wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b4a4953",
   "metadata": {},
   "source": [
    "# Project: Wrangling and Analyze Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc4751f3",
   "metadata": {},
   "source": [
    "# Table of contents\n",
    "1. [Data Gathering](#gathering)\n",
    "2. [Assessing Data](#assess)\n",
    " 1. [Quality Issues](#quality)\n",
    " 2. [Tidiness Issues](#tidiness)\n",
    "3. [Cleaning Data](#clean)\n",
    "4. [Storing Data](#storing)\n",
    "5. [Analyzing and Visualizing Data](#analyzing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "c99718c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install tweepy\n",
    "import requests\n",
    "import os\n",
    "import pandas as pd\n",
    "import tweepy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "404d525c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76c316b",
   "metadata": {},
   "source": [
    "## Data Gathering\n",
    "<a id=\"gathering\"></a>\n",
    "In the cell below, gather **all** three pieces of data for this project and load them in the notebook. **Note:** the methods required to gather each data are different.\n",
    "1. Directly download the WeRateDogs Twitter archive data (twitter_archive_enhanced.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "19cb49df",
   "metadata": {},
   "outputs": [],
   "source": [
    "archive= pd.read_csv('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "596dde66",
   "metadata": {},
   "source": [
    "2. Use the Requests library to download the tweet image prediction (image_predictions.tsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "de833da0",
   "metadata": {},
   "outputs": [],
   "source": [
    "url= 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "response= requests.get(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d6259717",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open (os.path.join(url.split('/')[-1]), mode='wb') as file:\n",
    "    file.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "6385970f",
   "metadata": {},
   "outputs": [],
   "source": [
    "image=pd.read_csv('image-predictions.tsv',sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7d2ccdb",
   "metadata": {},
   "source": [
    "3. Use the Tweepy library to query additional data via the Twitter API (tweet_json.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0c7a939",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "from tweepy import OAuthHandler\n",
    "import json\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "# Query Twitter API for each tweet in the Twitter archive and save JSON in a text file\n",
    "# These are hidden to comply with Twitter's API terms and conditions\n",
    "consumer_key = 'HIDDEN'\n",
    "consumer_secret = 'HIDDEN'\n",
    "access_token = 'HIDDEN'\n",
    "access_secret = 'HIDDEN'\n",
    "\n",
    "auth = OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True)\n",
    "\n",
    "# NOTE TO STUDENT WITH MOBILE VERIFICATION ISSUES:\n",
    "# archive is a DataFrame with the twitter_archive_enhanced.csv file. You may have to\n",
    "# change line 17 to match the name of your DataFrame with twitter_archive_enhanced.csv\n",
    "# NOTE TO REVIEWER: this student had mobile verification issues so the following\n",
    "# Twitter API code was sent to this student from a Udacity instructor\n",
    "# Tweet IDs for which to gather additional data via Twitter's API\n",
    "tweet_ids = archive.tweet_id.values\n",
    "len(tweet_ids)\n",
    "\n",
    "# Query Twitter's API for JSON data for each tweet ID in the Twitter archive\n",
    "count = 0\n",
    "fails_dict = {}\n",
    "start = timer()\n",
    "# Save each tweet's returned JSON as a new line in a .txt file\n",
    "with open('tweet_json.txt', 'w') as outfile:\n",
    "    # This loop will likely take 20-30 minutes to run because of Twitter's rate limit\n",
    "    for tweet_id in tweet_ids:\n",
    "        count += 1\n",
    "        print(str(count) + \": \" + str(tweet_id))\n",
    "        try:\n",
    "            tweet = api.get_status(tweet_id, tweet_mode='extended')\n",
    "            print(\"Success\")\n",
    "            json.dump(tweet._json, outfile)\n",
    "            outfile.write('\\n')\n",
    "        except tweepy.TweepError as e:\n",
    "            print(\"Fail\")\n",
    "            fails_dict[tweet_id] = e\n",
    "            pass\n",
    "end = timer()\n",
    "print(end - start)\n",
    "print(fails_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d9cc2d5",
   "metadata": {},
   "source": [
    "#### reading tweet_json.txt to a dataframe (tweet id, retweet count, favorite tweet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b670ce8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = []\n",
    "with open('tweet_json.txt', encoding='utf-8') as file:\n",
    "    lines= file.readlines()\n",
    "    for line in lines:\n",
    "        line=json.loads(line)\n",
    "        tweet_id=line['id']\n",
    "        retweet_count=line['retweet_count']\n",
    "        favorite_count=line['favorite_count']\n",
    "        df_list.append({'tweet_id':tweet_id,\n",
    "                       'retweet_count':retweet_count,\n",
    "                       'favorite_count':favorite_count})\n",
    "additional_info=pd.DataFrame(df_list,columns=['tweet_id','retweet_count','favorite_count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95adb56f",
   "metadata": {},
   "source": [
    "## Assessing Data\n",
    "<a id=\"assess\"></a>\n",
    "In this section, detect and document at least **eight (8) quality issues and two (2) tidiness issue**. You must use **both** visual assessment\n",
    "programmatic assessement to assess the data.\n",
    "\n",
    "**Note:** pay attention to the following key points when you access the data.\n",
    "\n",
    "* You only want original ratings (no retweets) that have images. Though there are 5000+ tweets in the dataset, not all are dog ratings and some are retweets.\n",
    "* Assessing and cleaning the entire dataset completely would require a lot of time, and is not necessary to practice and demonstrate your skills in data wrangling. Therefore, the requirements of this project are only to assess and clean at least 8 quality issues and at least 2 tidiness issues in this dataset.\n",
    "* The fact that the rating numerators are greater than the denominators does not need to be cleaned. This [unique rating system](http://knowyourmeme.com/memes/theyre-good-dogs-brent) is a big part of the popularity of WeRateDogs.\n",
