analyze_tweet_1_wrangle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Tweet Data\n",
    "## Part 1: Data Wrangling\n",
    "\n",
    "## Table of Contents\n",
    "- [Introduction](#intro)\n",
    "- [Gathering Data](#gather)\n",
    "- [Assessing Data](#assess)\n",
    "- [Cleaning Data](#clean)\n",
    "- [Conclusion](#conclusion)\n",
    "- [Reference](#reference)\n",
    "\n",
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "The process of cleaning and preparing data defines a bulk (approximately 80%) of data analysis (Dasu and Johnson 2003). In fact, data often must be cleaned and prepared multiple times throughout the course of analysis as new questions arise from a series of observations or new data is gathered to address these additional questions (Wickham 2014). To explore the vast array of possible methods for _cleaning and preparing data_ and understand the significance of this process in data analysis, this project was divided into two sections, __Part 1: Data Wrangling__ and __Part 2: Exploratory Data Analysis (EDA) and Data Visualization__, for analyzing Tweets from [_WeRateDogs (@dogrates)_](https://twitter.com/dog_rates).\n",
    "\n",
    "This document covers the first section, the _data wrangling_ phase of analyzing Tweets, which encompasses gathering, assessing, and cleaning data.\n",
    "* Each of the three sources of data on Tweets from _WeRateDogs (@dogrates)_ were gathered with different methods: manually downloading available data, performing a GET Request to call the URL that hosts the data, and querying Twitter API for Tweet data. \n",
    "* Then, these datasets were assessed for two types of issues, quality and tidiness.\n",
    "* The identified issues were addressed by defining a series of cleaning operations for each issue, translating the plans into codes, performing each cleaning operation, and verifying the resolutions.\n",
    "\n",
    "At the end of this phase, cleaned datasets were merged and stored as a separate file so that this _functional_ dataset structured in a format that permits and potentially streamlines analysis is readily available for the next phase of the data analysis. __Part 2: Exploratory Data Analysis (EDA) and Data Visualization__ of the project, which deals with exploring and augmenting the data to maximize the potential of the analyses and deriving insights from visualizations, is covered in the separate file, `analyze_tweet_part2_eda.html`.\n",
    "\n",
    "All relevant packages for wrangling the desired Tweet data from _WeRateDogs (@dogrates)_ are imported below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import os\n",
    "import twitter_api as t_api\n",
    "from timeit import default_timer as timer\n",
    "import json\n",
    "import copy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='gather'></a>\n",
    "## Gathering Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Enhanced Twitter Archive\n",
    "[Udacity](http://udacity.com) was provided with _WeRateDogs_ Twitter Archive which contains the basic data for all 5,000+ Tweets. Udacity _enhanced_ this original dataset by extracting dog ratings, dog names, and dog _stage_ from each Tweet's text data. Note that as a part of this enhancement, the original dataset was filtered to produce 2,356 rows of data which include only the Tweets that appear to mention dog ratings. The enhanced version was made available for download and was manually imported as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </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",
       "\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",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "\n",
       "   rating_denominator     name doggo floofer pupper puppo  \n",
       "0                  10  Phineas  None    None   None  None  \n",
       "1                  10    Tilly  None    None   None  None  \n",
       "2                  10   Archie  None    None   None  None  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# enhanced twitter archive data\n",
    "df_archive = pd.read_csv('data/twitter_archive_enhanced.csv')\n",
    "df_archive.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Image Predictions\n",
    "Udacity ran the images included in the Tweets from the enhanced Twitter archive through a [neural network](https://www.youtube.com/watch?v=2-Ol7ZB0MmU) and made top three predictions of each dog's breed. The `.tsv` file which contains the data on these predictions is hosted in Udacity's server ([URL](https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv)). Python's [Requests](http://docs.python-requests.org/en/master/) library was used to submit a GET request to this URL and programmatically download the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GET request URL\n",
    "get_url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "\n",
    "# response from GET request\n",
    "response = requests.get(get_url)\n",
    "\n",
    "# .tsv file name\n",
    "tsv_file = os.path.join('data', get_url.split('/')[-1])\n",
    "\n",
    "# store response data\n",
    "    # 1. create a file handle from the .tsv file name\n",
    "    # 2. open the file handle in writing mode\n",
    "    # 3. write the binary response to the file\n",
    "with open(tsv_file, mode = 'wb') as file:\n",
    "    file.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully downloaded image-predictions.tsv\n"
     ]
    }
   ],
   "source": [
    "# verify successful download of .tsv file\n",
    "tsv_check = [file for file in os.listdir('data') if file == tsv_file.split('\\\\')[-1]]\n",
    "\n",
    "if len(tsv_check) == 1:\n",
    "    print('Successfully downloaded {}'.format(tsv_check[0]))\n",
    "else:\n",
    "    print('Downloading {} failed'.format(tsv_check[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import .tsv data\n",
    "df_image = pd.read_csv(tsv_file, sep = '\\t')\n",
    "df_image.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Additional Tweet Data\n",
    "Additional Tweet data which were omitted during the process of enhancing the Twitter archive were gathered by querying Twitter's API with Python's [Tweepy](http://www.tweepy.org/) library for each Tweet ID in the enhanced Twitter archive. This JSON-formatted data was _dumped_ to a `.txt` file. Because the counts of re-tweets and favorites were the only types of additional Tweet data the author intended to analyze and the previous datasets were imported to dataframe objects, only the re-tweet and favorite counts for each Tweet ID were extracted from the `.txt` file and were used to create a dataframe object.\n",
    "\n",
    "Note that the Twitter API wrapper was imported from a separate Python file, `twitter_api.py`, because this file contains private keys and tokens unique to the author's Twitter Developer account. However, the specific code, excluding the actual keys and tokens, in this file which returns the Twitter API wrapper is available for review in the `README.md` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# twitter API wrapper from twitter_api.py\n",
    "api = t_api.twitter_api()\n",
    "\n",
    "# input: tweet ids from enhanced twitter archive\n",
    "tweet_ids = df_archive.tweet_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweet ID: 892420643555336193 (Count: 1)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 892177421306343426 (Count: 2)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 891815181378084864 (Count: 3)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 891689557279858688 (Count: 4)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 891327558926688256 (Count: 5)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 891087950875897856 (Count: 6)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 890971913173991426 (Count: 7)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 890729181411237888 (Count: 8)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 890609185150312448 (Count: 9)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 890240255349198849 (Count: 10)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 890006608113172480 (Count: 11)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 889880896479866881 (Count: 12)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 889665388333682689 (Count: 13)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 889638837579907072 (Count: 14)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 889531135344209921 (Count: 15)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 889278841981685760 (Count: 16)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 888917238123831296 (Count: 17)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 888804989199671297 (Count: 18)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 888554962724278272 (Count: 19)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 888202515573088257 (Count: 20)\n",
      " - Fail\n",
      "\n",
      "Tweet ID: 888078434458587136 (Count: 21)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 887705289381826560 (Count: 22)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 887517139158093824 (Count: 23)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 887473957103951883 (Count: 24)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 887343217045368832 (Count: 25)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 887101392804085760 (Count: 26)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 886983233522544640 (Count: 27)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 886736880519319552 (Count: 28)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 886680336477933568 (Count: 29)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 886366144734445568 (Count: 30)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 886267009285017600 (Count: 31)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 886258384151887873 (Count: 32)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 886054160059072513 (Count: 33)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 885984800019947520 (Count: 34)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 885528943205470208 (Count: 35)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 885518971528720385 (Count: 36)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 885311592912609280 (Count: 37)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 885167619883638784 (Count: 38)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 884925521741709313 (Count: 39)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 884876753390489601 (Count: 40)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 884562892145688576 (Count: 41)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 884441805382717440 (Count: 42)\n",
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      "Rate limit reached. Sleeping for: 707\n"
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      "Rate limit reached. Sleeping for: 185\n"
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      " - Success\n",
      "\n",
      "Tweet ID: 666058600524156928 (Count: 2347)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 666057090499244032 (Count: 2348)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 666055525042405380 (Count: 2349)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 666051853826850816 (Count: 2350)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 666050758794694657 (Count: 2351)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 666049248165822465 (Count: 2352)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 666044226329800704 (Count: 2353)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 666033412701032449 (Count: 2354)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 666029285002620928 (Count: 2355)\n",
      " - Success\n",
      "\n",
      "Tweet ID: 666020888022790149 (Count: 2356)\n",
      " - Success\n",
      "\n",
      "Query took 32.61 minutes.\n",
      "Query failed for 18 tweets.\n"
     ]
    }
   ],
   "source": [
    "# dict-object containing failed tweet ids and error details\n",
    "error_dict = {}\n",
    "\n",
    "# track progress of iteration over all tweets in archive\n",
    "count = 0\n",
    "start = timer()\n",
    "\n",
    "# query Twitter API for JSON data\n",
    "with open('data/tweet_json.txt', 'w') as outfile:\n",
    "    for tweet_id in tweet_ids:\n",
    "        count += 1\n",
    "        print('Tweet ID: {} (Count: {})'.format(tweet_id, count)) # tweet id being queried\n",
    "        try: # query with tweet id and obtain status object\n",
    "            api_data = api.get_status(tweet_id, tweet_mode = 'extended')\n",
    "        except t_api.tweepy.TweepError as e: # failed query\n",
    "            print(' - Fail\\n')\n",
    "            error_dict[tweet_id] = e\n",
    "        else: # write JSON data to .txt file\n",
    "            print(' - Success\\n')\n",
    "            json.dump(api_data._json, outfile)\n",
