
       "      <th>confidence_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2091</th>\n",
       "      <td>no_breed</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.029340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>pug</td>\n",
       "      <td>0.997445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   breed  confidence_level\n",
       "2091            no_breed          0.000000\n",
       "262   Labrador_retriever          0.029340\n",
       "1455                 pug          0.997445"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify columns created\n",
    "df_all[['breed','confidence_level']].sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "2f3930b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2075, 32)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check shape of present dataframe\n",
    "df_all.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "fec109cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>...</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>breed</th>\n",
       "      <th>confidence_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>790581949425475584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-24 15:53:19 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Duke. He sneaks into the fridge sometimes. It's his safe place. 11/10 would give little jacket if necessary https://t.co/Fd5WFDTMH4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/790581949425475584/photo/1,https://twitter.com/dog_rates/status/790581949425475584/photo/1</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>8183.0</td>\n",
       "      <td>22473.0</td>\n",
       "      <td>https://pbs.twimg.com/media/Cvi2FiKWgAAif1u.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245</th>\n",
       "      <td>667885044254572545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-21 01:59:37 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Meet Stu. Stu has stacks on stacks and an eye made of pure gold. 10/10 pay for my tuition pls https://t.co/7rkYZQdKEd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667885044254572545/photo/1</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>530.0</td>\n",
       "      <td>868.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CUTN5V4XAAAIa4R.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.088530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "666   790581949425475584                    NaN                  NaN   \n",
       "2245  667885044254572545                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "666   2016-10-24 15:53:19 +0000   \n",
       "2245  2015-11-21 01:59:37 +0000   \n",
       "\n",
       "                                                                                  source  \\\n",
       "666   <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2245  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                                             text  \\\n",
       "666   This is Duke. He sneaks into the fridge sometimes. It's his safe place. 11/10 would give little jacket if necessary https://t.co/Fd5WFDTMH4   \n",
       "2245                        Meet Stu. Stu has stacks on stacks and an eye made of pure gold. 10/10 pay for my tuition pls https://t.co/7rkYZQdKEd   \n",
       "\n",
       "      retweeted_status_id  retweeted_status_user_id  \\\n",
       "666                   NaN                       NaN   \n",
       "2245                  NaN                       NaN   \n",
       "\n",
       "     retweeted_status_timestamp  \\\n",
       "666                         NaN   \n",
       "2245                        NaN   \n",
       "\n",
       "                                                                                                                        expanded_urls  \\\n",
       "666   https://twitter.com/dog_rates/status/790581949425475584/photo/1,https://twitter.com/dog_rates/status/790581949425475584/photo/1   \n",
       "2245                                                                  https://twitter.com/dog_rates/status/667885044254572545/photo/1   \n",
       "\n",
       "      ...  doggo  floofer pupper puppo retweet_count favorite_count  \\\n",
       "666   ...   None     None   None  None        8183.0        22473.0   \n",
       "2245  ...   None     None   None  None         530.0          868.0   \n",
       "\n",
       "                                              jpg_url  img_num     breed  \\\n",
       "666   https://pbs.twimg.com/media/Cvi2FiKWgAAif1u.jpg      2.0  malinois   \n",
       "2245  https://pbs.twimg.com/media/CUTN5V4XAAAIa4R.jpg      1.0  malamute   \n",
       "\n",
       "     confidence_level  \n",
       "666          0.000153  \n",
       "2245         0.088530  \n",
       "\n",
       "[2 rows x 23 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping clumns ['p1', 'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog']\n",
    "df_all = df_all.drop(['p1', 'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog'], axis=1)\n",
    "\n",
    "# Verifying the absence of the columns dropped\n",
    "df_all.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28ba914b",
   "metadata": {},
   "source": [
    "## Issue #4\n",
    "*.**\"img_num\"** and **\"expanded_urls\"** column may not be relevant for analysis*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ff9311",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *  *Drop the columns **\"ing_num\"** and **\"expanded_urls\"** *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be5afecb",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "d53e3d52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping img_num column\n",
    "df_all = df_all.drop(['img_num', 'expanded_urls'], axis =1)\n",
    "\n",
    "# verify column is dropped with function created above\n",
    "check('img_num', df_all), check('expanded_urls',df_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "969ac800",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aee145b0",
   "metadata": {},
   "source": [
    "## Issue #5\n",
    "*doggo, floofer, pupper and puppo occupying 4 separate columns*\n",
    "*  *A quick visual assessment of the various stages using the filter function in an excel sheet on the original twitter archive dataset reveals there are some breed of dogs that are between transitions, that is, have 2 stages they are inolved into*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "472cbc5a",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *  *Melt all the columns into one giving it a name of dog_stage*\n",
    "    >  >* *change any instance of None to **\"0\"** and otherwise to **\"1\"** in all the 4 column stages.*\n",
    "    >  >* *Create a new dataframe called **\"no_stage\"** which would be equal to the sum of all instances with stage being equal to **\"0\"**.*\n",
    "    >  >* *Create a function that would use **\"the name of the stage if it exist\"** and **\"none\"** if otherwise.*\n",
    "    >  >* *Create a new dataframe called **\"dog_stage\"** of whose values would be based on the function described*\n",
    "        >  >* *Drop the original four stages columns that is, **'doggo', 'floofer', 'pupper', 'puppo'** as well as the **\"no_stage\"** dataframe created..*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de9331f8",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "418cd76a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1754\n",
       "1     308\n",
       "2      13\n",
       "Name: no_stage, dtype: int64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace all instances of a stage name with 1, and 'None' with 0\n",
    "no_stage = lambda x: 0 if x == 'None' else 1\n",
    "\n",
    "df_all.doggo = df_all.doggo.apply(no_stage)\n",
    "df_all.floofer = df_all.floofer.apply(no_stage)\n",
    "df_all.pupper = df_all.pupper.apply(no_stage)\n",
    "df_all.puppo = df_all.puppo.apply(no_stage)\n",
    "\n",
    "# adding all instances of the value \"None\" for each of the stage colulmns\n",
    "df_all['no_stage'] = df_all['doggo'] + df_all['floofer'] + \\\n",
    "                        df_all['pupper'] + df_all['puppo']\n",
    "\n",
    "# have a look at what we have before we continue...\n",
    "df_all['no_stage'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195684fb",
   "metadata": {},
   "source": [
    "*The stats displayed implies there are **\"1754\"** instances where there were no dog stages assigned as values, while for **\"308\"** times, a dog was transitioning, but as indicated above, using visual assessment, the instances with a dog being between transitions, this time arround 2 transitions is clearly indicated as **\"13\"** dogs being involved. These may be of any combination of two of any of the 4 dog stages*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "94da6f6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# It should be notted that, at this point, all stages if present in cloumn is now == 1\n",
    "# hence function to convert the 1's if present to the stage names is defined as: \n",
    "\n",
    "# source = https://stackoverflow.com/questions/21702342/creating-a-new-column-based-on-if-elif-else-condition\n",
    "def f(row):\n",
    "    if row['floofer'] == 1:\n",
    "        stage = 'floofer'\n",
    "    elif row['puppo'] == 1:\n",
    "        stage = 'puppo'\n",
    "    elif row['doggo'] == 1:\n",
    "        stage = 'doggo'\n",
    "    elif row['pupper'] == 1:\n",
    "        stage = 'pupper'\n",
    "    else:\n",
    "        stage = 'none'\n",
    "    return stage\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "a833b16a",
   "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>rating_numerator</th>\n",
       "      <th>...</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>breed</th>\n",
       "      <th>confidence_level</th>\n",
       "      <th>no_stage</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>673317986296586240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-06 01:48:12 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>Take a moment and appreciate how these two dogs fell asleep. Simply magnificent. 10/10 for both https://t.co/juX48bWpng</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CVgbIobUYAEaeI3.jpg</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.384099</td>\n",
       "      <td>0</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>773547596996571136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-07 15:44:53 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Chelsea. She forgot how to dog. 11/10 get it together pupper https://t.co/nBJ5RE4yHb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7126.0</td>\n",
       "      <td>24553.0</td>\n",
       "      <td>https://pbs.twimg.com/media/Crwxb5yWgAAX5P_.jpg</td>\n",
       "      <td>Norwegian_elkhound</td>\n",
       "      <td>0.372202</td>\n",
       "      <td>1</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "1969  673317986296586240                    NaN                  NaN   \n",
       "793   773547596996571136                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "1969  2015-12-06 01:48:12 +0000   \n",
       "793   2016-09-07 15:44:53 +0000   \n",
       "\n",
       "                                                                                  source  \\\n",