    "* You do not need to gather the tweets beyond August 1st, 2017. You can, but note that you won't be able to gather the image predictions for these tweets since you don't have access to the algorithm used.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "06be64c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>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",
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       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
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       "      <td>13</td>\n",
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       "      <td>2017-07-31 00:18:03 +0000</td>\n",
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       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
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       "      <td>None</td>\n",
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       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
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       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
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       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
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       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
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       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
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       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2356 rows × 17 columns</p>\n",
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      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0     892420643555336193                    NaN                  NaN   \n",
       "1     892177421306343426                    NaN                  NaN   \n",
       "2     891815181378084864                    NaN                  NaN   \n",
       "3     891689557279858688                    NaN                  NaN   \n",
       "4     891327558926688256                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "2351  666049248165822465                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "2355  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "0     2017-08-01 16:23:56 +0000   \n",
       "1     2017-08-01 00:17:27 +0000   \n",
       "2     2017-07-31 00:18:03 +0000   \n",
       "3     2017-07-30 15:58:51 +0000   \n",
       "4     2017-07-29 16:00:24 +0000   \n",
       "...                         ...   \n",
       "2351  2015-11-16 00:24:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "2355  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "0     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "0     This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1     This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2     This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3     This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4     This is Franklin. He would like you to stop ca...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "2351  Here we have a 1949 1st generation vulpix. Enj...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                          NaN                        NaN   \n",
       "1                          NaN                        NaN   \n",
       "2                          NaN                        NaN   \n",
       "3                          NaN                        NaN   \n",
       "4                          NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2351                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "0     https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1     https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2     https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3     https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4     https://twitter.com/dog_rates/status/891327558...                12   \n",
       "...                                                 ...               ...   \n",
       "2351  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator      name doggo floofer pupper puppo  \n",
       "0                     10   Phineas  None    None   None  None  \n",
       "1                     10     Tilly  None    None   None  None  \n",
       "2                     10    Archie  None    None   None  None  \n",
       "3                     10     Darla  None    None   None  None  \n",
       "4                     10  Franklin  None    None   None  None  \n",
       "...                  ...       ...   ...     ...    ...   ...  \n",
       "2351                  10      None  None    None   None  None  \n",
       "2352                  10         a  None    None   None  None  \n",
       "2353                  10         a  None    None   None  None  \n",
       "2354                  10         a  None    None   None  None  \n",
       "2355                  10      None  None    None   None  None  \n",
       "\n",
       "[2356 rows x 17 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "671b7075",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "8d9e70b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2333\n",
       "11        3\n",
       "50        3\n",
       "20        2\n",
       "80        2\n",
       "70        1\n",
       "7         1\n",
       "15        1\n",
       "150       1\n",
       "170       1\n",
       "0         1\n",
       "90        1\n",
       "40        1\n",
       "130       1\n",
       "110       1\n",
       "16        1\n",
       "120       1\n",
       "2         1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "ca60fe1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <td>Welsh_springer_spaniel</td>\n",
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       "      <td>German_shepherd</td>\n",
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       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>2</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2071</th>\n",
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       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
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       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
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       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
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       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
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       "<p>2075 rows × 12 columns</p>\n",