    "            outfile.write('\\n') # change line for the next tweet id\n",
    "end = timer()\n",
    "print('Query took {} minutes.'.format(round((end-start)/60, 2))) # duration of iteration\n",
    "print('Query failed for {} tweets.'.format(len(error_dict)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>8212</td>\n",
       "      <td>37667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>6072</td>\n",
       "      <td>32353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>4015</td>\n",
       "      <td>24372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id  retweet_count  favorite_count\n",
       "0  892420643555336193           8212           37667\n",
       "1  892177421306343426           6072           32353\n",
       "2  891815181378084864           4015           24372"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list-object containing counts of re-tweets and favorites for each tweet\n",
    "json_list = []\n",
    "json_attributes = ['id', 'retweet_count','favorite_count']\n",
    "\n",
    "# append each JSON data as dict-object to the list\n",
    "with open('data/tweet_json.txt') as file:\n",
    "    for line in file:\n",
    "        json_data = json.loads(line[:-1]) # exclude \\n and load JSON data only\n",
    "        json_list.append({json_attributes[0]: json_data[json_attributes[0]]\n",
    "                        , json_attributes[1]: json_data[json_attributes[1]]\n",
    "                        , json_attributes[2]: json_data[json_attributes[2]]})\n",
    "\n",
    "# create dataframe from the list of dict-objects containing JSON data\n",
    "df_json = pd.DataFrame(data = json_list, columns = json_attributes)\n",
    "df_json.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='assess'></a>\n",
    "## Assessing Data\n",
    "\n",
    "The three datasets collected in __Gathering Data__ were assessed for quality issues which deal with the content of the data and tidiness issues which pertain to the structure of the data. A total of 14 quality issues and two tidiness issues were observed. Only the observations of these issues, which render cleaning necessary in __Cleaning Data__, were documented. \n",
    "\n",
    "In this step of the data wrangling phase, a copy of each dataset was created and assessed. However, the actual datasets can still be reviewed for quality and tidiness issues, and creating copies of these datasets can be postponed to the next step of the data wrangling phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# copy of twitter_archive_enhanced.csv\n",
    "# df_archive = pd.read_csv('data/twitter_archive_enhanced.csv')\n",
    "df_archive_clean = df_archive.copy()\n",
    "\n",
    "# copy of image-predictions.tsv \n",
    "# df_image = pd.read_csv('data/image-predictions.tsv', sep = '\\t')\n",
    "df_image_clean = df_image.copy()\n",
    "\n",
    "# copy of JSON data from tweet_json.txt\n",
    "df_json_clean = df_json.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Enhanced Twitter Archive\n",
    "__Quality Issues__\n",
    "1. Data type for the `tweet_id` column is integer, instead of string (object). \\[see _assessment 1.2_\\]\n",
    "2. The two columns `in_reply_to_status_id` and `in_reply_to_user_id` appear to be outside the scope of the interest of this project. \\[see _assessments 1.1 and 1.2_ and documentation on [Tweet Object](https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object.html)\\]\n",
    "3. Data type for the `timestamp` column is object, instead of datetime. \\[see _assessment 1.2_\\]\n",
    "4. The four possible values for the `source` column not only indicate the source of the tweet but includes the HTML tags and attributes. \\[see _assessment 1.3_\\]\n",
    "5. 181 tweets were created by re-tweeting existing tweets. All retweets have non-`NaN` values, and all original tweets have `NaN` for the three columns `retweeted_status_id`, `retweeted_status_user_id`, `retweeted_status_timestamp`. \\[see _assessments 1.4.1 ~ 1.4.4_\\]\n",
    "6. The `expanded_urls` column does not show meaningful information besides the tweet id which is already listed under the `tweet_id` column. \\[see _assessment 1.1_\\]\n",
    "7. 23 dog ratings extracted from the tweet are inaccurate in that these ratings have values for the `rating_denominator` column other than 10. \\[see _assessments 1.5.1, 1.5.2_\\]\n",
    "8. 109 names of dogs in the `name` column are inaccurate. \\[see _assessments 1.6.1 ~ 1.6.4_\\]    \n",
    "    * These names not only begin with a lower case alphabet but are oridinary terms such as \"such\", \"a\", \"quite\", etc.\n",
    "    * Few of the tweet texts corresponding to these names do introduce the actual names of dogs in the phrase, 'name is ~' or 'named ~'.\n",
    "\n",
    "\n",
    "9. The five columns, `name`, `doggo`, `floofer`, `pupper`, and `puppo`, use \"None\" instead of `NaN` for missing values. \\[see _assessment 1.1_\\]\n",
    "10. 14 Tweets have multiple dog stages. \\[see _assessment 1.7_\\]\n",
    "\n",
    "__Tidiness Issue__\n",
    "1. Four possible dog \"stages\", doggo, floofer, pupper, and puppo, are shown as column headers although these \"stages\" are not variable names. \\[see _assessment 1.1_\\]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>695064344191721472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-02-04 02:00:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This may be the greatest video I've ever been ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/695064344...</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>828376505180889089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-02-05 22:55:23 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Beebop. Her name means \"Good Dog\" in r...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/828376505...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Beebop</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>841680585030541313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-03-14 16:01:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Penny. She's a dragon slayer. Feared b...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/841680585...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Penny</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>787717603741622272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-16 18:11:26 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tonks. She is a service puppo. Can hea...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/787717603...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tonks</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1518</th>\n",
       "      <td>690932576555528194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-23 16:22:17 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Buddy. He's testing out the water. Suc...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/690932576...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Buddy</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",
       "1459  695064344191721472                    NaN                  NaN   \n",
       "373   828376505180889089                    NaN                  NaN   \n",
       "267   841680585030541313                    NaN                  NaN   \n",
       "689   787717603741622272                    NaN                  NaN   \n",
       "1518  690932576555528194                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "1459  2016-02-04 02:00:27 +0000   \n",
       "373   2017-02-05 22:55:23 +0000   \n",
       "267   2017-03-14 16:01:03 +0000   \n",
       "689   2016-10-16 18:11:26 +0000   \n",
       "1518  2016-01-23 16:22:17 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "1459  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "373   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "267   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "689   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1518  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "1459  This may be the greatest video I've ever been ...                  NaN   \n",
       "373   This is Beebop. Her name means \"Good Dog\" in r...                  NaN   \n",
       "267   This is Penny. She's a dragon slayer. Feared b...                  NaN   \n",
       "689   This is Tonks. She is a service puppo. Can hea...                  NaN   \n",
       "1518  This is Buddy. He's testing out the water. Suc...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "1459                       NaN                        NaN   \n",
       "373                        NaN                        NaN   \n",
       "267                        NaN                        NaN   \n",
       "689                        NaN                        NaN   \n",
       "1518                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "1459  https://twitter.com/dog_rates/status/695064344...                 4   \n",
       "373   https://twitter.com/dog_rates/status/828376505...                13   \n",
       "267   https://twitter.com/dog_rates/status/841680585...                12   \n",
       "689   https://twitter.com/dog_rates/status/787717603...                13   \n",
       "1518  https://twitter.com/dog_rates/status/690932576...                12   \n",
       "\n",
       "      rating_denominator    name doggo floofer pupper  puppo  \n",
       "1459                  10    None  None    None   None   None  \n",
       "373                   10  Beebop  None    None   None   None  \n",
       "267                   10   Penny  None    None   None   None  \n",
       "689                   10   Tonks  None    None   None  puppo  \n",
       "1518                  10   Buddy  None    None   None   None  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 1.1\n",
    "df_archive_clean.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
      "tweet_id                      2356 non-null int64\n",
      "in_reply_to_status_id         78 non-null float64\n",
      "in_reply_to_user_id           78 non-null float64\n",
      "timestamp                     2356 non-null object\n",
      "source                        2356 non-null object\n",
      "text                          2356 non-null object\n",
      "retweeted_status_id           181 non-null float64\n",
      "retweeted_status_user_id      181 non-null float64\n",
      "retweeted_status_timestamp    181 non-null object\n",
      "expanded_urls                 2297 non-null object\n",
      "rating_numerator              2356 non-null int64\n",
      "rating_denominator            2356 non-null int64\n",
      "name                          2356 non-null object\n",
      "doggo                         2356 non-null object\n",
      "floofer                       2356 non-null object\n",
      "pupper                        2356 non-null object\n",
      "puppo                         2356 non-null object\n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# assessment 1.2\n",
    "df_archive_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>\n",
      "2 <a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>\n",
      "3 <a href=\"http://vine.co\" rel=\"nofollow\">Vine - Make a Scene</a>\n",
      "4 <a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>\n"
     ]
    }
   ],
   "source": [
    "# assessment 1.3: unique values for source column\n",
    "for i, source in enumerate(df_archive_clean.source.unique()):\n",
    "    print(i+1, source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 1.4.1: number of non-na's in retweeted_status_id column\n",
    "df_archive_clean.retweeted_status_id.notna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 1.4.2: number of non-na's in retweeted_status_user_id column\n",
    "df_archive_clean.retweeted_status_user_id.notna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 1.4.3: number of non-na's in retweeted_status_timestamp column\n",
    "df_archive_clean.retweeted_status_timestamp.notna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 1.4.4: number of all non-na's rows in three retweeted columns\n",
    "df_archive_clean[['retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp']].notna().any(axis = 1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 1.5.1: number of values in rating_denominator column besides 10\n",
    "(df_archive_clean.rating_denominator != 10).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>@jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>13/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>@docmisterio account started on 11/15/15</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>14/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE</td>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>14/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>13/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>11/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp;amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3</td>\n",