       "1969  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "793   <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                         text  \\\n",
       "1969  Take a moment and appreciate how these two dogs fell asleep. Simply magnificent. 10/10 for both https://t.co/juX48bWpng   \n",
       "793                              This is Chelsea. She forgot how to dog. 11/10 get it together pupper https://t.co/nBJ5RE4yHb   \n",
       "\n",
       "      retweeted_status_id  retweeted_status_user_id  \\\n",
       "1969                  NaN                       NaN   \n",
       "793                   NaN                       NaN   \n",
       "\n",
       "     retweeted_status_timestamp  rating_numerator  ...  floofer pupper  puppo  \\\n",
       "1969                        NaN                10  ...        0      0      0   \n",
       "793                         NaN                11  ...        0      1      0   \n",
       "\n",
       "      retweet_count  favorite_count  \\\n",
       "1969          293.0           924.0   \n",
       "793          7126.0         24553.0   \n",
       "\n",
       "                                              jpg_url               breed  \\\n",
       "1969  https://pbs.twimg.com/media/CVgbIobUYAEaeI3.jpg  miniature_pinscher   \n",
       "793   https://pbs.twimg.com/media/Crwxb5yWgAAX5P_.jpg  Norwegian_elkhound   \n",
       "\n",
       "      confidence_level no_stage dog_stage  \n",
       "1969          0.384099        0      none  \n",
       "793           0.372202        1    pupper  \n",
       "\n",
       "[2 rows x 23 columns]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a new dataframe called \"dog_stage\" and applying the function above to it\n",
    "df_all['dog_stage'] = df_all.apply(f, axis=1)\n",
    "\n",
    "# Verifying change made\n",
    "df_all.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "b31378d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the 'doggo', 'floofer', 'pupper', 'puppo', and the 'no_stage' column\n",
    "df_all = df_all.drop(['doggo', 'floofer', 'pupper', 'puppo', 'no_stage'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd193c7",
   "metadata": {},
   "source": [
    "*.**# NB:** by helping us come up with the **\"stage\"** column, the **\"no_stage\"** column only tells us the the number of transitions or stages a dog was into which would not be needed in our analysis*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "c0ed1cf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2075 entries, 0 to 2355\n",
      "Data columns (total 18 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2075 non-null   int64  \n",
      " 1   in_reply_to_status_id       23 non-null     float64\n",
      " 2   in_reply_to_user_id         23 non-null     float64\n",
      " 3   timestamp                   2075 non-null   object \n",
      " 4   source                      2075 non-null   object \n",
      " 5   text                        2075 non-null   object \n",
      " 6   retweeted_status_id         81 non-null     float64\n",
      " 7   retweeted_status_user_id    81 non-null     float64\n",
      " 8   retweeted_status_timestamp  81 non-null     object \n",
      " 9   rating_numerator            2075 non-null   int64  \n",
      " 10  rating_denominator          2075 non-null   int64  \n",
      " 11  name                        2075 non-null   object \n",
      " 12  retweet_count               2073 non-null   float64\n",
      " 13  favorite_count              2073 non-null   float64\n",
      " 14  jpg_url                     2075 non-null   object \n",
      " 15  breed                       2075 non-null   object \n",
      " 16  confidence_level            2075 non-null   float64\n",
      " 17  dog_stage                   2075 non-null   object \n",
      "dtypes: float64(7), int64(3), object(8)\n",
      "memory usage: 308.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# verifying changes made\n",
    "df_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de5c1469",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "27e98336",
   "metadata": {},
   "source": [
    "## Issue #6\n",
    "*the **\"tweet ids\"** are integers in all the datasets*\n",
    "*  *Since the tweet id's would not be needed for any computations, it is would be advisable to convert it to a datatype of which the computer would not have to spend extra effort for its computation.*\n",
    "*  *All the datasets are now merged together hence the only tweet id now would be the one on th combined dataset, that is the df_all dataframe.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9acfc4f",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *  *Check the datatype for the **\"tweet_id\"** column*\n",
    ">  *  *Change the **\"tweet_id\"** datatype to string type object if not*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d81659ba",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "3c93bdf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking datatype of the tweet_id column\n",
    "df_all.tweet_id.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "48ba7959",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting tweet_id datatype to a str\n",
    "df_all.tweet_id = df_all.tweet_id.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "e879bbaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifying change made to tweet_id datatype\n",
    "df_all.tweet_id.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8b914d1",
   "metadata": {},
   "source": [
    "# Issue #7\n",
    "*3 **\"retweets\"** columns in the df_archive with 181 non-null values each*\n",
    "*  *The value **\"181\"** at this point has been reduced significantly*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be660d64",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *Exclude rows whose **'retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp'** have values*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9417e331",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "7d55af28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excluding rows with retweeted_status_id not equall to null\n",
    "df_all = df_all[~(df_all.retweeted_status_id.notnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "1f579aaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1994 entries, 0 to 2355\n",
      "Data columns (total 18 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    1994 non-null   object \n",
      " 1   in_reply_to_status_id       23 non-null     float64\n",
      " 2   in_reply_to_user_id         23 non-null     float64\n",
      " 3   timestamp                   1994 non-null   object \n",
      " 4   source                      1994 non-null   object \n",
      " 5   text                        1994 non-null   object \n",
      " 6   retweeted_status_id         0 non-null      float64\n",
      " 7   retweeted_status_user_id    0 non-null      float64\n",
      " 8   retweeted_status_timestamp  0 non-null      object \n",
      " 9   rating_numerator            1994 non-null   int64  \n",
      " 10  rating_denominator          1994 non-null   int64  \n",
      " 11  name                        1994 non-null   object \n",
      " 12  retweet_count               1994 non-null   float64\n",
      " 13  favorite_count              1994 non-null   float64\n",
      " 14  jpg_url                     1994 non-null   object \n",
      " 15  breed                       1994 non-null   object \n",
      " 16  confidence_level            1994 non-null   float64\n",
      " 17  dog_stage                   1994 non-null   object \n",
      "dtypes: float64(7), int64(2), object(9)\n",
      "memory usage: 296.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# veruifying changes made\n",
    "df_all.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eaf76e2",
   "metadata": {},
   "source": [
    "*The **\"\".info\"** method reveals both **'retweeted_status_timestamp' and 'retweeted_status_user_id'** are in a way related to the **'retweeted_status_id'** hence also got affected in the process.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f402f2",
   "metadata": {},
   "source": [
    "*All the retweets columns at this moment has no values hence we would want to drop those columns to tidy up our dataset*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92feeb45",
   "metadata": {},
   "source": [
    "**Dropping all retweeted columns**\n",
    ">  *It should be notted that, this is a **tidy Issue** which could be handled best at this point of the project*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "114a8a79",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *Drop the 'retweeted_status_id', 'retweeted_status_user_id' and the 'retweeted_status_timestamp' columns*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6141bfe0",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "2854d944",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping columns 'retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp'\n",
    "df_all = df_all.drop(['retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "9477fb5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1994 entries, 0 to 2355\n",
      "Data columns (total 15 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   tweet_id               1994 non-null   object \n",
      " 1   in_reply_to_status_id  23 non-null     float64\n",
      " 2   in_reply_to_user_id    23 non-null     float64\n",
      " 3   timestamp              1994 non-null   object \n",
      " 4   source                 1994 non-null   object \n",
      " 5   text                   1994 non-null   object \n",
      " 6   rating_numerator       1994 non-null   int64  \n",
      " 7   rating_denominator     1994 non-null   int64  \n",
      " 8   name                   1994 non-null   object \n",
      " 9   retweet_count          1994 non-null   float64\n",
      " 10  favorite_count         1994 non-null   float64\n",
      " 11  jpg_url                1994 non-null   object \n",
      " 12  breed                  1994 non-null   object \n",
      " 13  confidence_level       1994 non-null   float64\n",
      " 14  dog_stage              1994 non-null   object \n",
      "dtypes: float64(5), int64(2), object(8)\n",
      "memory usage: 249.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#verifying changes made\n",
    "df_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13020cb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9a87e029",
   "metadata": {},
   "source": [
    "# Issue #8\n",
    "\n",
    "*2 **\"reply_retweets\"** columns in the df_archive with 78 non-null values each*\n",
    "*  *This value at this point has significantly reduced*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "959435be",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *Drop **'in_reply_to_status_id' and 'in_reply_to_user_id'** columns*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc0dd46",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "4d021c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop columns 'in_reply_to_status_id' and 'in_reply_to_user_id'\n",