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      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "0     666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1     666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2     666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3     666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4     666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "...                  ...                                              ...   \n",
       "2070  891327558926688256  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "2071  891689557279858688  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "2072  891815181378084864  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "2073  892177421306343426  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2074  892420643555336193  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0           1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1           1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2           1         German_shepherd  0.596461    True            malinois   \n",
       "3           1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4           1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "...       ...                     ...       ...     ...                 ...   \n",
       "2070        2                  basset  0.555712    True    English_springer   \n",
       "2071        1             paper_towel  0.170278   False  Labrador_retriever   \n",
       "2072        1               Chihuahua  0.716012    True            malamute   \n",
       "2073        1               Chihuahua  0.323581    True            Pekinese   \n",
       "2074        1                  orange  0.097049   False               bagel   \n",
       "\n",
       "       p2_conf  p2_dog                           p3   p3_conf  p3_dog  \n",
       "0     0.156665    True            Shetland_sheepdog  0.061428    True  \n",
       "1     0.074192    True          Rhodesian_ridgeback  0.072010    True  \n",
       "2     0.138584    True                   bloodhound  0.116197    True  \n",
       "3     0.360687    True           miniature_pinscher  0.222752    True  \n",
       "4     0.243682    True                     Doberman  0.154629    True  \n",
       "...        ...     ...                          ...       ...     ...  \n",
       "2070  0.225770    True  German_short-haired_pointer  0.175219    True  \n",
       "2071  0.168086    True                      spatula  0.040836   False  \n",
       "2072  0.078253    True                       kelpie  0.031379    True  \n",
       "2073  0.090647    True                     papillon  0.068957    True  \n",
       "2074  0.085851   False                       banana  0.076110   False  \n",
       "\n",
       "[2075 rows x 12 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ef12e212",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "image.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "23dc0d82",
   "metadata": {},
   "outputs": [
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       "      <td>666029285002620928</td>\n",
       "      <td>48</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>532</td>\n",
       "      <td>2535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2354 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  retweet_count  favorite_count\n",
       "0     892420643555336193           8853           39467\n",
       "1     892177421306343426           6514           33819\n",
       "2     891815181378084864           4328           25461\n",
       "3     891689557279858688           8964           42908\n",
       "4     891327558926688256           9774           41048\n",
       "...                  ...            ...             ...\n",
       "2349  666049248165822465             41             111\n",
       "2350  666044226329800704            147             311\n",
       "2351  666033412701032449             47             128\n",
       "2352  666029285002620928             48             132\n",
       "2353  666020888022790149            532            2535\n",
       "\n",
       "[2354 rows x 3 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "additional_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "e627234d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   tweet_id        2354 non-null   int64\n",
      " 1   retweet_count   2354 non-null   int64\n",
      " 2   favorite_count  2354 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 55.3 KB\n"
     ]
    }
   ],
   "source": [
    "additional_info.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5f37812c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Phineas\n",
       "1          Tilly\n",
       "2         Archie\n",
       "3          Darla\n",
       "4       Franklin\n",
       "          ...   \n",
       "2351        None\n",
       "2352           a\n",
       "2353           a\n",
       "2354           a\n",
       "2355        None\n",
       "Name: name, Length: 2356, dtype: object"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "13cf006e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
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       "      <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>118</th>\n",
       "      <td>869988702071779329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-05-31 18:47:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: We only rate dogs. This is quit...</td>\n",
       "      <td>8.591970e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-05-02 00:04:57 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/859196978...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>quite</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>859196978902773760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-05-02 00:04:57 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>We only rate dogs. This is quite clearly a smo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/859196978...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>quite</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>855459453768019968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-04-21 16:33:22 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Guys, we only rate dogs. This is quite clearly...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/855459453...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>quite</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>666411507551481857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-17 00:24:19 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is quite the dog. Gets really excited whe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666411507...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>quite</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",