       "      <td>143</td>\n",
       "      <td>130</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55</td>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>10/10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq</td>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw</td>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9/10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                           text  \\\n",
       "313   @jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho                                                                          \n",
       "342   @docmisterio account started on 11/15/15                                                                                                                                    \n",
       "433   The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd                                                                         \n",
       "516   Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx   \n",
       "784   RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…                                \n",
       "902   Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE                                                                                              \n",
       "1068  After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ                                \n",
       "1120  Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv                                                   \n",
       "1165  Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a                                                                                                            \n",
       "1202  This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq                                                       \n",
       "1228  Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1                                                                               \n",
       "1254  Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12                                                                \n",
       "1274  From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK                          \n",
       "1351  Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa                                                                                          \n",
       "1433  Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ                                                                                \n",
       "1598  Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating                                    \n",
       "1634  Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3                                              \n",
       "1635  Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55                                \n",
       "1662  This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5                                 \n",
       "1663  I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible                                 \n",
       "1779  IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq                                                                                                      \n",
       "1843  Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw                                                                 \n",
       "2335  This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv                                    \n",
       "\n",
       "      rating_numerator  rating_denominator rating  \n",
       "313   960               0                   13/10  \n",
       "342   11                15                  NaN    \n",
       "433   84                70                  NaN    \n",
       "516   24                7                   NaN    \n",
       "784   9                 11                  14/10  \n",
       "902   165               150                 NaN    \n",
       "1068  9                 11                  14/10  \n",
       "1120  204               170                 NaN    \n",
       "1165  4                 20                  13/10  \n",
       "1202  50                50                  11/10  \n",
       "1228  99                90                  NaN    \n",
       "1254  80                80                  NaN    \n",
       "1274  45                50                  NaN    \n",
       "1351  60                50                  NaN    \n",
       "1433  44                40                  NaN    \n",
       "1598  4                 20                  NaN    \n",
       "1634  143               130                 NaN    \n",
       "1635  121               110                 NaN    \n",
       "1662  7                 11                  10/10  \n",
       "1663  20                16                  NaN    \n",
       "1779  144               120                 NaN    \n",
       "1843  88                80                  NaN    \n",
       "2335  1                 2                   9/10   "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 1.5.2: extract rating-like substring from tweet text\n",
    "\n",
    "# sub-dataframe for rows with rating_denominator values besides 10 \n",
    "df_ratings = df_archive_clean.query('rating_denominator != 10').loc[:,['text', 'rating_numerator', 'rating_denominator']]\n",
    "\n",
    "# extract rating, if any, of format digit(s)/10\n",
    "rating_format = '(\\d+/10)'\n",
    "df_ratings['rating'] = df_ratings.text.str.extract(pat = rating_format)\n",
    "\n",
    "# compare given ratings and ratings extracted from text\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "df_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset the option configured in assessment 1.5.2\n",
    "pd.reset_option('display.max_colwidth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 1.6.1: number of values in name column that begins with a lower case alphabet\n",
    "df_archive_clean.name.str.extract(pat = '(^[a-z])').dropna().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 1.6.2: number of values in name column that begins with a lower case alphabet\n",
    "error_name = [name for name in df_archive_clean.name.unique() if name.lower() == name]\n",
    "df_archive_clean.query('name in @error_name').shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "such\n",
      "a\n",
      "quite\n",
      "not\n",
      "one\n",
      "incredibly\n",
      "mad\n",
      "an\n",
      "very\n",
      "just\n",
      "my\n",
      "his\n",
      "actually\n",
      "getting\n",
      "this\n",
      "unacceptable\n",
      "all\n",
      "old\n",
      "infuriating\n",
      "the\n",
      "by\n",
      "officially\n",
      "life\n",
      "light\n",
      "space\n"
     ]
    }
   ],
   "source": [
    "# assessment 1.6.3: values in name column that begins with a lower case alphabet\n",
    "for name in error_name:\n",
    "    print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>name</th>\n",
       "      <th>new_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba</td>\n",
       "      <td>such</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Here is a pupper approaching maximum borkdrive. Zooming at never before seen speeds. 14/10 paw-inspiring af \\n(IG: puffie_the_chow) https://t.co/ghXBIIeQZF</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>RT @dog_rates: We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10…</td>\n",
       "      <td>quite</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10 https://t.co/g2nSyGenG9</td>\n",
       "      <td>quite</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Guys, we only rate dogs. This is quite clearly a bulbasaur. Please only send dogs. Thank you... 12/10 human used pet, it's super effective https://t.co/Xc7uj1C64x</td>\n",
       "      <td>quite</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>There's going to be a dog terminal at JFK Airport. This is not a drill. 10/10  \\nhttps://t.co/dp5h9bCwU7</td>\n",
       "      <td>not</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>Occasionally, we're sent fantastic stories. This is one of them. 14/10 for Grace https://t.co/bZ4axuH6OK</td>\n",
       "      <td>one</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>We only rate dogs. Please stop sending in non-canines like this Freudian Poof Lion. This is incredibly frustrating... 11/10 https://t.co/IZidSrBvhi</td>\n",
       "      <td>incredibly</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>Here is a perfect example of someone who has their priorities in order. 13/10 for both owner and Forrest https://t.co/LRyMrU7Wfq</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>RT @dog_rates: Say hello to mad pupper. You know what you did. 13/10 would pet until no longer furustrated https://t.co/u1ulQ5heLX</td>\n",
       "      <td>mad</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>RT @dog_rates: This is an East African Chalupa Seal. We only rate dogs. Please only send in dogs. Thank you... 10/10 https://t.co/iHe6liLwWR</td>\n",
       "      <td>an</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>RT @dog_rates: We only rate dogs. Pls stop sending in non-canines like this Mongolian grass snake. This is very frustrating. 11/10 https://…</td>\n",
       "      <td>very</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>Guys this is getting so out of hand. We only rate dogs. This is a Galapagos Speed Panda. Pls only send dogs... 10/10 https://t.co/8lpAGaZRFn</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>We only rate dogs. Pls stop sending in non-canines like this Arctic Floof Kangaroo. This is very frustrating. 11/10 https://t.co/qlUDuPoE3d</td>\n",
       "      <td>very</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>RT @dog_rates: This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC</td>\n",
       "      <td>just</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>This is my dog. Her name is Zoey. She knows I've been rating other dogs. She's not happy. 13/10 no bias at all https://t.co/ep1NkYoiwB</td>\n",
       "      <td>my</td>\n",
       "      <td>Zoey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>This is one of the most inspirational stories I've ever come across. I have no words. 14/10 for both doggo and owner https://t.co/I5ld3eKD5k</td>\n",
       "      <td>one</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>What jokester sent in a pic without a dog in it? This is not @rock_rates. This is @dog_rates. Thank you ...10/10 https://t.co/nDPaYHrtNX</td>\n",
       "      <td>not</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>That is Quizno. This is his beach. He does not tolerate human shenanigans on his beach. 10/10 reclaim ur land doggo https://t.co/vdr7DaRSa7</td>\n",
       "      <td>his</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>This is one of the most reckless puppers I've ever seen. How she got a license in the first place is beyond me. 6/10 https://t.co/z5bAdtn9kd</td>\n",
       "      <td>one</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>This is a mighty rare blue-tailed hammer sherk. Human almost lost a limb trying to take these. Be careful guys. 8/10 https://t.co/TGenMeXreW</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>This is a carrot. We only rate dogs. Please only send in dogs. You all really should know this by now ...11/10 https://t.co/9e48aPrBm2</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>This is an Iraqi Speed Kangaroo. It is not a dog. Please only send in dogs. I'm very angry with all of you ...9/10 https://t.co/5qpBTTpgUt</td>\n",
       "      <td>an</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>We only rate dogs. Pls stop sending in non-canines like this Jamaican Flop Seal. This is very very frustrating. 9/10 https://t.co/nc53zEN0hZ</td>\n",
       "      <td>very</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>This is actually a pupper and I'd pet it so well. 12/10\\nhttps://t.co/RNqS7C4Y4N</td>\n",
       "      <td>actually</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>This is a very rare Great Alaskan Bush Pupper. Hard to stumble upon without spooking. 12/10 would pet passionately https://t.co/xOBKCdpzaa</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063</th>\n",
       "      <td>This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC</td>\n",
       "      <td>just</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>This is getting incredibly frustrating. This is a Mexican Golden Beaver. We only rate dogs. Only send dogs ...10/10 https://t.co/0yolOOyD3X</td>\n",
       "      <td>getting</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>Say hello to mad pupper. You know what you did. 13/10 would pet until no longer furustrated https://t.co/u1ulQ5heLX</td>\n",