    "df_all = df_all.drop(['in_reply_to_status_id', 'in_reply_to_user_id'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "c1a07589",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1994 entries, 0 to 2355\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   tweet_id            1994 non-null   object \n",
      " 1   timestamp           1994 non-null   object \n",
      " 2   source              1994 non-null   object \n",
      " 3   text                1994 non-null   object \n",
      " 4   rating_numerator    1994 non-null   int64  \n",
      " 5   rating_denominator  1994 non-null   int64  \n",
      " 6   name                1994 non-null   object \n",
      " 7   retweet_count       1994 non-null   float64\n",
      " 8   favorite_count      1994 non-null   float64\n",
      " 9   jpg_url             1994 non-null   object \n",
      " 10  breed               1994 non-null   object \n",
      " 11  confidence_level    1994 non-null   float64\n",
      " 12  dog_stage           1994 non-null   object \n",
      "dtypes: float64(3), int64(2), object(8)\n",
      "memory usage: 218.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# verifying changes made\n",
    "df_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7de7cf22",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e144d3e6",
   "metadata": {},
   "source": [
    "# Issue #9\n",
    "\n",
    "*strange values in **\"rating_denomimator\"** of df_archive table*\n",
    "*  *In as much as the ratings of **\"WeRateDogs\"** is questioned by most people, it seems to have a constant denominator value of **\"10\"** for most of its ratings. In this project we would want to maintain a column rating of **\"10\"** for all our rating_denominators for a much comprehensible understanding of our data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "f9f323bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>rating_numerator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.532096</td>\n",
       "      <td>12.280843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.320710</td>\n",
       "      <td>41.497718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>170.000000</td>\n",
       "      <td>1776.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rating_denominator  rating_numerator\n",
       "count         1994.000000       1994.000000\n",
       "mean            10.532096         12.280843\n",
       "std              7.320710         41.497718\n",
       "min              2.000000          0.000000\n",
       "25%             10.000000         10.000000\n",
       "50%             10.000000         11.000000\n",
       "75%             10.000000         12.000000\n",
       "max            170.000000       1776.000000"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# brief description of the rating columns\n",
    "df_all[['rating_denominator','rating_numerator']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c2fdf35",
   "metadata": {},
   "source": [
    ">  * *The describe function shows there are vallues within the denominator greater than or or less than **\"10\"** with most of them being **\"10\"**.*\n",
    ">  * *Lets have a look at th values not equall to **\"10\"** and the number of times they appear*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "cc8b4906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating_denominator\n",
       "50                    3\n",
       "11                    2\n",
       "80                    2\n",
       "2                     1\n",
       "7                     1\n",
       "20                    1\n",
       "40                    1\n",
       "70                    1\n",
       "90                    1\n",
       "110                   1\n",
       "120                   1\n",
       "130                   1\n",
       "150                   1\n",
       "170                   1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding all denominators greator than 10\n",
    "df_all[['rating_denominator']].query('rating_denominator != 10').value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0040e6e",
   "metadata": {},
   "source": [
    "*Lets now eliminate these rows of whose denominator values are not equal to **\"10\"**.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff40c1be",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *  *Drop row whose rating_denomintor value is not equal to **\"10\"**.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7f28269",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "b6f32b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excluding rating denominators not equal to 10\n",
    "df_all = df_all[~(df_all.rating_denominator != 10)] # where ~ means not or exclude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "bb2b4bb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify  the sum of denominator values not equal to 10 present\n",
    "df_all[['rating_denominator']].query('rating_denominator != 10').value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5375a74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a62c65ae",
   "metadata": {},
   "source": [
    "# Issue #10\n",
    "*strange values in **\"rating numerator\"** of df_archive table*\n",
    "*  *We would want to maintain a rating_numerator maximum of 15 for our data.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa65a498",
   "metadata": {},
   "source": [
    "*The description above shows there are rating_numerators greater than the value **\"15\"** of which we are interested in*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d0f8e0",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *Drop 'ratings_numerator' values greater than **\"15\"**.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50f0dd2f",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "8cea6121",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping rating numerators greater than 15\n",
    "df_all = df_all[~(df_all.rating_numerator > 15)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "5d18dfb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for changes made\n",
    "df_all[['rating_numerator']].query('rating_numerator > 15').value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "ee5e720b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating_numerator  rating_denominator\n",
       "12                10                    450\n",
       "10                10                    419\n",
       "11                10                    396\n",
       "13                10                    261\n",
       "9                 10                    150\n",
       "8                 10                     95\n",
       "7                 10                     51\n",
       "14                10                     35\n",
       "5                 10                     33\n",
       "6                 10                     32\n",
       "3                 10                     19\n",
       "4                 10                     15\n",
       "2                 10                      9\n",
       "1                 10                      4\n",
       "0                 10                      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making further verification on numerator and denominator values\n",
    "df_all[['rating_numerator','rating_denominator']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce27c2a8",
   "metadata": {},
   "source": [
    "*A visual assessment of the dataframe above shows a unique denominator value of **\"10\"** and no rating numerator not grater than **\"15\"** as required*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c473f4",
   "metadata": {},
   "source": [
    "# Issue #11\n",
    "*.**\"wierd names\"** in the names columns in df_archive table such as **\"a, an, e,t,c\"**.*\n",
    "*  *A visual assessment of the dataset in the twitter csv file shown most these weird names begun with small letters and the those that did not were much meaningful to be names in my opinion*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "6c12faec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['such',\n",
       " 'a',\n",
       " 'quite',\n",
       " 'quite',\n",
       " 'one',\n",
       " 'incredibly',\n",
       " 'a',\n",
       " 'a',\n",
       " 'very',\n",
       " 'my',\n",
       " 'one',\n",
       " 'not',\n",
       " 'his',\n",
       " 'one',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'an',\n",
       " 'very',\n",
       " 'a',\n",
       " 'just',\n",
       " 'getting',\n",
       " 'very',\n",
       " 'unacceptable',\n",
       " 'all',\n",
       " 'a',\n",
       " 'a',\n",
       " 'infuriating',\n",
       " 'a',\n",
       " 'a',\n",
       " 'an',\n",
       " 'a',\n",
       " 'a',\n",
       " 'very',\n",
       " 'getting',\n",
       " 'just',\n",
       " 'a',\n",
       " 'the',\n",
       " 'the',\n",
       " 'actually',\n",
       " 'by',\n",
       " 'a',\n",
       " 'officially',\n",
       " 'a',\n",
       " 'the',\n",
       " 'the',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'one',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'light',\n",
       " 'just',\n",
       " 'space',\n",
       " 'a',\n",
       " 'the',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'an',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'quite',\n",
       " 'a',\n",
       " 'an',\n",
       " 'a',\n",
       " 'the',\n",
       " 'the',\n",
       " 'a',\n",
       " 'a',\n",
       " 'an',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a',\n",
       " 'a']"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', None) #sets columns width to unlimited\n",
    "\n",
    "# displaying the names whose values starts with a lower case letter\n",
    "list(df_all[df_all['name'].str[0].str.islower()]['name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76ca8a68",
   "metadata": {},
   "source": [
    ">  * *The data displayed shows that all the names that starts with the lowercase letters seemed incorrect*\n",
    ">  * *As a result, we would want to replace all such names with a value of \"None\"*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73835e3d",
   "metadata": {},
   "source": [
    "**Lets find the total number of the starting with lower case values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "629d65a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df_all[df_all['name'].str[0].str.islower()]['name']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "5b4b66b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "532"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fnding the total number of names equivalent to \"None\"\n",
    "len(df_all[['name']].query('name == \"None\"'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "221ff80f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "627"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total of names equivalent to \"None\" and names whose starting letters are lowercase\n",
    "len(df_all[['name']].query('name == \"None\"')) + len(list(df_all[df_all['name'].str[0].str.islower()]['name']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5452c4dc",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *replace all names that starts with a lowercase letters to **\"None\"**.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c419a6",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "30798f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# soure: https://pythonguides.com/pandas-replace-multiple-values/\n",