       "118   869988702071779329                    NaN                  NaN   \n",
       "169   859196978902773760                    NaN                  NaN   \n",
       "193   855459453768019968                    NaN                  NaN   \n",
       "2326  666411507551481857                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "118   2017-05-31 18:47:24 +0000   \n",
       "169   2017-05-02 00:04:57 +0000   \n",
       "193   2017-04-21 16:33:22 +0000   \n",
       "2326  2015-11-17 00:24:19 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "118   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "169   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "193   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2326  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "118   RT @dog_rates: We only rate dogs. This is quit...         8.591970e+17   \n",
       "169   We only rate dogs. This is quite clearly a smo...                  NaN   \n",
       "193   Guys, we only rate dogs. This is quite clearly...                  NaN   \n",
       "2326  This is quite the dog. Gets really excited whe...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "118               4.196984e+09  2017-05-02 00:04:57 +0000   \n",
       "169                        NaN                        NaN   \n",
       "193                        NaN                        NaN   \n",
       "2326                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "118   https://twitter.com/dog_rates/status/859196978...                12   \n",
       "169   https://twitter.com/dog_rates/status/859196978...                12   \n",
       "193   https://twitter.com/dog_rates/status/855459453...                12   \n",
       "2326  https://twitter.com/dog_rates/status/666411507...                 2   \n",
       "\n",
       "      rating_denominator   name doggo floofer pupper puppo  \n",
       "118                   10  quite  None    None   None  None  \n",
       "169                   10  quite  None    None   None  None  \n",
       "193                   10  quite  None    None   None  None  \n",
       "2326                  10  quite  None    None   None  None  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive[archive.name==\"quite\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "551b9368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "745"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(archive.name==\"None\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "15451e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
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       "      <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>200</th>\n",
       "      <td>854010172552949760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-04-17 16:34:26 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>At first I thought this was a shy doggo, but i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/854010172...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>doggo</td>\n",
       "      <td>floofer</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "200  854010172552949760                    NaN                  NaN   \n",
       "\n",
       "                     timestamp  \\\n",
       "200  2017-04-17 16:34:26 +0000   \n",
       "\n",
       "                                                source  \\\n",
       "200  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                  text  retweeted_status_id  \\\n",
       "200  At first I thought this was a shy doggo, but i...                  NaN   \n",
       "\n",
       "     retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "200                       NaN                        NaN   \n",
       "\n",
       "                                         expanded_urls  rating_numerator  \\\n",
       "200  https://twitter.com/dog_rates/status/854010172...                11   \n",
       "\n",
       "     rating_denominator  name  doggo  floofer pupper puppo  \n",
       "200                  10  None  doggo  floofer   None  None  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive[((archive['doggo']!=\"None\") & (archive['floofer']!=\"None\"))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "69c6d088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  13,   12,   14,    5,   17,   11,   10,  420,  666,    6,   15,\n",
       "        182,  960,    0,   75,    7,   84,    9,   24,    8,    1,   27,\n",
       "          3,    4,  165, 1776,  204,   50,   99,   80,   45,   60,   44,\n",
       "        143,  121,   20,   26,    2,  144,   88], dtype=int64)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive.rating_numerator.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23dea0a0",
   "metadata": {},
   "source": [
    "### Quality issues\n",
    "<a id=\"quality\"></a>\n",
    "##### `archive` table\n",
    "\n",
    "1. The timestamp in the twitter archive dataframe is not in time format\n",
    "\n",
    "2. Some dogs names are erronous (*\"and\", \"a\", \"quite\"* etc...)\n",
    "\n",
    "3. There is no need for tweets that are beyond August 1st, 2017.\n",
    "\n",
    "4. There are 181 retweeted tweets in the twitter archive dataset. They are not original.\n",
    "\n",
    "5. Some ratings have a denominator lower than 10 (0, 2 and 7).\n",
    "\n",
    "6. Some ratings are erronous.\n",
    "\n",
    "##### `additional_info` table\n",
    "\n",
    "7. There are 2354 tweets in the Additional Data via the Twitter API dataframe. More rows than twitter archive table.\n",
    "\n",
    "##### `image` table\n",
    "\n",
    "8. There are missing images for some tweets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "59c2e0f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
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       "      <th>retweeted_status_timestamp</th>\n",
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       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "523e8915",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "6094c0df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 0        Phineas\n",
       "1          Tilly\n",
       "2         Archie\n",
       "3          Darla\n",
       "4       Franklin\n",
       "          ...   \n",
       "2351        None\n",
       "2352           a\n",
       "2353           a\n",
       "2354           a\n",