       "      <td>mad</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>This is a Slovakian Helter Skelter Feta named Leroi. Likes to skip on roofs. Good traction. Much balance. 10/10 wow! https://t.co/Dmy2mY2Qj5</td>\n",
       "      <td>a</td>\n",
       "      <td>Leroi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2198</th>\n",
       "      <td>This is a wild Toblerone from Papua New Guinea. Mouth always open. Addicted to hay. Acts blind. 7/10 handsome dog https://t.co/IGmVbz07tZ</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg</td>\n",
       "      <td>an</td>\n",
       "      <td>Berta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>Here is a horned dog. Much grace. Can jump over moons (dam!). Paws not soft. Bad at barking. 7/10 can still pet tho https://t.co/2Su7gmsnZm</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>Never forget this vine. You will not stop watching for at least 15 minutes. This is the second coveted.. 13/10 https://t.co/roqIxCvEB3</td>\n",
       "      <td>the</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>This is a Birmingham Quagmire named Chuk. Loves to relax and watch the game while sippin on that iced mocha. 10/10 https://t.co/HvNg9JWxFt</td>\n",
       "      <td>a</td>\n",
       "      <td>Chuk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>This is a Trans Siberian Kellogg named Alfonso. Huge ass eyeballs. Actually Dobby from Harry Potter. 7/10 https://t.co/XpseHBlAAb</td>\n",
       "      <td>a</td>\n",
       "      <td>Alfonso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>This is a Shotokon Macadamia mix named Cheryl. Sophisticated af. Looks like a disappointed librarian. Shh (lol) 9/10 https://t.co/J4GnJ5Swba</td>\n",
       "      <td>a</td>\n",
       "      <td>Cheryl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>This is a rare Hungarian Pinot named Jessiga. She is either mid-stroke or got stuck in the washing machine. 8/10 https://t.co/ZU0i0KJyqD</td>\n",
       "      <td>a</td>\n",
       "      <td>Jessiga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>This is a southwest Coriander named Klint. Hat looks expensive. Still on house arrest :(\\n9/10 https://t.co/IQTOMqDUIe</td>\n",
       "      <td>a</td>\n",
       "      <td>Klint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>This is a northern Wahoo named Kohl. He runs this town. Chases tumbleweeds. Draws gun wicked fast. 11/10 legendary https://t.co/J4vn2rOYFk</td>\n",
       "      <td>a</td>\n",
       "      <td>Kohl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>This is a Dasani Kingfisher from Maine. His name is Daryl. Daryl doesn't like being swallowed by a panda. 8/10 https://t.co/jpaeu6LNmW</td>\n",
       "      <td>a</td>\n",
       "      <td>Daryl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>This is a curly Ticonderoga named Pepe. No feet. Loves to jet ski. 11/10 would hug until forever https://t.co/cyDfaK8NBc</td>\n",
       "      <td>a</td>\n",
       "      <td>Pepe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2311</th>\n",
       "      <td>This is a purebred Bacardi named Octaviath. Can shoot spaghetti out of mouth. 10/10 https://t.co/uEvsGLOFHa</td>\n",
       "      <td>a</td>\n",
       "      <td>Octaviath</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>This is a golden Buckminsterfullerene named Johm. Drives trucks. Lumberjack (?). Enjoys wall. 8/10 would hug softly https://t.co/uQbZJM2DQB</td>\n",
       "      <td>a</td>\n",
       "      <td>Johm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t</td>\n",
       "      <td>quite</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>This is a southern Vesuvius bumblegruff. Can drive a truck (wow). Made friends with 5 other nifty dogs (neat). 7/10 https://t.co/LopTBkKa8h</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL</td>\n",
       "      <td>an</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>an</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2345</th>\n",
       "      <td>This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK</td>\n",
       "      <td>the</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2346</th>\n",
       "      <td>Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p</td>\n",
       "      <td>the</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>Here is a Siberian heavily armored polar bear mix. Strong owner. 10/10 I would do unspeakable things to pet this dog https://t.co/rdivxLiqEt</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc</td>\n",
       "      <td>an</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                    text  \\\n",
       "22    I've yet to rate a Venezuelan Hover Wiener. This is such an honor. 14/10 paw-inspiring af (IG: roxy.thedoxy) https://t.co/20VrLAA8ba                                 \n",
       "56    Here is a pupper approaching maximum borkdrive. Zooming at never before seen speeds. 14/10 paw-inspiring af \\n(IG: puffie_the_chow) https://t.co/ghXBIIeQZF          \n",
       "118   RT @dog_rates: We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10…                         \n",
       "169   We only rate dogs. This is quite clearly a smol broken polar bear. We'd appreciate if you only send dogs. Thank you... 12/10 https://t.co/g2nSyGenG9                 \n",
       "193   Guys, we only rate dogs. This is quite clearly a bulbasaur. Please only send dogs. Thank you... 12/10 human used pet, it's super effective https://t.co/Xc7uj1C64x   \n",
       "335   There's going to be a dog terminal at JFK Airport. This is not a drill. 10/10  \\nhttps://t.co/dp5h9bCwU7                                                             \n",
       "369   Occasionally, we're sent fantastic stories. This is one of them. 14/10 for Grace https://t.co/bZ4axuH6OK                                                             \n",
       "542   We only rate dogs. Please stop sending in non-canines like this Freudian Poof Lion. This is incredibly frustrating... 11/10 https://t.co/IZidSrBvhi                  \n",
       "649   Here is a perfect example of someone who has their priorities in order. 13/10 for both owner and Forrest https://t.co/LRyMrU7Wfq                                     \n",
       "682   RT @dog_rates: Say hello to mad pupper. You know what you did. 13/10 would pet until no longer furustrated https://t.co/u1ulQ5heLX                                   \n",
       "759   RT @dog_rates: This is an East African Chalupa Seal. We only rate dogs. Please only send in dogs. Thank you... 10/10 https://t.co/iHe6liLwWR                         \n",
       "773   RT @dog_rates: We only rate dogs. Pls stop sending in non-canines like this Mongolian grass snake. This is very frustrating. 11/10 https://…                         \n",
       "801   Guys this is getting so out of hand. We only rate dogs. This is a Galapagos Speed Panda. Pls only send dogs... 10/10 https://t.co/8lpAGaZRFn                         \n",
       "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",
       "822   RT @dog_rates: This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC                                                           \n",
       "852   This is my dog. Her name is Zoey. She knows I've been rating other dogs. She's not happy. 13/10 no bias at all https://t.co/ep1NkYoiwB                               \n",
       "924   This is one of the most inspirational stories I've ever come across. I have no words. 14/10 for both doggo and owner https://t.co/I5ld3eKD5k                         \n",
       "988   What jokester sent in a pic without a dog in it? This is not @rock_rates. This is @dog_rates. Thank you ...10/10 https://t.co/nDPaYHrtNX                             \n",
       "992   That is Quizno. This is his beach. He does not tolerate human shenanigans on his beach. 10/10 reclaim ur land doggo https://t.co/vdr7DaRSa7                          \n",
       "993   This is one of the most reckless puppers I've ever seen. How she got a license in the first place is beyond me. 6/10 https://t.co/z5bAdtn9kd                         \n",
       "1002  This is a mighty rare blue-tailed hammer sherk. Human almost lost a limb trying to take these. Be careful guys. 8/10 https://t.co/TGenMeXreW                         \n",
       "1004  Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R                           \n",
       "1017  This is a carrot. We only rate dogs. Please only send in dogs. You all really should know this by now ...11/10 https://t.co/9e48aPrBm2                               \n",
       "1025  This is an Iraqi Speed Kangaroo. It is not a dog. Please only send in dogs. I'm very angry with all of you ...9/10 https://t.co/5qpBTTpgUt                           \n",
       "1031  We only rate dogs. Pls stop sending in non-canines like this Jamaican Flop Seal. This is very very frustrating. 9/10 https://t.co/nc53zEN0hZ                         \n",
       "1040  This is actually a pupper and I'd pet it so well. 12/10\\nhttps://t.co/RNqS7C4Y4N                                                                                     \n",
       "1049  This is a very rare Great Alaskan Bush Pupper. Hard to stumble upon without spooking. 12/10 would pet passionately https://t.co/xOBKCdpzaa                           \n",
       "1063  This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC                                                                          \n",
       "1071  This is getting incredibly frustrating. This is a Mexican Golden Beaver. We only rate dogs. Only send dogs ...10/10 https://t.co/0yolOOyD3X                          \n",
       "1095  Say hello to mad pupper. You know what you did. 13/10 would pet until no longer furustrated https://t.co/u1ulQ5heLX                                                  \n",
       "...                                                                                                                   ...                                                  \n",
       "2191  This is a Slovakian Helter Skelter Feta named Leroi. Likes to skip on roofs. Good traction. Much balance. 10/10 wow! https://t.co/Dmy2mY2Qj5                         \n",
       "2198  This is a wild Toblerone from Papua New Guinea. Mouth always open. Addicted to hay. Acts blind. 7/10 handsome dog https://t.co/IGmVbz07tZ                            \n",
       "2204  This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg                          \n",
       "2211  Here is a horned dog. Much grace. Can jump over moons (dam!). Paws not soft. Bad at barking. 7/10 can still pet tho https://t.co/2Su7gmsnZm                          \n",
       "2212  Never forget this vine. You will not stop watching for at least 15 minutes. This is the second coveted.. 13/10 https://t.co/roqIxCvEB3                               \n",
       "2218  This is a Birmingham Quagmire named Chuk. Loves to relax and watch the game while sippin on that iced mocha. 10/10 https://t.co/HvNg9JWxFt                           \n",
       "2222  Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m                              \n",
       "2235  This is a Trans Siberian Kellogg named Alfonso. Huge ass eyeballs. Actually Dobby from Harry Potter. 7/10 https://t.co/XpseHBlAAb                                    \n",
       "2249  This is a Shotokon Macadamia mix named Cheryl. Sophisticated af. Looks like a disappointed librarian. Shh (lol) 9/10 https://t.co/J4GnJ5Swba                         \n",
       "2255  This is a rare Hungarian Pinot named Jessiga. She is either mid-stroke or got stuck in the washing machine. 8/10 https://t.co/ZU0i0KJyqD                             \n",
       "2264  This is a southwest Coriander named Klint. Hat looks expensive. Still on house arrest :(\\n9/10 https://t.co/IQTOMqDUIe                                               \n",
       "2273  This is a northern Wahoo named Kohl. He runs this town. Chases tumbleweeds. Draws gun wicked fast. 11/10 legendary https://t.co/J4vn2rOYFk                           \n",
       "2287  This is a Dasani Kingfisher from Maine. His name is Daryl. Daryl doesn't like being swallowed by a panda. 8/10 https://t.co/jpaeu6LNmW                               \n",
       "2304  This is a curly Ticonderoga named Pepe. No feet. Loves to jet ski. 11/10 would hug until forever https://t.co/cyDfaK8NBc                                             \n",
       "2311  This is a purebred Bacardi named Octaviath. Can shoot spaghetti out of mouth. 10/10 https://t.co/uEvsGLOFHa                                                          \n",
       "2314  This is a golden Buckminsterfullerene named Johm. Drives trucks. Lumberjack (?). Enjoys wall. 8/10 would hug softly https://t.co/uQbZJM2DQB                          \n",
       "2326  This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t                         \n",