    "\n",
    "#  where name column value starts with lowercase, replace with none\n",
    "df_all.loc[(df_all['name'].str.contains(r'^[a-z]')), 'name'] = 'None' #(r'^[a-z]'), a regex to check leading letters included in lowercase a to z\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "abb95435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying column values in \"name\" that starts with a lowercase letter\n",
    "len(list(df_all[df_all['name'].str[0].str.islower()]['name']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "71858ef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "627"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking total number of name column with a value of \"None\"\n",
    "len(df_all[['name']].query('name == \"None\"'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "c89633d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['None',\n",
       " 'Charlie',\n",
       " 'Cooper',\n",
       " 'Oliver',\n",
       " 'Lucy',\n",
       " 'Penny',\n",
       " 'Tucker',\n",
       " 'Winston',\n",
       " 'Sadie',\n",
       " 'Daisy',\n",
       " 'Toby',\n",
       " 'Lola',\n",
       " 'Jax',\n",
       " 'Bo',\n",
       " 'Stanley',\n",
       " 'Bella',\n",
       " 'Koda',\n",
       " 'Dave',\n",
       " 'Scout',\n",
       " 'Leo',\n",
       " 'Bailey',\n",
       " 'Buddy',\n",
       " 'Rusty',\n",
       " 'Oscar',\n",
       " 'Milo',\n",
       " 'Louis',\n",
       " 'Chester',\n",
       " 'Phil',\n",
       " 'Brody',\n",
       " 'Oakley',\n",
       " 'Bear',\n",
       " 'Clarence',\n",
       " 'Finn',\n",
       " 'Jerry',\n",
       " 'Alfie',\n",
       " 'Dexter',\n",
       " 'Larry',\n",
       " 'Reggie',\n",
       " 'Scooter',\n",
       " 'Winnie',\n",
       " 'George',\n",
       " 'Bentley',\n",
       " 'Clark',\n",
       " 'Jack',\n",
       " 'Duke',\n",
       " 'Derek',\n",
       " 'Cassie',\n",
       " 'Chip',\n",
       " 'Gary',\n",
       " 'Maggie',\n",
       " 'Gus',\n",
       " 'Archie',\n",
       " 'Moose',\n",
       " 'Sophie',\n",
       " 'Wyatt',\n",
       " 'Riley',\n",
       " 'Earl',\n",
       " 'Bruce',\n",
       " 'Calvin',\n",
       " 'Reese',\n",
       " 'Sunny',\n",
       " 'Nala',\n",
       " 'Kyle',\n",
       " 'Waffles',\n",
       " 'Lily',\n",
       " 'Loki',\n",
       " 'Wallace',\n",
       " 'Olive',\n",
       " 'Malcolm',\n",
       " 'Jeffrey',\n",
       " 'Mia',\n",
       " 'Samson',\n",
       " 'Gerald',\n",
       " 'Zeke',\n",
       " 'Hank',\n",
       " 'Max',\n",
       " 'Wilson',\n",
       " 'Vincent',\n",
       " 'Zoey',\n",
       " 'Sammy',\n",
       " 'Rosie',\n",
       " 'Ellie',\n",
       " 'Beau',\n",
       " 'Louie',\n",
       " 'Ruby',\n",
       " 'Otis',\n",
       " 'Boomer',\n",
       " 'Jimothy',\n",
       " 'Shadow',\n",
       " 'Sebastian',\n",
       " 'Steven',\n",
       " 'Walter',\n",
       " 'Gizmo',\n",
       " 'Griffin',\n",
       " 'Lincoln',\n",
       " 'Wally',\n",
       " 'Sampson',\n",
       " 'Chipson',\n",
       " 'Lou',\n",
       " 'Mister',\n",
       " 'Frank',\n",
       " 'Herm',\n",
       " 'Fiona',\n",
       " 'Bernie',\n",
       " 'Jackson',\n",
       " 'Sugar',\n",
       " 'Percy',\n",
       " 'Linda',\n",
       " 'Kirby',\n",
       " 'Kenneth',\n",
       " 'Charles',\n",
       " 'Rory',\n",
       " 'Benji',\n",
       " 'Finley',\n",
       " 'Maximus',\n",
       " 'Abby',\n",
       " 'Maxaroni',\n",
       " 'Frankie',\n",
       " 'Solomon',\n",
       " 'Penelope',\n",
       " 'Jiminy',\n",
       " 'Doug',\n",
       " 'Oliviér',\n",
       " 'Theodore',\n",
       " 'Dash',\n",
       " 'Kilo',\n",
       " 'Sansa',\n",
       " 'Peaches',\n",
       " 'Tyr',\n",
       " 'Opal',\n",
       " 'Titan',\n",
       " 'Olivia',\n",
       " 'Atlas',\n",
       " 'Baxter',\n",
       " 'Rocky',\n",
       " 'Misty',\n",
       " 'Albert',\n",
       " 'Doc',\n",
       " 'Hammond',\n",
       " 'Moe',\n",
       " 'Hunter',\n",
       " 'Sandy',\n",
       " 'Patrick',\n",
       " 'Ollie',\n",
       " 'Lennon',\n",
       " 'Watson',\n",
       " 'Django',\n",
       " 'Jeph',\n",
       " 'Juno',\n",
       " 'Eli',\n",
       " 'Lenny',\n",
       " 'Phred',\n",
       " 'Gabe',\n",
       " 'Jimison',\n",
       " 'Piper',\n",
       " 'Luca',\n",
       " 'Dakota',\n",
       " 'Rufus',\n",
       " 'Herald',\n",
       " 'Marley',\n",
       " 'Kreg',\n",
       " 'Brad',\n",
       " 'Roosevelt',\n",
       " 'Keith',\n",
       " 'Ash',\n",
       " 'Phineas',\n",
       " 'Ava',\n",
       " 'Pippa',\n",
       " 'Churlie',\n",
       " 'Coco',\n",
       " 'Paisley',\n",
       " 'Albus',\n",
       " 'Neptune',\n",
       " 'Belle',\n",
       " 'Panda',\n",
       " 'Axel',\n",
       " 'Hobbes',\n",
       " 'Bisquick',\n",
       " 'Lorenzo',\n",
       " 'Carl',\n",
       " 'Reginald',\n",
       " 'Trooper',\n",
       " 'Sarge',\n",
       " 'Kreggory',\n",
       " 'Alice',\n",
       " 'Colby',\n",
       " 'Aspen',\n",
       " 'Harold',\n",
       " 'Cody',\n",
       " 'Raymond',\n",
       " 'Flávio',\n",
       " 'Franklin',\n",
       " 'Ted',\n",
       " 'Chuckles',\n",
       " 'Klevin',\n",
       " 'Kevin',\n",
       " 'Yogi',\n",
       " 'Thumas',\n",
       " 'Curtis',\n",
       " 'Calbert',\n",
       " 'Remington',\n",
       " 'Elliot',\n",
       " 'Jesse',\n",
       " 'Romeo',\n",
       " 'Bubbles',\n",
       " 'Nelly',\n",
       " 'Benedict',\n",
       " 'Cupcake',\n",
       " 'Smokey',\n",
       " 'Cash',\n",
       " 'Chet',\n",
       " 'Bob',\n",
       " 'Terry',\n",
       " 'CeCe',\n",
       " 'Odie',\n",
       " 'Indie',\n",
       " 'Crystal',\n",
       " 'Luna',\n",
       " 'Pickles',\n",
       " 'Coops',\n",
       " 'Cuddles',\n",
       " 'Banjo',\n",
       " 'Farfle',\n",
       " 'Jiminus',\n",
       " 'Freddery',\n",
       " 'Harper',\n",
       " 'Grady',\n",
       " 'Keurig',\n",
       " 'Kobe',\n",
       " 'Claude',\n",
       " 'Clarkus',\n",
       " 'Brandy',\n",
       " 'Hazel',\n",
       " 'Finnegus',\n",
       " 'Taco',\n",
       " 'Tiger',\n",
       " 'Lupe',\n",
       " 'Bodie',\n",
       " 'Kulet',\n",
       " 'Kathmandu',\n",
       " 'Katie',\n",
       " 'Kara',\n",
       " 'Tino',\n",
       " 'Adele',\n",
       " 'Zara',\n",
       " 'Obi',\n",
       " 'Lulu',\n",
       " 'Jessiga',\n",
       " 'Reagan',\n",
       " 'Karma',\n",
       " 'Carly',\n",
       " 'Asher',\n",
       " 'Glacier',\n",
       " 'Chuck',\n",
       " 'Billy',\n",
       " 'Blipson',\n",
       " 'Champ',\n",
       " 'Brian',\n",
       " 'Ozzie',\n",
       " 'Griswold',\n",
       " 'Cheesy',\n",
       " 'Moofasa',\n",
       " 'Reptar',\n",
       " 'Vince',\n",
       " 'Walker',\n",
       " 'Apollo',\n",
       " 'Ole',\n",
       " 'Rodney',\n",
       " 'Toffee',\n",
       " 'Ulysses',\n",
       " 'Malikai',\n",
       " 'Tess',\n",
       " 'Todo',\n",
       " 'Bobble',\n",
       " 'River',\n",
       " 'Trevith',\n",
       " 'Berb',\n",
       " 'Carter',\n",
       " 'Jebberson',\n",
       " 'Colin',\n",
       " 'Joey',\n",
       " 'Dunkin',\n",
       " 'Ambrose',\n",
       " 'Bobby',\n",
       " 'Rilo',\n",
       " 'Kingsley',\n",
       " 'Derby',\n",
       " 'Frönq',\n",
       " 'Pubert',\n",
       " 'Brockly',\n",
       " 'Socks',\n",
       " 'Kaia',\n",
       " 'Hamrick',\n",
       " 'Daniel',\n",
       " 'Rambo',\n",
       " 'Trip',\n",
       " 'Lucky',\n",
       " 'Fiji',\n",
       " 'Ricky',\n",
       " 'Richie',\n",
       " 'Lolo',\n",
       " 'Fynn',\n",
       " 'Durg',\n",
       " 'Molly',\n",
       " 'Bilbo',\n",
       " 'Covach',\n",
       " 'Vinscent',\n",
       " 'Geoff',\n",
       " 'Maxwell',\n",
       " 'Coopson',\n",
       " 'Yoda',\n",
       " 'Millie',\n",
       " 'Cedrick',\n",
       " 'Crouton',\n",
       " 'Eriq',\n",
       " 'Lizzie',\n",
       " 'Blakely',\n",
       " 'Ralphson',\n",
       " 'Edgar',\n",
       " 'Perry',\n",
       " 'Bode',\n",
       " 'Bruiser',\n",
       " 'Terrenth',\n",
       " 'Oreo',\n",
       " 'Charl',\n",
       " 'Gerbald',\n",
       " 'Miley',\n",
       " 'Hubertson',\n",
       " 'Petrick',\n",
       " 'Fillup',\n",
       " 'Brooks',\n",
       " 'Marty',\n",
       " 'Lorelei',\n",
       " 'Tupawc',\n",
       " 'Chesterson',\n",
       " 'Birf',\n",
       " 'Crimson',\n",
       " 'Eazy',\n",
       " 'Amber',\n",
       " 'Lucia',\n",
       " 'Mona',\n",
       " 'Dotsy',\n",
       " 'Herschel',\n",
       " 'Gordon',\n",
       " 'Tyrone',\n",
       " 'Kramer',\n",
       " 'Marq',\n",
       " 'Murphy',\n",
       " 'Horace',\n",
       " 'Rodman',\n",
       " 'Hector',\n",
       " 'Randall',\n",
       " 'Tommy',\n",
       " 'Ben',\n",
       " 'Raphael',\n",
       " 'Julio',\n",
       " 'Andru',\n",
       " 'Kloey',\n",
       " 'Shawwn',\n",
       " 'Skye',\n",
       " 'Kollin',\n",
       " 'Ronduh',\n",
       " 'Billl',\n",
       " 'Saydee',\n",
       " 'Dug',\n",
       " 'Tessa',\n",
       " 'Sully',\n",
       " 'Kirk',\n",
       " 'Ralf',\n",
       " 'Clarq',\n",
       " 'Jaspers',\n",
       " 'Samsom',\n",
       " 'Terrance',\n",
       " 'Liam',\n",
       " 'Fabio',\n",
       " 'Chaz',\n",
       " 'Sandra',\n",
       " 'Andy',\n",
       " 'Mason',\n",
       " 'Trigger',\n",
       " 'Antony',\n",
       " 'Creg',\n",
       " 'Traviss',\n",
       " 'Gin',\n",
       " 'Jeffrie',\n",
       " 'Danny',\n",
       " 'Ester',\n",
       " 'Pluto',\n",
       " 'Bloo',\n",
       " 'Edd',\n",
       " 'Paull',\n",
       " 'Sam',\n",
       " 'Willy',\n",
       " 'Herb',\n",
       " 'Damon',\n",
       " 'Peanut',\n",
       " 'Nigel',\n",
       " 'Butters',\n",
       " 'Harrison',\n",
       " 'Jeremy',\n",
       " 'Goliath',\n",
       " 'Erik',\n",
       " 'Tedrick',\n",
       " 'Shaggy',\n",
       " 'Filup',\n",
       " 'Kial',\n",
       " 'Naphaniel',\n",
       " 'Dook',\n",
       " 'Hall',\n",
       " 'Philippe',\n",
       " 'Biden',\n",
       " 'Fwed',\n",
       " 'Genevieve',\n",
       " 'Joshwa',\n",
       " 'Timison',\n",
       " 'Bradlay',\n",
       " 'Pipsy',\n",
       " 'Clybe',\n",
       " 'Keet',\n",
       " 'Carll',\n",
       " 'Jockson',\n",
       " 'Josep',\n",
       " 'Lugan',\n",
       " 'Stu',\n",
       " 'Cleopatricia',\n",
       " 'Jaycob',\n",
       " 'Skittles',\n",
       " 'Lambeau',\n",
       " 'Ruffles',\n",
       " 'Amélie',\n",
       " 'Bobb',\n",
       " 'Banditt',\n",
       " 'Kevon',\n",
       " 'Winifred',\n",
       " 'Hanz',\n",
       " 'Zeek',\n",
       " 'Timofy',\n",
       " 'Maks',\n",
       " 'Jomathan',\n",
       " 'Kallie',\n",
       " 'Marvin',\n",
       " 'Spark',\n",
       " 'Gòrdón',\n",
       " 'Jo',\n",
       " 'DayZ',\n",
       " 'Jareld',\n",
       " 'Torque',\n",
       " 'Ron',\n",
       " 'Sage',\n",
       " 'Amy',\n",
       " 'Jett',\n",
       " 'Tyrus',\n",
       " 'Godzilla',\n",
       " 'Vinnie',\n",
       " 'Bert',\n",
       " 'Striker',\n",
       " 'Donny',\n",
       " 'Pepper',\n",
       " 'Buddah',\n",
       " 'Arnold',\n",
       " 'Zuzu',\n",
       " 'Mollie',\n",
       " 'Laela',\n",
       " 'Tedders',\n",
       " 'Superpup',\n",
       " 'Rufio',\n",
       " 'Jeb',\n",
       " 'Olaf',\n",
       " 'Jonah',\n",
       " 'Chesney',\n",
       " 'Kenny',\n",
       " 'Henry',\n",
       " 'Bobbay',\n",
       " 'Karl',\n",
       " 'Chuq',\n",
       " 'Holly',\n",
       " 'Juckson',\n",
       " 'Kawhi',\n",
       " 'Ozzy',\n",
       " 'Emmie',\n",
       " 'Willie',\n",
       " 'Rinna',\n",
       " 'Mike',\n",
       " 'William',\n",
       " 'Dwight',\n",
       " 'Evy',\n",
       " 'Hurley',\n",
       " 'Rubio',\n",
       " 'Chompsky',\n",
       " 'Tug',\n",
       " 'Tango',\n",
       " 'Grizz',\n",
       " 'Jerome',\n",
       " 'Crumpet',\n",
       " 'Jessifer',\n",
       " 'Ralph',\n",
       " 'Humphrey',\n",
       " 'Tassy',\n",
       " 'Mitch',\n",
       " 'Kaiya',\n",
       " 'Acro',\n",
       " 'Aiden',\n",
       " 'Pippin',\n",
       " 'Rolf',\n",
       " 'Snickers',\n",
       " 'Ridley',\n",
       " 'Cal',\n",
       " 'Bradley',\n",
       " 'Bubba',\n",
       " 'Tuco',\n",
       " 'Patch',\n",
       " 'Mojo',\n",
       " 'Batdog',\n",
       " 'Dylan',\n",
       " 'Mark',\n",
       " 'JD',\n",
       " 'Alejandro',\n",
       " 'Scruffers',\n",
       " 'Pip',\n",
       " 'Julius',\n",
       " 'Tanner',\n",
       " 'Sparky',\n",
       " 'Anthony',\n",
       " 'Franq',\n",
       " 'Jazz',\n",
       " 'Jackie',\n",
       " 'Kenzie',\n",
       " 'Obie',\n",
       " 'Dot',\n",
       " 'Shnuggles',\n",
       " 'Kendall',\n",
       " 'Jeffri',\n",
       " 'Steve',\n",
       " 'Eve',\n",
       " 'Mac',\n",
       " 'Fletcher',\n",
       " 'Pumpkin',\n",
       " 'Darby',\n",
       " 'Schnozz',\n",
       " 'Gustaf',\n",
       " 'Cheryl',\n",
       " 'Ed',\n",
       " 'Leonidas',\n",
       " 'Norman',\n",
       " 'Caryl',\n",
       " 'Scott',\n",
       " 'Taz',\n",
       " 'Cecil',\n",
       " 'Bell',\n",
       " 'Tater',\n",
       " 'Mo',\n",
       " 'Pablo',\n",
       " 'Crawford',\n",
       " 'Duchess',\n",
       " 'Harlso',\n",
       " 'Sundance',\n",
       " 'Flash',\n",
       " 'Howie',\n",
       " 'Jazzy',\n",
       " 'Anna',\n",
       " 'Wafer',\n",
       " 'Tom',\n",
       " 'Florence',\n",
       " 'Autumn',\n",
       " 'Dido',\n",
       " 'Eugene',\n",
       " 'Ken',\n",
       " 'Strudel',\n",
       " 'Tebow',\n",
       " 'Chloe',\n",
       " 'Sweet',\n",
       " 'Ike',\n",
       " 'Orion',\n",
       " 'Pilot',\n",
       " 'Smiley',\n",
       " 'Miguel',\n",
       " 'Emanuel',\n",
       " 'Kuyu',\n",
       " 'Dutch',\n",
       " 'Pete',\n",
       " 'Lilly',\n",
       " 'Astrid',\n",
       " 'Mutt',\n",
       " 'Doobert',\n",
       " 'Beebop',\n",
       " 'Alexander',\n",
       " 'Sailer',\n",
       " 'Brutus',\n",
       " 'Kona',\n",
       " 'Boots',\n",
       " 'Ralphie',\n",
       " 'Cupid',\n",
       " 'Pawnd',\n",
       " 'Timber',\n",
       " 'Binky',\n",
       " 'Dudley',\n",
       " 'Comet',\n",
       " 'Sonny',\n",
       " 'Severus',\n",
       " 'Ronnie',\n",
       " 'Bones',\n",
       " 'Mauve',\n",
       " 'Chef',\n",
       " 'Sobe',\n",
       " 'Longfellow',\n",
       " 'Iroh',\n",
       " 'Pancake',\n",
       " 'Snicku',\n",
       " 'Mack',\n",
       " 'Nimbus',\n",
       " 'Laika',\n",
       " 'Dobby',\n",
       " 'Moreton',\n",
       " 'Maude',\n",
       " 'Newt',\n",
       " 'Nida',\n",
       " 'Diogi',\n",
       " 'Shooter',\n",
       " 'Major',\n",
       " 'Bauer',\n",
       " 'Akumi',\n",
       " 'Alf',\n",
       " 'Oshie',\n",
       " 'Chubbs',\n",
       " 'Sky',\n",
       " 'Eleanor',\n",
       " 'Layla',\n",
       " 'Baron',\n",
       " 'Swagger',\n",
       " 'Stephan',\n",
       " 'Brandi',\n",
       " 'Mary',\n",
       " 'Halo',\n",
       " 'Augie',\n",
       " 'Craig',\n",
       " 'Pavlov',\n",
       " 'Kyro',\n",
       " 'Ito',\n",
       " 'Eevee',\n",
       " 'Willow',\n",
       " 'Lacy',\n",
       " 'Ginger',\n",
       " 'Goose',\n",
       " 'Nugget',\n",
       " 'Jed',\n",
       " 'Sierra',\n",
       " 'Monkey',\n",
       " 'Harry',\n",
       " 'Kody',\n",
       " 'Lassie',\n",
       " 'Rover',\n",
       " 'Napolean',\n",
       " 'Rumble',\n",
       " 'Clifford',\n",
       " 'Dewey',\n",
       " 'Shikha',\n",
       " 'Lili',\n",
       " 'Jamesy',\n",
       " 'Meatball',\n",
       " 'Quinn',\n",
       " 'Zooey',\n",
       " 'Venti',\n",
       " 'Dante',\n",
       " 'Rhino',\n",
       " 'Aja',\n",
       " 'Darla',\n",
       " 'Bruno',\n",
       " 'Stuart',\n",
       " 'Jim',\n",
       " 'Ralphus',\n",
       " 'Canela',\n",
       " 'Maya',\n",
       " 'Mingus',\n",
       " 'Roscoe',\n",
       " 'Jimbo',\n",
       " 'Maisey',\n",
       " 'Noah',\n",
       " 'Grizzwald',\n",
       " 'Alfy',\n",
       " 'Koko',\n",
       " 'Rey',\n",
       " 'Duddles',\n",
       " 'Snoopy',\n",
       " 'Emmy',\n",
       " 'Jersey',\n",
       " 'Burt',\n",
       " 'Jordy',\n",
       " 'Milky',\n",
       " 'Vixen',\n",
       " 'Jarvis',\n",
       " 'Mimosa',\n",
       " 'Brady',\n",
       " 'Margo',\n",
       " 'Tycho',\n",
       " 'Dawn',\n",
       " 'Maddie',\n",
       " 'Monty',\n",
       " 'Sojourner',\n",
       " 'Arlo',\n",
       " 'Mookie',\n",
       " 'Meera',\n",
       " 'Tobi',\n",
       " 'Sunshine',\n",
       " 'Lipton',\n",
       " 'Bronte',\n",
       " 'Poppy',\n",
       " 'Gidget',\n",
       " 'Barney',\n",
       " 'Tuck',\n",
       " 'Furzey',\n",
       " 'Jarod',\n",
       " 'Thor',\n",
       " 'Callie',\n",
       " 'Cermet',\n",
       " 'Marlee',\n",
       " 'Arya',\n",
       " 'Einstein',\n",
       " 'Rumpole',\n",
       " 'Benny',\n",
       " 'Wiggles',\n",
       " 'Cannon',\n",
       " 'General',\n",
       " 'Sailor',\n",
       " 'Iggy',\n",
       " 'Snoop',\n",
       " 'Noosh',\n",
       " 'Odin',\n",
       " 'Georgie',\n",
       " 'Rontu',\n",
       " 'Robin',\n",
       " 'Monster',\n",
       " 'BeBe',\n",
       " 'Baloo',\n",
       " 'Nollie',\n",
       " 'Rorie',\n",
       " 'Simba',\n",
       " 'Bayley',\n",
       " 'Storkson',\n",
       " 'Remy',\n",
       " 'Chadrick',\n",
       " 'Kellogg',\n",
       " 'Buckley',\n",
       " 'Livvie',\n",
       " 'Hermione',\n",
       " 'Ralpher',\n",
       " 'Aldrick',\n",
       " 'Rooney',\n",
       " 'Ziva',\n",
       " 'Stefan',\n",
       " 'Pupcasso',\n",
       " 'Puff',\n",
       " 'Flurpson',\n",
       " 'Chase',\n",
       " 'Aqua',\n",
       " 'Remus',\n",
       " 'Edmund',\n",
       " 'Blu',\n",
       " 'Dietrich',\n",
       " 'Divine',\n",
       " 'Tripp',\n",
       " 'Cora',\n",
       " 'Huxley',\n",
       " 'Bookstore',\n",
       " 'Atticus',\n",
       " 'Shiloh',\n",
       " 'Gustav',\n",
       " 'Arlen',\n",
       " 'Lenox',\n",
       " 'Harvey',\n",
       " 'Blanket',\n",
       " 'Geno',\n",
       " 'Stark',\n",
       " 'Beya',\n",
       " 'Kayla',\n",
       " 'Tilly',\n",
       " 'Coleman',\n",
       " 'Enchilada',\n",
       " 'Rueben',\n",
       " 'Cilantro',\n",
       " 'Ebby',\n",
       " 'Link',\n",
       " 'Jennifur',\n",
       " 'Stephanus',\n",
       " 'Zeus',\n",
       " 'Bertson',\n",
       " 'Nico',\n",
       " 'Michelangelope',\n",
       " 'Siba',\n",
       " 'Travis',\n",
       " 'Kanu',\n",
       " 'Lance',\n",
       " 'Opie',\n",
       " 'Stubert',\n",
       " 'Kane',\n",
       " 'Staniel',\n",
       " 'Sora',\n",
       " 'Beemo',\n",
       " 'Gunner',\n",
       " 'Barclay',\n",
       " 'Suki',\n",
       " 'Alexanderson',\n",
       " 'Ivar',\n",
       " 'Karll',\n",
       " 'Sprout',\n",
       " 'Blitz',\n",
       " 'Bloop',\n",
       " 'Lillie',\n",
       " 'Fred',\n",
       " 'Ashleigh',\n",
       " 'Luther',\n",
       " 'Jangle',\n",
       " 'Bowie',\n",
       " 'Schnitzel',\n",
       " 'Berkeley',\n",
       " 'Ralphé',\n",
       " 'Charleson',\n",
       " 'Clyde',\n",
       " 'Harnold',\n",
       " 'Sid',\n",
       " 'Carper',\n",
       " 'Jameson',\n",
       " 'Spanky',\n",
       " 'Lilah',\n",
       " 'Al',\n",
       " 'Timmy',\n",
       " 'Hercules',\n",
       " 'Jay',\n",
       " 'Mya',\n",
       " 'Strider',\n",
       " 'Wesley',\n",
       " 'Huck',\n",
       " 'O',\n",
       " 'Blue',\n",
       " 'Anakin',\n",
       " 'Sprinkles',\n",
       " 'Heinrich',\n",
       " 'Shakespeare',\n",
       " 'Chelsea',\n",
       " 'Bungalo',\n",
       " 'Grey',\n",
       " 'Willem',\n",
       " 'Davey',\n",
       " 'Fizz',\n",
       " 'Deacon',\n",
       " 'Cali',\n",
       " 'Godi',\n",
       " 'Butter',\n",
       " 'Levi',\n",
       " 'Mabel',\n",
       " 'Betty',\n",
       " 'Mosby',\n",
       " 'Happy',\n",
       " 'Brownie',\n",
       " 'Rizzy',\n",
       " 'Stella',\n",
       " 'Tonks',\n",
       " 'Loomis',\n",
       " 'Dale',\n",
       " 'Rizzo',\n",
       " 'Mattie',\n",
       " 'Pinot',\n",
       " 'Dallas',\n",
       " 'Hero',\n",
       " 'Stormy',\n",
       " 'Mairi',\n",
       " 'Dixie',\n",
       " 'Carbon',\n",
       " 'Stewie',\n",
       " 'Klein',\n",
       " 'Fido',\n",
       " 'Emma',\n",
       " 'Spencer',\n",
       " 'Lilli',\n",
       " 'Boston',\n",
       " 'Brandonald',\n",
       " 'Corey',\n",
       " 'Leonard',\n",
       " 'Beckham',\n",
       " 'Devón',\n",
       " 'Gert',\n",
       " 'Dex',\n",
       " 'Ace',\n",
       " 'Tayzie',\n",
       " 'Grizzie',\n",
       " 'Gilbert',\n",
       " 'Meyer',\n",
       " 'Arnie',\n",
       " 'Zoe',\n",
       " 'Rocco',\n",
       " 'Theo',\n",
       " 'Rose',\n",
       " 'Brat',\n",
       " 'DonDon',\n",
       " 'Chevy',\n",
       " 'Tito',\n",
       " 'Philbert',\n",
       " 'Rupert',\n",
       " 'Brudge',\n",
       " 'Shadoe',\n",
       " 'Angel',\n",
       " 'Tove',\n",
       " 'Wishes',\n",
       " 'Gromit',\n",
       " 'Aubie',\n",
       " 'Kota',\n",
       " 'Leela',\n",
       " 'Glenn',\n",
       " 'Shelby',\n",
       " 'Sephie',\n",
       " 'Bonaparte',\n",
       " 'Christoper']"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking unique values in the names columns\n",
    "list(df_all.name.value_counts().keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "148f7e7d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "58dbb765",
   "metadata": {},
   "source": [
    "# Issue #12\n",
    "*the **\"+0000\"** in timestamp column in df_archive table*\n",
    "*  *All the values within the timestamp seems to have a format of **\"2016-06-06 15:40:26 +0000\"**.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64aa868d",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *Extract the **\"year\"**, **\"month\"** and **\"date\"** from all the timestamp values*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85bf9302",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "00271db3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting year, month and date from timestamp column using regex\n",
    "df_all['timestamp'] = df_all['timestamp'].astype(str).str.extract(r'(\\d{4}.\\d{2}.\\d{2})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "73cea794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1060    2016-06-11\n",
       "403     2017-01-25\n",
       "2015    2015-12-03\n",
       "1692    2015-12-28\n",
       "1151    2016-04-29\n",
       "Name: timestamp, dtype: object"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying changes made\n",