       "2327  This is a southern Vesuvius bumblegruff. Can drive a truck (wow). Made friends with 5 other nifty dogs (neat). 7/10 https://t.co/LopTBkKa8h                          \n",
       "2333  This is an extremely rare horned Parthenon. Not amused. Wears shoes. Overall very nice. 9/10 would pet aggressively https://t.co/QpRjllzWAL                          \n",
       "2334  This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0                           \n",
       "2335  This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv                             \n",
       "2345  This is the happiest dog you will ever see. Very committed owner. Nice couch. 10/10 https://t.co/RhUEAloehK                                                          \n",
       "2346  Here is the Rand Paul of retrievers folks! He's probably good at poker. Can drink beer (lol rad). 8/10 good dog https://t.co/pYAJkAe76p                              \n",
       "2347  My oh my. This is a rare blond Canadian terrier on wheels. Only $8.98. Rather docile. 9/10 very rare https://t.co/yWBqbrzy8O                                         \n",
       "2348  Here is a Siberian heavily armored polar bear mix. Strong owner. 10/10 I would do unspeakable things to pet this dog https://t.co/rdivxLiqEt                         \n",
       "2349  This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc                           \n",
       "2350  This is a truly beautiful English Wilson Staff retriever. Has a nice phone. Privileged. 10/10 would trade lives with https://t.co/fvIbQfHjIe                         \n",
       "2352  This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx                            \n",
       "2353  Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR                                   \n",
       "2354  This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI                          \n",
       "\n",
       "            name   new_name  \n",
       "22    such        NaN        \n",
       "56    a           NaN        \n",
       "118   quite       NaN        \n",
       "169   quite       NaN        \n",
       "193   quite       NaN        \n",
       "335   not         NaN        \n",
       "369   one         NaN        \n",
       "542   incredibly  NaN        \n",
       "649   a           NaN        \n",
       "682   mad         NaN        \n",
       "759   an          NaN        \n",
       "773   very        NaN        \n",
       "801   a           NaN        \n",
       "819   very        NaN        \n",
       "822   just        NaN        \n",
       "852   my          Zoey       \n",
       "924   one         NaN        \n",
       "988   not         NaN        \n",
       "992   his         NaN        \n",
       "993   one         NaN        \n",
       "1002  a           NaN        \n",
       "1004  a           NaN        \n",
       "1017  a           NaN        \n",
       "1025  an          NaN        \n",
       "1031  very        NaN        \n",
       "1040  actually    NaN        \n",
       "1049  a           NaN        \n",
       "1063  just        NaN        \n",
       "1071  getting     NaN        \n",
       "1095  mad         NaN        \n",
       "...   ...         ...        \n",
       "2191  a           Leroi      \n",
       "2198  a           NaN        \n",
       "2204  an          Berta      \n",
       "2211  a           NaN        \n",
       "2212  the         NaN        \n",
       "2218  a           Chuk       \n",
       "2222  a           NaN        \n",
       "2235  a           Alfonso    \n",
       "2249  a           Cheryl     \n",
       "2255  a           Jessiga    \n",
       "2264  a           Klint      \n",
       "2273  a           Kohl       \n",
       "2287  a           Daryl      \n",
       "2304  a           Pepe       \n",
       "2311  a           Octaviath  \n",
       "2314  a           Johm       \n",
       "2326  quite       NaN        \n",
       "2327  a           NaN        \n",
       "2333  an          NaN        \n",
       "2334  a           NaN        \n",
       "2335  an          NaN        \n",
       "2345  the         NaN        \n",
       "2346  the         NaN        \n",
       "2347  a           NaN        \n",
       "2348  a           NaN        \n",
       "2349  an          NaN        \n",
       "2350  a           NaN        \n",
       "2352  a           NaN        \n",
       "2353  a           NaN        \n",
       "2354  a           NaN        \n",
       "\n",
       "[109 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 1.6.4: extract name-like string from tweet text\n",
    "\n",
    "# sub-dataframe of rows with dog names which are oridinary vocabularies\n",
    "df_names = df_archive_clean.query('name in @error_name').loc[:, ['text','name']]\n",
    "\n",
    "# extract dog-name, if any, following 'named' or 'name is'\n",
    "name_format = '(?:named|name is)\\s([A-Z][a-z]+)'\n",
    "df_names['new_name'] = df_names.text.str.extract(pat = name_format)\n",
    "\n",
    "# compare given names and names extracted from text\n",
    "pd.set_option('display.max_colwidth',-1)\n",
    "df_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset the option configured in assessment 1.6.4\n",
    "pd.reset_option('display.max_colwidth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 1.7: number of rows with more than one dog stages\n",
    "((df_archive_clean[['doggo', 'floofer', 'pupper', 'puppo']] != \"None\").sum(axis = 1) > 1).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Image Predictions\n",
    "__Quality Issue__\n",
    "1. Data type for the `tweet_id` column is integer, instead of string (object). \\[see _assessment 2.2_\\]\n",
    "\n",
    "__Tidiness Issue__\n",
    "1. Each set of three columns listed below are shown as column headers although these are not variable names. \\[see _assessment 2.1_\\]\n",
    "    * `p1`, `p2`, `p3`: 1st, 2nd, and 3rd predictions of a dog's breed\n",
    "    * `p1_conf`, `p2_conf`, `p3_conf`: confidence in the 1st, 2nd, and 3rd predictions of a dog's breed\n",
    "    * `p1_dog`, `p2_dog`, `p3_dog`: whether the 1st, 2nd, and 3rd predictions of a dog's breed is in fact a breed of dog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>849776966551130114</td>\n",
       "      <td>https://pbs.twimg.com/media/C8sDpDWWsAE5P08.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.292092</td>\n",
       "      <td>True</td>\n",
       "      <td>toy_terrier</td>\n",
       "      <td>0.136852</td>\n",
       "      <td>True</td>\n",
       "      <td>bonnet</td>\n",
       "      <td>0.103111</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>808106460588765185</td>\n",
       "      <td>https://pbs.twimg.com/media/Czb4iFRXgAIUMiN.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.426183</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.257447</td>\n",
       "      <td>True</td>\n",
       "      <td>Great_Pyrenees</td>\n",
       "      <td>0.126482</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>687664829264453632</td>\n",
       "      <td>https://pbs.twimg.com/media/CYsTg1XUsAEPjxE.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.957365</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.038559</td>\n",
       "      <td>True</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>806542213899489280</td>\n",
       "      <td>https://pbs.twimg.com/media/CzFp3FNW8AAfvV8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>vizsla</td>\n",
       "      <td>0.938617</td>\n",
       "      <td>True</td>\n",
       "      <td>Brittany_spaniel</td>\n",
       "      <td>0.036739</td>\n",
       "      <td>True</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.003971</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>707038192327901184</td>\n",
       "      <td>https://pbs.twimg.com/media/Cc_ney1W4AANuY3.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.642426</td>\n",
       "      <td>True</td>\n",
       "      <td>llama</td>\n",
       "      <td>0.057306</td>\n",
       "      <td>False</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.054186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "1894  849776966551130114  https://pbs.twimg.com/media/C8sDpDWWsAE5P08.jpg   \n",
       "1644  808106460588765185  https://pbs.twimg.com/media/Czb4iFRXgAIUMiN.jpg   \n",
       "746   687664829264453632  https://pbs.twimg.com/media/CYsTg1XUsAEPjxE.jpg   \n",
       "1637  806542213899489280  https://pbs.twimg.com/media/CzFp3FNW8AAfvV8.jpg   \n",
       "977   707038192327901184  https://pbs.twimg.com/media/Cc_ney1W4AANuY3.jpg   \n",
       "\n",
       "      img_num                p1   p1_conf  p1_dog                  p2  \\\n",
       "1894        2         Chihuahua  0.292092    True         toy_terrier   \n",
       "1644        1  golden_retriever  0.426183    True  Labrador_retriever   \n",
       "746         1               pug  0.957365    True      French_bulldog   \n",
       "1637        1            vizsla  0.938617    True    Brittany_spaniel   \n",
       "977         1               pug  0.642426    True               llama   \n",
       "\n",
       "       p2_conf  p2_dog                        p3   p3_conf  p3_dog  \n",
       "1894  0.136852    True                    bonnet  0.103111   False  \n",
       "1644  0.257447    True            Great_Pyrenees  0.126482    True  \n",
       "746   0.038559    True                toy_poodle  0.000667    True  \n",
       "1637  0.036739    True  Chesapeake_Bay_retriever  0.003971    True  \n",
       "977   0.057306   False            French_bulldog  0.054186    True  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 2.1\n",
    "df_image_clean.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      "tweet_id    2075 non-null int64\n",
      "jpg_url     2075 non-null object\n",
      "img_num     2075 non-null int64\n",
      "p1          2075 non-null object\n",
      "p1_conf     2075 non-null float64\n",
      "p1_dog      2075 non-null bool\n",
      "p2          2075 non-null object\n",
      "p2_conf     2075 non-null float64\n",
      "p2_dog      2075 non-null bool\n",
      "p3          2075 non-null object\n",
      "p3_conf     2075 non-null float64\n",
      "p3_dog      2075 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# assessment 2.2\n",
    "df_image_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Additional Tweet Data\n",
    "__Quality Issues__\n",
    "1. Unlike the two previous datasets, the name of the column for tweet IDs in this dataset is `id` instead of `tweet_id`. \\[see _assessments 3.1 and 3.2_\\]\n",
    "2. Data type for the `id` column is integer, instead of string (object). \\[see _assessment 3.2_\\]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>709179584944730112</td>\n",
       "      <td>698</td>\n",
       "      <td>2077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>673662677122719744</td>\n",
       "      <td>367</td>\n",
       "      <td>1266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>833863086058651648</td>\n",
       "      <td>2550</td>\n",
       "      <td>13966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>798682547630837760</td>\n",
       "      <td>5116</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>696405997980676096</td>\n",
       "      <td>1203</td>\n",
       "      <td>3337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id  retweet_count  favorite_count\n",
       "1259  709179584944730112            698            2077\n",
       "1935  673662677122719744            367            1266\n",
       "315   833863086058651648           2550           13966\n",
       "586   798682547630837760           5116               0\n",
       "1432  696405997980676096           1203            3337"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# assessment 3.1\n",
    "df_json_clean.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2338 entries, 0 to 2337\n",
      "Data columns (total 3 columns):\n",
      "id                2338 non-null int64\n",
      "retweet_count     2338 non-null int64\n",
      "favorite_count    2338 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 54.9 KB\n"
     ]
    }
   ],
   "source": [
    "# assessment 3.2\n",
    "df_json_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean'></a>\n",
    "## Cleaning Data\n",
    "Each issue identified in __Assessing Data__ was addressed in three sequential steps.\n",
    "1. _Define_: translate the assessment into an actionable plan which describes the necessary cleaning operations\n",
    "2. _Code_: translate the plan into codes and programmatically perform the cleaning operation\n",
    "3. _Test_: verify that the operation resolved the issue\n",
    "\n",
    "After addressing all issues, the cleaned datasets were merged by their common Tweet IDs. The merged dataset was stored as a separate file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Enhanced Twitter Archive\n",