    "df_all.timestamp.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccd342f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "27409902",
   "metadata": {},
   "source": [
    "# Issue #13\n",
    "*datatype of **\"timestamp\"** column of df_archive*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6c591ff",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *Convert timestamp of date_time using pd.to_datetime method*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69c4021c",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "36dc8b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change datatype to datetime \n",
    "df_all['timestamp'] = pd.to_datetime(df_all['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "f5e5f6e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifying datatype of timestamp column\n",
    "df_all.timestamp.dtype\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d93d820",
   "metadata": {},
   "source": [
    "*datetime64[ns] maps to either <M8[ns] or >M8[ns] depending on the endian-ness of the machine*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "d9d3a309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# source: https://stackoverflow.com/questions/29206612/difference-between-data-type-datetime64ns-and-m8ns\n",
    "\n",
    "# On a machine whose byte order is little endian, there is no difference\n",
    "np.dtype('datetime64[ns]') == np.dtype('<M8[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d81886c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "84ae1ba2",
   "metadata": {},
   "source": [
    "# Issue #14\n",
    "*.**\"source\"** column of df_archive displayed in raw html codes*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "e80b750c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>     1933\n",
       "<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>                       28\n",
       "<a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>      10\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking value varities with source column\n",
    "df_all.source.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301f7eac",
   "metadata": {},
   "source": [
    "*There appears to be **\"only 3 different value types\"** for the source column, hence, we would like to set them into a single group in a regular expression to extract either of them*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e768fb5",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *  *Convert to string and extract text within the **\"a\"** tag for all **\"source\"** column values*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a3b3d12",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "d2bc709c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting to string and extracting text within the \"a\" tags of the source column\n",
    "df_all['source'] = df_all['source'].astype(str).str.extract(r'(Twitter for iPhone|Twitter Web Client|TweetDeck)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "2f704c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Twitter for iPhone    1933\n",
       "Twitter Web Client      28\n",
       "TweetDeck               10\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifying changes made \n",
    "df_all.source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "f22e2c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "526     Twitter for iPhone\n",
       "2026    Twitter for iPhone\n",
       "1669    Twitter for iPhone\n",
       "Name: source, dtype: object"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking random values for source data\n",
    "df_all.source.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44f3d3b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6fa5e3ad",
   "metadata": {},
   "source": [
    "# Issue #15\n",
    "*datatypes of some columns not valid*\n",
    ">  *Lets first have a look a the datatypes for all the columns at the moment*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "9847b76e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1971 entries, 0 to 2355\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype         \n",
      "---  ------              --------------  -----         \n",
      " 0   tweet_id            1971 non-null   object        \n",
      " 1   timestamp           1971 non-null   datetime64[ns]\n",
      " 2   source              1971 non-null   object        \n",
      " 3   text                1971 non-null   object        \n",
      " 4   rating_numerator    1971 non-null   int64         \n",
      " 5   rating_denominator  1971 non-null   int64         \n",
      " 6   name                1971 non-null   object        \n",
      " 7   retweet_count       1971 non-null   float64       \n",
      " 8   favorite_count      1971 non-null   float64       \n",
      " 9   jpg_url             1971 non-null   object        \n",
      " 10  breed               1971 non-null   object        \n",
      " 11  confidence_level    1971 non-null   float64       \n",
      " 12  dog_stage           1971 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(3), int64(2), object(7)\n",
      "memory usage: 215.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Checking datatypes of all data columns \n",
    "df_all.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eac6d578",
   "metadata": {},
   "source": [
    "*Some datatypes seems to have been  corrected from previous cleaning steps above*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e72b77",
   "metadata": {},
   "source": [
    "**Define**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296f2919",
   "metadata": {},
   "source": [
    ">  * *Change **\"source\"**, **\"dog_stage\"**, **\"favorite_count\"** and **\"retweet_counts\"** datatypes to **\"category\"**, **\"category\"**, **\"int\"** and **\"int\"** dataypes respectively*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5717bcd8",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "f8896624",
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting column datatypes to their appropriate formats\n",
    "df_all['source'] = df_all.source.astype('category')\n",
    "df_all['dog_stage'] = df_all.dog_stage.astype('category')\n",
    "df_all['favorite_count'] = df_all.favorite_count.astype(int)\n",
    "df_all['retweet_count'] = df_all.retweet_count.astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "feed2064",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1971 entries, 0 to 2355\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype         \n",
      "---  ------              --------------  -----         \n",
      " 0   tweet_id            1971 non-null   object        \n",
      " 1   timestamp           1971 non-null   datetime64[ns]\n",
      " 2   source              1971 non-null   category      \n",
      " 3   text                1971 non-null   object        \n",
      " 4   rating_numerator    1971 non-null   int64         \n",
      " 5   rating_denominator  1971 non-null   int64         \n",
      " 6   name                1971 non-null   object        \n",
      " 7   retweet_count       1971 non-null   int32         \n",
      " 8   favorite_count      1971 non-null   int32         \n",
      " 9   jpg_url             1971 non-null   object        \n",
      " 10  breed               1971 non-null   object        \n",
      " 11  confidence_level    1971 non-null   float64       \n",
      " 12  dog_stage           1971 non-null   category      \n",
      "dtypes: category(2), datetime64[ns](1), float64(1), int32(2), int64(2), object(5)\n",
      "memory usage: 173.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# verify changes made\n",
    "df_all.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4907aa25",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "032ce521",
   "metadata": {},
   "source": [
    "# Issue#16\n",
    "*undiscriptive column names*\n",
    ">   *In my opinion, columns names **\"timestamp\"** and **\"favorite_counts\"** seems undiscriptive hence would be important to be renamed*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1bbe4e7",
   "metadata": {},
   "source": [
    "**Define**\n",
    ">  *Rename **\"timestamp\"**, **\"favorite_counts\"** and **\"jpg_url\"** columns to **\"tweet_date\"**, **\"tweet_url\"**, **\"num_of_likes\"** and **\"img_url\"** respectively*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ef97d9c",
   "metadata": {},
   "source": [
    "**Coding and Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "e625eac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming undiscriptive columns\n",
    "df_all.rename(columns  = {'timestamp':'tweet_date', 'favorite_count':'num_of_likes', 'jpg_url':'img_url'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "6b371038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'tweet_date', 'source', 'text', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'retweet_count', 'num_of_likes',\n",
       "       'img_url', 'breed', 'confidence_level', 'dog_stage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# verify changes made\n",
    "df_all.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "d32d4632",
   "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>tweet_date</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>num_of_likes</th>\n",
       "      <th>img_url</th>\n",
       "      <th>breed</th>\n",
       "      <th>confidence_level</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>689835978131935233</td>\n",
       "      <td>2016-01-20</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Meet Fynn &amp;amp; Taco. Fynn is an all-powerful leaf lord and Taco is in the wrong place at the wrong time. 11/10 &amp;amp; 10/10 https://t.co/MuqHPvtL8c</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Fynn</td>\n",
       "      <td>850</td>\n",
       "      <td>2369</td>\n",
       "      <td>https://pbs.twimg.com/media/CZLKJpDWQAA-5u4.jpg</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.600186</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>802572683846291456</td>\n",
       "      <td>2016-11-26</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Winnie. She's h*ckin ferocious. Dandelion doesn't even see her coming. 12/10 would pet with caution https://t.co/EFfLCP7oQv</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Winnie</td>\n",
       "      <td>2926</td>\n",
       "      <td>9959</td>\n",
       "      <td>https://pbs.twimg.com/media/CyNPmJgXcAECPuB.jpg</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.610171</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2163</th>\n",
       "      <td>669375718304980992</td>\n",
       "      <td>2015-11-25</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Billl. He's trying to be a ghost but he's not very good at it. 6/10 c'mon Billl https://t.co/ero0XfdGtY</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>Billl</td>\n",
       "      <td>792</td>\n",
       "      <td>1425</td>\n",
       "      <td>https://pbs.twimg.com/media/CUoZqaqWcAAA2MQ.jpg</td>\n",
       "      <td>Airedale</td>\n",
       "      <td>0.168762</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id tweet_date              source  \\\n",
       "1538  689835978131935233 2016-01-20  Twitter for iPhone   \n",
       "563   802572683846291456 2016-11-26  Twitter for iPhone   \n",
       "2163  669375718304980992 2015-11-25  Twitter for iPhone   \n",
       "\n",
       "                                                                                                                                                     text  \\\n",
       "1538  Meet Fynn &amp; Taco. Fynn is an all-powerful leaf lord and Taco is in the wrong place at the wrong time. 11/10 &amp; 10/10 https://t.co/MuqHPvtL8c   \n",
       "563                   This is Winnie. She's h*ckin ferocious. Dandelion doesn't even see her coming. 12/10 would pet with caution https://t.co/EFfLCP7oQv   \n",
       "2163                                      This is Billl. He's trying to be a ghost but he's not very good at it. 6/10 c'mon Billl https://t.co/ero0XfdGtY   \n",
       "\n",
       "      rating_numerator  rating_denominator    name  retweet_count  \\\n",
       "1538                11                  10    Fynn            850   \n",
       "563                 12                  10  Winnie           2926   \n",
       "2163                 6                  10   Billl            792   \n",
       "\n",
       "      num_of_likes                                          img_url  \\\n",
       "1538          2369  https://pbs.twimg.com/media/CZLKJpDWQAA-5u4.jpg   \n",
       "563           9959  https://pbs.twimg.com/media/CyNPmJgXcAECPuB.jpg   \n",
       "2163          1425  https://pbs.twimg.com/media/CUoZqaqWcAAA2MQ.jpg   \n",
       "\n",
       "                 breed  confidence_level dog_stage  \n",
       "1538            collie          0.600186      none  \n",
       "563   golden_retriever          0.610171      none  \n",
       "2163          Airedale          0.168762      none  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking dataframe for changes made\n",
    "df_all.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de281578",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fa473027",
   "metadata": {},
   "source": [
    "# Checking for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "4774a141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicates within dataframe\n",
    "sum(df_all.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc78a944",
   "metadata": {},
   "source": [
    "# Arranging dataset for easier analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "66020e78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'tweet_date', 'source', 'text', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'retweet_count', 'num_of_likes',\n",
       "       'img_url', 'breed', 'confidence_level', 'dog_stage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking dataframe columns\n",
    "df_all.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "48884545",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rearranging columns for easier analysis\n",
    "new_arrangement = ['tweet_id', 'num_of_likes', 'retweet_count', \n",
    "                   'rating_numerator', 'rating_denominator', \n",
    "                   'tweet_date', 'name', 'dog_stage',\n",
    "                   'breed', 'confidence_level', 'source', 'text', 'img_url']\n",
    "\n",
    "df_all = df_all[new_arrangement]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "5e0d345a",
   "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>num_of_likes</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>tweet_date</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>breed</th>\n",
       "      <th>confidence_level</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>img_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>770655142660169732</td>\n",
       "      <td>8130</td>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-08-30</td>\n",
       "      <td>None</td>\n",
       "      <td>none</td>\n",
       "      <td>no_breed</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>We only rate dogs. Pls stop sending in non-canines like this Arctic Floof Kangaroo. This is very frustrating. 11/10 https://t.co/qlUDuPoE3d</td>\n",
       "      <td>https://pbs.twimg.com/media/CrHqwjWXgAAgJSe.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id  num_of_likes  retweet_count  rating_numerator  \\\n",
       "819  770655142660169732          8130           2013                11   \n",
       "\n",
       "     rating_denominator tweet_date  name dog_stage     breed  \\\n",
       "819                  10 2016-08-30  None      none  no_breed   \n",
       "\n",
       "     confidence_level              source  \\\n",
       "819               0.0  Twitter for iPhone   \n",
       "\n",
       "                                                                                                                                            text  \\\n",
       "819  We only rate dogs. Pls stop sending in non-canines like this Arctic Floof Kangaroo. This is very frustrating. 11/10 https://t.co/qlUDuPoE3d   \n",
       "\n",
       "                                             img_url  \n",
       "819  https://pbs.twimg.com/media/CrHqwjWXgAAgJSe.jpg  "
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verifying changes to column arrangements\n",
    "df_all.sample(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66f0462d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d97f65ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f5a03bce",
   "metadata": {},
   "source": [
    "# Storing Data \n",
    ">  *The cleaned dataset will now be saved in a csv format with the name **\"twitter_archive_master.csv\"**.*\n",
    ">  *A copy of the cleaned dataset would be made to work with during the analyis phase*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "598cdb1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the cleaned dataset in a csv format\n",
    "df_all.to_csv('twitter_archive_master.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "96e23451",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive D is ABBEY\n",
      " Volume Serial Number is 0A3B-0506\n",
      "\n",
      " Directory of D:\\jupiter_files\\data_wrangling\\project_2\n",
      "\n",
      "06/26/2022  12:39 PM           536,999 twitter_archive_master.csv\n",
      "               1 File(s)        536,999 bytes\n",
      "               0 Dir(s)  64,458,264,576 bytes free\n"
     ]
    }
   ],
   "source": [
    "# checking twitter_archive_master.csv file in local disc\n",
    "%ls twitter_archive_master.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "e693be39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# making a copy of the cleaned dataset\n",
    "df_master = df_all.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "e2e6d174",
   "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>num_of_likes</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>tweet_date</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>breed</th>\n",
       "      <th>confidence_level</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>img_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>39467</td>\n",
       "      <td>8853</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>none</td>\n",
       "      <td>no_breed</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>33819</td>\n",
       "      <td>6514</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>none</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  num_of_likes  retweet_count  rating_numerator  \\\n",
       "0  892420643555336193         39467           8853                13   \n",
       "1  892177421306343426         33819           6514                13   \n",
       "\n",
       "   rating_denominator tweet_date     name dog_stage      breed  \\\n",
       "0                  10 2017-08-01  Phineas      none   no_breed   \n",
       "1                  10 2017-08-01    Tilly      none  Chihuahua   \n",
       "\n",
       "   confidence_level              source  \\\n",
       "0          0.000000  Twitter for iPhone   \n",
       "1          0.323581  Twitter for iPhone   \n",
       "\n",
       "                                                                                                                                                                 text  \\\n",
       "0                                                       This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "\n",
       "                                           img_url  \n",
       "0  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg  \n",
       "1  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# verify the copy made\n",
    "df_master.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa9fe438",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4746f813",
   "metadata": {},
   "source": [
    "# Analyzing and Visualizing the data\n",
    ">*Insights based on the cleaned dataset in this case, df_master was generated. The few that was looked into includes;*\n",
    ">  *  ***What was the most common ratings and which known dog stages were associated with that the most?***\n",
    ">  *  ***What are the likes most associated to tweets?***\n",
    ">  *  ***What was the relationship between the number of likes and the retweet counts?***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "bdc6a51d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fc68260",
   "metadata": {},
   "source": [
    "### *What was the most common ratings and which known dog stages were associated with that the most?*\n",
    "*The rating referred to in this case is  the rating_numerator of which different figures were involved in our dataset*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121bb107",
   "metadata": {},
   "source": [
    "***Lets first have a look at the number of ratings associated with each rating numerator***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "63bf1a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12    450\n",
       "10    419\n",
       "11    396\n",
       "13    261\n",
       "9     150\n",
       "8      95\n",
       "7      51\n",
       "14     35\n",
       "5      33\n",
       "6      32\n",
       "3      19\n",
       "4      15\n",
       "2       9\n",
       "1       4\n",
       "0       2\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# determining the most common rating_numerator value\n",
    "df_master['rating_numerator'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e386983",
   "metadata": {},
   "source": [
    "*The most common rating as presented by the data is **\"12\"** with a total of 450 dogs having this rating.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6906415",
   "metadata": {},
   "source": [
    "*Lets now findout the various **\"dog_stages\"** associated with this rating of dogs*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "40db92a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dog_stage\n",
       "none         378\n",
       "pupper        47\n",
       "doggo         17\n",
       "puppo          6\n",
       "floofer        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master.query('rating_numerator == 12')[['dog_stage']].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da5aee05",
   "metadata": {},
   "source": [