    "__Quality Issues__\n",
    "#### 1.1 Data type for the `tweet_id` column is integer, instead of string (object).\n",
    "__1.1.1 Define__\n",
    "\n",
    "Convert the data type of the `tweet_id` column from integer to string (object)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.1.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_clean.tweet_id = df_archive_clean.tweet_id.astype(dtype = 'str')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.1.3 Test__\n",
    "\n",
    "The data type for the `tweet_id` column was successfully converted to string (object)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.tweet_id.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 The two columns `in_reply_to_status_id` and `in_reply_to_user_id` appear to be outside the scope of interest of this project.\n",
    "__1.2.1 Define__\n",
    "\n",
    "Remove the two columns `in_reply_to_status_id` and `in_reply_to_user_id` from the dataframe object `df_archive_clean`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.2.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_clean.drop(columns = ['in_reply_to_status_id', 'in_reply_to_user_id'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.2.3 Test__\n",
    "\n",
    "The two columns `in_reply_to_status_id` and `in_reply_to_user_id` were successfully removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'retweeted_status_id',\n",
       "       'retweeted_status_user_id', 'retweeted_status_timestamp',\n",
       "       'expanded_urls', 'rating_numerator', 'rating_denominator', 'name',\n",
       "       'doggo', 'floofer', 'pupper', 'puppo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 Data type for the `timestamp` column is object, instead of datetime.\n",
    "__1.3.1 Define__\n",
    "\n",
    "Convert the data type of the `timestamp` column from object to datetime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.3.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_clean.timestamp = pd.to_datetime(arg = df_archive_clean.timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.3.3 Test__\n",
    "\n",
    "The data type of the `timestamp` column was successfully converted to datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime64[ns, UTC]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.timestamp.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4 The four possible values for the `source` column not only indicate the source of the tweet but includes the HTML tags and attributes.\n",
    "__1.4.1 Define__\n",
    "* Remove the HTML tags and attributes from the values of the `source` column by mapping these values to their corresponding sources defined in a dictionary object.\n",
    "* Convert the data type of the `source` column from string (object) to category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.4.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# empty dictionary for storing mapping of sources\n",
    "source_dict = {}\n",
    "\n",
    "for source in df_archive_clean.source.unique():\n",
    "    source_dict[source] = source[source.find('>')+1:source.find('</a>')]\n",
    "    # source.find('>')+1: starting position of source name\n",
    "    # source.find('</a>'): ending position + 1 of source name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map sources to the source names defined in the above dictionary\n",
    "def map_source(dataframe):\n",
    "    if dataframe['source'] in source_dict.keys():\n",
    "        return source_dict[dataframe['source']]\n",
    "\n",
    "df_archive_clean['source'] = df_archive_clean.apply(map_source, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert data type to category\n",
    "df_archive_clean.source = df_archive_clean.source.astype(dtype = 'category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.4.3 Test__\n",
    "After the values of the `source` column were shortened by removing the HTML tags and attributes, the data type of the column was successfully converted to category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Twitter for iPhone\n",
      "2 Twitter Web Client\n",
      "3 Vine - Make a Scene\n",
      "4 TweetDeck\n"
     ]
    }
   ],
   "source": [
    "# unique values for source column\n",
    "for i, source in enumerate(df_archive_clean.source.unique()):\n",
    "    print(i+1, source)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['TweetDeck', 'Twitter Web Client', 'Twitter for iPhone',\n",
       "                  'Vine - Make a Scene'],\n",
       "                 ordered=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.source.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.5 181 tweets were created by re-tweeting existing tweets. All retweets have non-`NaN` values, and all original tweets have `NaN` for the three columns `retweeted_status_id`, `retweeted_status_user_id`, `retweeted_status_timestamp`.\n",
    "__1.5.1 Define__\n",
    "\n",
    "1. Remove all retweets by dropping rows with non-`NaN` values for `retweeted_status_id`(, `retweeted_status_user_id`, or `retweeted_status_timestamp`).\n",
    "2. Drop all three columns, `retweeted_status_id`, `retweeted_status_user_id`, and `retweeted_status_timestamp`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.5.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# indices of original tweets\n",
    "ind = df_archive_clean[['retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp']].isna().any(axis = 1)\n",
    "\n",
    "# keep only original tweets\n",
    "df_archive_clean = df_archive_clean.loc[ind, :]\n",
    "\n",
    "# reset index\n",
    "df_archive_clean.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove three columns\n",
    "df_archive_clean.drop(columns = ['retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.5.3 Test__\n",
    "* The dataset has 2175 rows, also the expected number of original tweets.\n",
    "* The three columns, `retweeted_status_id`, `retweeted_status_user_id`, and `retweeted_status_timestamp`, were successfully removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2175 entries, 0 to 2174\n",
      "Data columns (total 12 columns):\n",
      "tweet_id              2175 non-null object\n",
      "timestamp             2175 non-null datetime64[ns, UTC]\n",
      "source                2175 non-null category\n",
      "text                  2175 non-null object\n",
      "expanded_urls         2117 non-null object\n",
      "rating_numerator      2175 non-null int64\n",
      "rating_denominator    2175 non-null int64\n",
      "name                  2175 non-null object\n",
      "doggo                 2175 non-null object\n",
      "floofer               2175 non-null object\n",
      "pupper                2175 non-null object\n",
      "puppo                 2175 non-null object\n",
      "dtypes: category(1), datetime64[ns, UTC](1), int64(2), object(8)\n",
      "memory usage: 189.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.6 The `expanded_urls` column does not show meaningful information besides the tweet id which is already listed under the `tweet_id` column.\n",
    "__1.6.1 Define__\n",
    "\n",
    "Remove the `expanded_urls` column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.6.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_clean.drop(columns = 'expanded_urls', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.6.3 Test__\n",
    "\n",
    "The `expanded_urls` column was successfully removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.7 23 dog ratings extracted from the tweet are inaccurate in that these ratings have values for the `rating_denominator` column other than 10.\n",
    "__1.7.1 Define__\n",
    "\n",
    "* Convert all `NaN` values under the `rating` column of the dataframe object `df_ratings` to the string `0/0`.\n",
    "* Correct the `rating_numerator` and `rating_denominator` columns in the original dataframe based on the values under the `rating` column of the dataframe object `df_ratings`.\n",
    "    * Split the values under the `rating` column by the separator `/`.\n",
    "    * Assign the first of the two splitted values to `rating_numerator` and the second to `rating_denominator`.\n",
    "    * Convert the data type of the splitted values from string to integer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.7.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# re-create df_ratings from assessment 1.5.2 because indices were updated during previous cleaning operations\n",
    "\n",
    "# sub-dataframe for rows with rating_denominator values besides 10 \n",
    "df_ratings = df_archive_clean.query('rating_denominator != 10').loc[:,['text', 'rating_numerator', 'rating_denominator']]\n",
    "\n",
    "# extract rating, if any, of format digit(s)/10\n",
    "rating_format = '(\\d+/10)'\n",
    "df_ratings['rating'] = df_ratings.text.str.extract(pat = rating_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert NaN's to 0/0\n",
    "df_ratings.rating.fillna(value = '0/0', inplace = True)\n",
    "\n",
    "# split ratings by /, assign values to numerator and denominator, and convert dtype to int\n",
    "df_archive_clean.loc[df_ratings.index, 'rating_numerator'] = df_ratings.rating.str.split('/').str[0].astype(dtype = 'int')\n",
    "df_archive_clean.loc[df_ratings.index, 'rating_denominator'] = df_ratings.rating.str.split('/').str[1].astype(dtype = 'int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.7.3 Test__\n",
    "\n",
    "The only remaining rows with values for the `rating_denominator` column besides 10 are those for which no rating is available in the text of the original tweet. A _rating_ of `0/0` instead of `NaN` was assigned to all instances of this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 16)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_archive_clean.rating_denominator != 10).sum(), (df_ratings.rating == '0/0').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.8 109 names of dogs in the `name` column are inaccurate.\n",
    "__1.8.1 Define__\n",
    "\n",
    "Replace the erroneous names in the `name` column with the values under the `new_name` column of the dataframe object `df_names`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.8.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# re-create df_names from assessment 1.6.4 because indices were updated during previous cleaning operations\n",
    "\n",
    "# sub-dataframe of rows with dog names which are oridinary vocabularies\n",
    "df_names = df_archive_clean.query('name in @error_name').loc[:, ['text','name']]\n",
    "\n",
    "# extract dog-name, if any, following 'named' or 'name is'\n",
    "name_format = '(?:named|name is)\\s([A-Z][a-z]+)'\n",
    "df_names['new_name'] = df_names.text.str.extract(pat = name_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace erroneous names\n",
    "df_archive_clean.loc[df_names.index, 'name'] = df_names['new_name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.8.3 Test__\n",
    "\n",
    "Erroneous names in the `name` column were successfully replaced with the correct names of the dogs. If the corresponding tweet did not introduce the name of the dog, then `NaN` was assigned, thus still replacing any erroneous names which were ordinary words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104, 104)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.loc[df_names.index, 'name'].isna().shape[0], df_names.new_name.isna().shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.9 The five columns, `name`, `doggo`, `floofer`, `pupper`, and `puppo`, use \"None\" instead of `NaN` for missing values.\n",
    "__1.9.1 Define__\n",
    "\n",
    "Replace the all instances of \"None\" in the five columns with `NaN`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.9.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name 680\n",
      "doggo 2088\n",
      "floofer 2165\n",
      "pupper 1941\n",
      "puppo 2150\n"
     ]
    }
   ],
   "source": [
    "# columns to be corrected\n",
    "column_list = ['name', 'doggo', 'floofer', 'pupper','puppo']\n",
    "\n",
    "# number of instances of None in each column\n",
    "for column in column_list:\n",
    "    print(column, df_archive_clean[df_archive_clean[column] == \"None\"].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace None with NaN\n",
    "for column in column_list:\n",
    "    df_archive_clean.loc[df_archive_clean[df_archive_clean[column] == \"None\"].index, column] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.9.3 Test__\n",
    "\n",
    "No instance of \"None\" was found in any of the five columns after the replacement with `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name 0\n",
      "doggo 0\n",
      "floofer 0\n",
      "pupper 0\n",
      "puppo 0\n"
     ]
    }
   ],
   "source": [
    "# number of instances of None remaining in each column\n",
    "for column in column_list:\n",
    "    print(column, df_archive_clean[df_archive_clean[column] == \"None\"].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.10 14 Tweets have multiple dog stages.\n",
    "This issue was resolved by addressing the tidiness issue below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Tidiness Issue__\n",
    "#### 1. Four possible dog \"stages\", doggo, floofer, pupper, and puppo, are shown as column headers although these \"stages\" are not variable names.\n",
    "__1.1 Define__\n",
    "\n",
    "* Extract and join the stage from each row across the four columns `doggo`, `floofer`, `pupper`, and `puppo`.\n",
    "* Assign the stage to a new column labeled as `stage`.\n",
    "* Drop the four columns `doggo`, `floofer`, `pupper`, and `puppo`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# sub-dataframe of columns representing stages\n",
    "df_stages = df_archive_clean.loc[:, ['doggo', 'floofer', 'pupper', 'puppo']]\n",
    "\n",
    "# join stage from the four columns in each row\n",
    "df_archive_clean['stage'] = df_stages.apply(lambda x: ','.join(x.dropna()), axis = 1)\n",
    "\n",
    "# replace empty values with NaN\n",
    "df_archive_clean['stage'].replace(to_replace = '', value = np.nan, inplace = True)\n",
    "\n",
    "# drop the four columns\n",
    "df_archive_clean.drop(columns = ['doggo', 'floofer', 'pupper', 'puppo'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__1.3 Test__\n",
    "* The four columns `doggo`, `floofer`, `pupper`, and `puppo` were successfully removed from the dataframe.\n",
    "* The new column `stage` successfully captured both single stages and multiple stages. Number of tweets with multiple stages decreased from 14 to 12 because the cleaned dataset now includes original tweets only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'stage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remaining columns\n",
    "df_archive_clean.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper           224\n",
       "doggo             75\n",
       "puppo             24\n",
       "doggo,pupper      10\n",
       "floofer            9\n",
       "doggo,floofer      1\n",
       "doggo,puppo        1\n",
       "Name: stage, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of instances of each stage\n",
    "df_archive_clean['stage'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Image Predictions\n",
    "__Quality Issue__\n",
    "#### 2.1 Data type for the `tweet_id` column is integer, instead of string (object).\n",
    "__2.1.1 Define__\n",
    "\n",
    "Convert the data type of the `tweet_id` column from integer to string (object)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__2.1.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_image_clean.tweet_id = df_image_clean.tweet_id.astype(dtype = 'str')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__2.1.3 Test__\n",
    "\n",
    "The data type of the `tweet_id` column was successfully converted to string (object)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_image_clean.tweet_id.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Tidiness Issue__\n",
    "#### 2.1 Three sets of three columns are shown as column headers although these are not variable names.\n",
    "__2.1.1 Define__\n",
    "\n",
    "* For each set of the three columns, unpivot the three columns, rank the predictions under a new variable `prediction`, and assign the values under a new value column.\n",
    "    * Assign the values of `p1`, `p2`, `p3` to a new column `breed`.\n",
    "    * Assign the values of `p1_conf`, `p2_conf`, `p3_conf` to a new column `confidence`.\n",
    "    * Assign the values of `p1_dog`, `p2_dog`, `p3_dog` to a new column `dog_breed`.\n",
    "* Join all three tidy datasets and assign the resulting object to the original dataframe object `df_image_clean`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__2.1.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# unpivot the dataframe for each set of three columns\n",
    "df_breed = df_image_clean.melt(id_vars = ['tweet_id', 'jpg_url', 'img_num']\n",
    "                               , value_vars = ['p1', 'p2', 'p3']\n",
    "                               , var_name = 'prediction'\n",
    "                               , value_name = 'breed')\n",
    "df_confidence = df_image_clean.melt(id_vars = ['tweet_id', 'jpg_url', 'img_num']\n",
    "                               , value_vars = ['p1_conf', 'p2_conf', 'p3_conf']\n",
    "                               , var_name = 'prediction'\n",
    "                               , value_name = 'confidence')\n",
    "df_check = df_image_clean.melt(id_vars = ['tweet_id', 'jpg_url', 'img_num']\n",
    "                               , value_vars = ['p1_dog', 'p2_dog', 'p3_dog']\n",
    "                               , var_name = 'prediction'\n",
    "                               , value_name = 'dog_breed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reconcile the ranks for predictions to p1, p2, and p3 for all three sub-dataframes\n",
    "df_confidence.prediction = df_confidence.prediction.str.split('_').str[0]\n",
    "df_check.prediction = df_check.prediction.str.split('_').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge first two sub-dataframes\n",
    "df2_image = df_breed.merge(right = df_confidence\n",
    "                           , on = ['tweet_id', 'jpg_url', 'img_num', 'prediction']\n",
    "                           , how = 'inner')\n",
    "\n",
    "# merge the last sub-dataframe\n",
    "df3_image = df2_image.merge(right = df_check\n",
    "                            , on = ['tweet_id', 'jpg_url', 'img_num', 'prediction']\n",
    "                            , how = 'inner')\n",
    "\n",
    "# assign the merged datasets to the original dataframe\n",
    "df3_image.sort_values(by = ['tweet_id', 'img_num', 'prediction'], inplace = True)\n",
    "df3_image.reset_index(inplace = True, drop = True)\n",
    "df_image_clean = df3_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__2.1.3 Test__\n",
    "\n",
    "Three sets of three columns were successfully tidied from a wide format to a long format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <td>German_shepherd</td>\n",
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       "      <td>malinois</td>\n",
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       "      <td>bloodhound</td>\n",
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       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
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       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
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       "      <th>14</th>\n",
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       "      <td>p3</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
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       "    <tr>\n",
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       "      <td>Bernese_mountain_dog</td>\n",
       "      <td>0.651137</td>\n",
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       "      <td>p2</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.263788</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>666050758794694657</td>\n",
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       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>Greater_Swiss_Mountain_dog</td>\n",
       "      <td>0.016199</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.045885</td>\n",
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       "    </tr>\n",
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      ],
      "text/plain": [
       "              tweet_id                                          jpg_url  \\\n",
       "0   666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1   666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "2   666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "3   666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "4   666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "5   666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "6   666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "7   666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "8   666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "9   666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "10  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "11  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "12  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "13  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "14  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "15  666050758794694657  https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg   \n",
       "16  666050758794694657  https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg   \n",
       "17  666050758794694657  https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg   \n",
       "18  666051853826850816  https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg   \n",
       "19  666051853826850816  https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg   \n",
       "\n",
       "    img_num prediction                       breed  confidence  dog_breed  \n",
       "0         1         p1      Welsh_springer_spaniel    0.465074       True  \n",
       "1         1         p2                      collie    0.156665       True  \n",
       "2         1         p3           Shetland_sheepdog    0.061428       True  \n",
       "3         1         p1                     redbone    0.506826       True  \n",
       "4         1         p2          miniature_pinscher    0.074192       True  \n",
       "5         1         p3         Rhodesian_ridgeback    0.072010       True  \n",
       "6         1         p1             German_shepherd    0.596461       True  \n",
       "7         1         p2                    malinois    0.138584       True  \n",
       "8         1         p3                  bloodhound    0.116197       True  \n",
       "9         1         p1         Rhodesian_ridgeback    0.408143       True  \n",
       "10        1         p2                     redbone    0.360687       True  \n",
       "11        1         p3          miniature_pinscher    0.222752       True  \n",
       "12        1         p1          miniature_pinscher    0.560311       True  \n",
       "13        1         p2                  Rottweiler    0.243682       True  \n",
       "14        1         p3                    Doberman    0.154629       True  \n",
       "15        1         p1        Bernese_mountain_dog    0.651137       True  \n",
       "16        1         p2            English_springer    0.263788       True  \n",
       "17        1         p3  Greater_Swiss_Mountain_dog    0.016199       True  \n",
       "18        1         p1                  box_turtle    0.933012      False  \n",
       "19        1         p2                  mud_turtle    0.045885      False  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_image_clean.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Additional Tweet Data\n",
    "__Quality Issues__\n",
    "#### 3.1 Data type for the `id` column is integer, instead of string (object).\n",
    "__3.1.1 Define__\n",
    "\n",
    "Convert the data type of the `id` column from integer to string (object)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__3.1.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_json_clean.id = df_json_clean.id.astype(dtype = 'str')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__3.1.3 Test__\n",
    "\n",
    "The data type of the `id` column was successfully converted to string (object)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__3.1.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_json_clean.id.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 Unlike the two previous datasets, the name of the column for tweet IDs in this dataset is `id` instead of `tweet_id`.\n",
    "__3.2.1 Define__\n",
    "\n",
    "Change the name of the `id` column to `tweet_id`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__3.2.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_json_clean.rename(columns = {'id':'tweet_id'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__3.2.3 Test__\n",
    "\n",
    "The name of the `id` column was successfully changed to `tweet_id`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'retweet_count', 'favorite_count'], dtype='object')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_json_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Merge and Store Cleaned Datasets\n",
    "#### 4.1 Twitter Archive Data\n",
    "__4.1.1 Define__\n",
    "\n",