    "*The most common dog rating turns out be for those dogs whose stages are not known, but the question seeks for those dogs of whose stages are known hence we would have eleminate those dogs of whom there are no knowledge of the stages the are invloved in*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d4e3aca",
   "metadata": {},
   "source": [
    "***Removing dogs with unknown dog stages***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "333ee8b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminating dogs without known dog stages\n",
    "df_none = df_master[~(df_master['dog_stage'] == \"none\")]\n",
    "\n",
    "# Taking dogs with rating_numerators equivalent to 12 which is the most common of the ratings\n",
    "data = df_none.query('rating_numerator == 12')[['dog_stage']].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "af25e3e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(kind = 'bar')\n",
    "\n",
    "x1 = [0,1,2,3]\n",
    "labels = ['Pupper', 'Doggo', 'Puppo', 'Floofer']\n",
    "plt.xticks(x1, labels, rotation=45)\n",
    "plt.ylabel('Number Of Dogs', fontsize = 14)\n",
    "plt.xlabel('Dog Stages', fontsize = 14)\n",
    "plt.title('Dog Stages With Highest Ratings', fontsize = 16)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6156b039",
   "metadata": {},
   "source": [
    "*Not including the unknown dog stages showed those within the Pupper stages of their life were dominant among the tweets with a value of about 47 as displayed by the graph and the Floofer groups being the least as shown by the stats displayed earlier on*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7ce0d1",
   "metadata": {},
   "source": [
    "## What are the likes most associated to tweets?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad291bb",
   "metadata": {},
   "source": [
    "*Lets first find out the most common like counts for the various tweets*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "54a07fd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2176     3\n",
       "345      3\n",
       "610      3\n",
       "2918     3\n",
       "2706     3\n",
       "        ..\n",
       "20539    1\n",
       "5760     1\n",
       "8021     1\n",
       "4149     1\n",
       "2535     1\n",
       "Name: num_of_likes, Length: 1830, dtype: int64"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding out the unique counts for a particular number of like\n",
    "df_master.num_of_likes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "262d318a",
   "metadata": {},
   "source": [
    "*  *It appears the **\"most common likes for tweets doesn't seem to come that often\"** and hence do not exceed three, that is, the same number of likes given to a tweet would most likely be 3, if a tweet receives **\"200\"** likes, it is most likely it is most likely that the same number of likes would come up not more than two times for some other tweets in the future*\n",
    "*  *Lets now find out the number of like numbers that appeeared exactly three times*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "9b9166af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_of_likes\n",
       "2768             True\n",
       "610              True\n",
       "2706             True\n",
       "2918             True\n",
       "345              True\n",
       "1691             True\n",
       "2176             True\n",
       "2169            False\n",
       "1674            False\n",
       "3837            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the number of likes that appeared the most\n",
    "a = df_master[['num_of_likes']].value_counts() == 3    # 3 was the highest number of times a certain number of like count showed up\n",
    "\n",
    "a.head(10) #head = 7 since the first 7 values had value counts of 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "361cf5f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    1823\n",
       "True        7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determining number of counts for Trues and False values within a\n",
    "a.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2579c2e4",
   "metadata": {},
   "source": [
    "*There appears to be **\"7\"** unique values that meets the highest frequency for the number of likes received by a tweet*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "5df144b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2768,), (610,), (2706,), (2918,), (345,), (1691,), (2176,)]"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the index of a which is value with seek for in this case\n",
    "possible_likes = a.index.tolist()[0:7]\n",
    "possible_likes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "356130de",
   "metadata": {},
   "source": [
    "*The values within the list **\"possible_likes\"** thus represents the number of likes that is tweet is most likely receive as shown by the stats computed earlier*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01f6cf67",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fede5758",
   "metadata": {},
   "source": [
    "## What is the relationship betweeen the number of likes and the retweet counts?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f736c71",
   "metadata": {},
   "source": [
    "*Lets have a brief description of the two columns involved in this case*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "e0619321",
   "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>num_of_likes</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1971.000000</td>\n",
       "      <td>1971.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8921.838153</td>\n",
       "      <td>2767.880771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12260.445319</td>\n",
       "      <td>4687.479281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1956.500000</td>\n",
       "      <td>622.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4134.000000</td>\n",
       "      <td>1350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11347.500000</td>\n",
       "      <td>3222.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>132810.000000</td>\n",
       "      <td>79515.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        num_of_likes  retweet_count\n",
       "count    1971.000000    1971.000000\n",
       "mean     8921.838153    2767.880771\n",
       "std     12260.445319    4687.479281\n",
       "min        81.000000      16.000000\n",
       "25%      1956.500000     622.500000\n",
       "50%      4134.000000    1350.000000\n",
       "75%     11347.500000    3222.000000\n",
       "max    132810.000000   79515.000000"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master[['num_of_likes','retweet_count']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d94e3e4",
   "metadata": {},
   "source": [
    "*A brief look at the stats displayed shows the two variables seem to increase gradually as they evolve. The two graphs are more likely to have a linear relationship as they all seem to increase with time. Comparing the mean values to the values displayed shows **majority of the variables may fall within the interquartile range**, that is, between the 3rd andf 1st Quartiles of the data as the difference seems very high from the 75th percentile to the maximum value*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65db9f67",
   "metadata": {},
   "source": [
    "*A scatter would most likely yield the best result in terms of comparing these two entities since these are only two variables which are quantitative hence the scatter plot*\n",
    "* *Using seaborns library:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "f52ff807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot to show the relation between the numbrer of likes and retweets for a tweet\n",
    "ax = sns.scatterplot(x='retweet_count', y='num_of_likes', data=df_master,\n",
    "                     hue='rating_numerator', hue_norm=(3, 15)) \n",
    "rcParams['figure.figsize'] = 14,6\n",
    "ax.set(xlabel='Retweet count', ylabel='Number of likes', title='Likes VS Retweets')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d55bebc",
   "metadata": {},
   "source": [
    "*The graph displayed shows a linear relationship between the two variables. It displays a very **strong correlation** between the two variables which seems reasonable in this case*\n",
    "*  *A tweet with a greater number of likes would most likely mean it was many people did appreciate the post hence the tendency to share it for others as well of which in this case, is recognized by the number of retweets. Hence, **an increase in the number of likes a tweet receives being directly proportional to the number of that tweets** retweet counts as well*\n",
    "*  *Most of the tweets as indicated from the graph did recieve likes and retweet counts of **\"40K\"** and **\"10K\"** respectively*\n",
    "*  *The hue, **rating_numerator** used in the plot as well also showed tweets that received higher ratings did actually receive higher number of likes and retweet counts as well*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced4beee",
   "metadata": {},
   "source": [
    "# LIMITATION\n",
    "> *The limitations with this project includes names for the dogs that may have have seemed inconsistent but could not really tell as these were related to dogs and could have such names under normal circumstance.*\n",
    "\n",
    "> *A visual assessment of the twitter archive csv file had some names associated with more than a single stage of their life but the graph seemed not to displa as such*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6790a45",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1e5e771b",
   "metadata": {},
   "source": [
    "# References\n",
    "* *The slack community*\n",
    "* *https://knowledge.udacity.com/questions/30335*\n",
    "* *https://knowledge.udacity.com/questions/280182*\n",
    "* *https://knowledge.udacity.com/questions/24594*\n",
    "* *https://researchdatapod.com/how-to-solve-python-jsondecodeerror-extra-data/*\n",
    "* *https://www.youtube.com/watch?v=d0jvEz4bCso*\n",
    "* *https://code.i-harness.com/en/q/19c9fbc*\n",
    "* *https://researchdatapod.com/how-to-solve-python-attributeerror-str-object-has-no-attribute-read/*\n",
    "* *https://galeascience.wordpress.com/2016/03/18/collecting-twitter-data-with-python/*\n",
    "* *https://udacity.zendesk.com/hc/en-us/articles/360060039651-ND002-Data-Analy*\n",
    "* *https://stackoverflow.com/questions/12309269/how-do-i-write-json-data-to-a-file*\n",
    "* *https://thewebdev.info/2022/04/16/how-to-fix-permissionerror-errno-13-permission-denied-with-python-open/*\n",
    "* *https://stackoverflow.com/questions/23870808/oserror-errno-13-permission-denied*\n",
    "* *https://stackoverflow.com/questions/53198665/typeerror-expected-str-bytes-or-os-pathlike-object-not-io-textiowrapper*\n",
    "* *https://stackoverflow.com/questions/41631873/how-to-read-json-txt-and-parse-json-python-read-txt-python*\n",
    "* *https://www.tutorialspoint.com/how-to-read-json-file-in-python*\n",
    "* *https://stackoverflow.com/questions/47857154/how-to-read-json-object-in-python*\n",
    "* *https://stackoverflow.com/questions/71043520/json-decoder-jsondecodeerror-extra-data-line-2-column-1-char-2167*\n",
    "* *https://pythonguides.com/pandas-replace-multiple-values/*"
   ]
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