    "Merge the two dataframe objects `df_archive_clean` and `df_json_clean`, which were cleaned in the first and third sections of __Cleaning Data__, by the Tweet IDs shared between the two."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__4.1.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine two datasets\n",
    "df_archive_master = df_archive_clean.merge(right = df_json_clean\n",
    "                                           , on = 'tweet_id'\n",
    "                                           , how = 'inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__4.1.3 Test__\n",
    "\n",
    "The two dataframe objects `df_archive_clean` and `df_json_clean` were successfully joined by the Tweet IDs shared between the two. Note that the combined dataset has fewer number of rows than those of the individual datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>stage</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>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8212</td>\n",
       "      <td>37667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6072</td>\n",
       "      <td>32353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4015</td>\n",
       "      <td>24372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8368</td>\n",
       "      <td>40973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9067</td>\n",
       "      <td>39184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                 timestamp              source  \\\n",
       "0  892420643555336193 2017-08-01 16:23:56+00:00  Twitter for iPhone   \n",
       "1  892177421306343426 2017-08-01 00:17:27+00:00  Twitter for iPhone   \n",
       "2  891815181378084864 2017-07-31 00:18:03+00:00  Twitter for iPhone   \n",
       "3  891689557279858688 2017-07-30 15:58:51+00:00  Twitter for iPhone   \n",
       "4  891327558926688256 2017-07-29 16:00:24+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                text  rating_numerator  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                13   \n",
       "1  This is Tilly. She's just checking pup on you....                13   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                12   \n",
       "3  This is Darla. She commenced a snooze mid meal...                13   \n",
       "4  This is Franklin. He would like you to stop ca...                12   \n",
       "\n",
       "   rating_denominator      name stage  retweet_count  favorite_count  \n",
       "0                  10   Phineas   NaN           8212           37667  \n",
       "1                  10     Tilly   NaN           6072           32353  \n",
       "2                  10    Archie   NaN           4015           24372  \n",
       "3                  10     Darla   NaN           8368           40973  \n",
       "4                  10  Franklin   NaN           9067           39184  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_master.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2175, 2338, 2171)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of rows in each individual dataset and the merged dataset\n",
    "df_archive_clean.shape[0], df_json_clean.shape[0], df_archive_master.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array of unique tweet ids after merging archive data and json data\n",
    "arr1 = df_archive_master.tweet_id.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2 Image Predictions\n",
    "The cleaned dataset `df_image_clean` shares with the two datasets which were combined above the same observational unit, Tweets. Thus, the clean version of the image predictions data also needs to be combined to the merged dataset. Note that this merge would result in a dataset with multiple rows of data for each Tweet ID because the clean version of the image predictions data include multiple rows of data for each Tweet ID by levels of predictions.\n",
    "\n",
    "__4.2.1 Define__\n",
    "\n",
    "Merge the dataframe object `df_image_clean` with the combined dataframe object `df_archive_master`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__4.2.2 Code__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine two datasets\n",
    "df_archive_master = df_archive_master.merge(right = df_image_clean\n",
    "                                            , on = 'tweet_id'\n",
    "                                            , how = 'inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__4.2.3 Test__\n",
    "\n",
    "The clean version of the image predictions data was successfully combined with the clean version of the enhanced Twitter archive data. Note that the merged dataset now includes three rows of data for each unique Tweet ID; however, each set of three rows of data is not a duplicate in that the last six columns of this dataset which were originally from the image predictions dataset contain data on three different levels of predictions of dog breeds for each Tweet ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</td>\n",
       "      <td>p3</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6072</td>\n",
       "      <td>32353</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
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       "      <th>4</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6072</td>\n",
       "      <td>32353</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>p2</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                 timestamp              source  \\\n",
       "0  892420643555336193 2017-08-01 16:23:56+00:00  Twitter for iPhone   \n",
       "1  892420643555336193 2017-08-01 16:23:56+00:00  Twitter for iPhone   \n",
       "2  892420643555336193 2017-08-01 16:23:56+00:00  Twitter for iPhone   \n",
       "3  892177421306343426 2017-08-01 00:17:27+00:00  Twitter for iPhone   \n",
       "4  892177421306343426 2017-08-01 00:17:27+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                text  rating_numerator  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                13   \n",
       "1  This is Phineas. He's a mystical boy. Only eve...                13   \n",
       "2  This is Phineas. He's a mystical boy. Only eve...                13   \n",
       "3  This is Tilly. She's just checking pup on you....                13   \n",
       "4  This is Tilly. She's just checking pup on you....                13   \n",
       "\n",
       "   rating_denominator     name stage  retweet_count  favorite_count  \\\n",
       "0                  10  Phineas   NaN           8212           37667   \n",
       "1                  10  Phineas   NaN           8212           37667   \n",
       "2                  10  Phineas   NaN           8212           37667   \n",
       "3                  10    Tilly   NaN           6072           32353   \n",
       "4                  10    Tilly   NaN           6072           32353   \n",
       "\n",
       "                                           jpg_url  img_num prediction  \\\n",
       "0  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg        1         p1   \n",
       "1  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg        1         p2   \n",
       "2  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg        1         p3   \n",
       "3  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg        1         p1   \n",
       "4  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg        1         p2   \n",
       "\n",
       "       breed  confidence  dog_breed  \n",
       "0     orange    0.097049      False  \n",
       "1      bagel    0.085851      False  \n",
       "2     banana    0.076110      False  \n",
       "3  Chihuahua    0.323581       True  \n",
       "4   Pekinese    0.090647       True  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_master.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The operations performed below provide insights into the changes in the number of unique Tweet IDs as a result of merging image predictions data with enhanced Twitter archive data. After the two datasets, each with 2171 and 2075 unique Tweet IDs, were combined, the number of unique Tweet IDs in the merged dataset was reduced to 1990, the number of unique Tweet IDs shared by both datasets which were merged. Because the merged dataset contains three rows of data for each Tweet ID, the final dataset contains a total of 5970 rows of data or three times the number of unique Tweet IDs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2171, 2075, 1990)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# array of unique tweet ids in cleaned image predictions data\n",
    "arr2 = df_image_clean.tweet_id.unique()\n",
    "\n",
    "# number of unique tweet ids in individual datasets and merged dataset\n",
    "arr1.shape[0], arr2.shape[0], np.intersect1d(arr1, arr2).shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of rows in merged dataset = number of unique tweet ids in merged dataset * 3\n",
    "df_archive_master.shape[0] == np.intersect1d(arr1, arr2).shape[0] * 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3 Store Merged Dataset\n",
    "Having tested and verified that the merge was successfully completed, store the merged dataset `df_archive_master` as a separate `.csv` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_master.to_csv('data/twitter_archive_master.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusion'></a>\n",
    "## Conclusion\n",
    "\n",
    "This document covered the first section of the data analysis project, wrangling data on Tweets from _WeRateDogs (@dogrates)_. Data wrangling began with gathering three datasets. First dataset which Udacity made readily available for manual download represents cases in which datasets subject to analysis for certain projects are provided by an entity or a project manager. The last two datasets which were programmatically downloaded by using _Requests_ and _Tweepy_ packages represent cases in which data analysts or data scientists scrape data available online, a method which is most appropriate for reproducibility and scalability.\n",
    "\n",
    "Next, these datasets were assessed for quality and tidiness issues or problems in the content and structure of the data, which  would significantly complicate the processes for analyzing data and creating visualizations to derive insights. Although the project made attempts to perform a comprehensive assessment of the data, the assessments documented here may not be the only issues that must be addressed depending on the questions or problems of interest. In fact, potential cases in which questions or problems are not defined until the exploratory data analysis phase suggest the need to re-visit the assessments if these do not streamline the investigation of the questions and problems of interest. Also, note that assessing data not only encompasses identification of issues but covers simply understanding the content and structure of the data. __Assessing Data__ would be even more comprehensive if comments on the basic content and structure of the three datasets were documented as well.\n",
    "\n",
    "Effective assessments of the quality and tidiness issues facilitated the next step of data wrangling, _cleaning data_. For each assessment, a step-by-step actionable plan for addressing the issue was documented, which were then translated into codes. After executing the codes, the results were tested to verify whether the operation resolved the identified issue. If the results show another issue that would potentially affect the data analysis and visualization, then the three steps, _define_, _code_, and _test_, need to be repeated to address the new issue.\n",
    "\n",
    "At the end of the data wrangling phase, cleaned datasets were merged and stored as a separate file and made available for the next part of this project, __Part 2: Exploratory Data Analysis (EDA) and Data Visualization__. These now functional datasets are expected to facilitate the investigation and augmentation of the data to maximize the potential of the analyses and deriving insights from visualizations. However, based on the questions or problems raised in this phase of the data analysis, some steps from data wrangling may need to be repeated. If the gathered data is insufficient to address these questions or problems, then all steps from data wrangling need to be repeated. As such, data wrangling definitely defines a bulk of the data analysis and is likely iterated throughout the course of the analysis to ensure that analyzing and visualizing data to draw insights becomes possible, at the least."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='reference'></a>\n",
    "## Reference\n",
    "Dasu T, Johnson T (2003). _Exploratory Data Mining and Data Cleaning._ John Wiley & Sons.\n",
    "\n",
    "Wickham, H. (2014). Tidy Data. _Journal of Statistical Software, 59_(10). doi:10.18637/jss.v059.i10"
   ]
  }
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