       "2355        None\n",
       "Name: name, Length: 2356, dtype: object>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive.name.unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "f9bf6945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2333\n",
       "11        3\n",
       "50        3\n",
       "20        2\n",
       "80        2\n",
       "70        1\n",
       "7         1\n",
       "15        1\n",
       "150       1\n",
       "170       1\n",
       "0         1\n",
       "90        1\n",
       "40        1\n",
       "130       1\n",
       "110       1\n",
       "16        1\n",
       "120       1\n",
       "2         1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f429bd8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "image.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "0717f35a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2070</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "2070  891327558926688256  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "2071  891689557279858688  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "2072  891815181378084864  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "2073  892177421306343426  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2074  892420643555336193  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num           p1   p1_conf  p1_dog                  p2   p2_conf  \\\n",
       "2070        2       basset  0.555712    True    English_springer  0.225770   \n",
       "2071        1  paper_towel  0.170278   False  Labrador_retriever  0.168086   \n",
       "2072        1    Chihuahua  0.716012    True            malamute  0.078253   \n",
       "2073        1    Chihuahua  0.323581    True            Pekinese  0.090647   \n",
       "2074        1       orange  0.097049   False               bagel  0.085851   \n",
       "\n",
       "      p2_dog                           p3   p3_conf  p3_dog  \n",
       "2070    True  German_short-haired_pointer  0.175219    True  \n",
       "2071    True                      spatula  0.040836   False  \n",
       "2072    True                       kelpie  0.031379    True  \n",
       "2073    True                     papillon  0.068957    True  \n",
       "2074   False                       banana  0.076110   False  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "5e195f37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   tweet_id        2354 non-null   int64\n",
      " 1   retweet_count   2354 non-null   int64\n",
      " 2   favorite_count  2354 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 55.3 KB\n"
     ]
    }
   ],
   "source": [
    "additional_info.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df8efbf9",
   "metadata": {},
   "source": [
    "### Tidiness issues\n",
    "<a id=\"tidiness\"></a>\n",
    "1.  the dog stage is one variable and hence should form single column. But this variable is spread across 4 columns - `doggo`, `floofer`, `pupper`, `puppo`.\n",
    "2. Information about one type of observational unit (tweets) is spread across three different files/dataframes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a165e1",
   "metadata": {},
   "source": [
    "## Cleaning Data\n",
    "<a id=\"clean\"></a>\n",
    "In this section, clean **all** of the issues you documented while assessing. \n",
    "\n",
    "**Note:** Make a copy of the original data before cleaning. Cleaning includes merging individual pieces of data according to the rules of [tidy data](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html). The result should be a high-quality and tidy master pandas DataFrame (or DataFrames, if appropriate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "bfc3f22c",
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean = archive.copy()\n",
    "image_clean = image.copy()\n",
    "additional_info_clean = additional_info.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "975c6a3a",
   "metadata": {},
   "source": [
    "### Tidiness"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "770416e3",
   "metadata": {},
   "source": [
    "## Issue #1: \n",
    "#### The dog stage is one variable and hence should form single column. But this variable is spread across 4 columns - `doggo`, `floofer`, `pupper`, `puppo`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "616c68e7",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Combine the four dog stage columns into one single column. Format entries with multiple dog stages. Drop the four dog stages columns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e98dc6ea",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "d9b1833e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First replace None in stage columns with empty string as follows.\n",
    "archive_clean.doggo.replace('None', '', inplace=True)\n",
    "archive_clean.floofer.replace('None', '', inplace=True)\n",
    "archive_clean.pupper.replace('None', '', inplace=True)\n",
    "archive_clean.puppo.replace('None', '', inplace=True)\n",
    "# Then combine stage columns.\n",
    "archive_clean['stage'] = archive_clean.doggo + archive_clean.floofer + archive_clean.pupper + archive_clean.puppo\n",
    "\n",
    "# Then format entries with multiple dog stages which appeared like doggopupper.\n",
    "\n",
    "archive_clean.loc[archive_clean.stage == 'doggopupper', 'stage'] = 'doggo, pupper' \n",
    "archive_clean.loc[archive_clean.stage == 'doggopuppo', 'stage'] = 'doggo, puppo' \n",
    "archive_clean.loc[archive_clean.stage == 'doggofloofer', 'stage'] = 'doggo, floofer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "a5fd375c",
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean.drop(['doggo','floofer','puppo','pupper'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "896b15df",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Merge the `archive` table and the `additionnal_info` table on the *tweet_id* column using the merge function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c47ea78",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "cc391398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['', 'doggo', 'puppo', 'pupper', 'floofer', 'doggo, puppo',\n",
       "       'doggo, floofer', 'doggo, pupper'], dtype=object)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.stage.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "41d02d29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>706346369204748288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-06 05:11:12 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Koda. She's a Beneboom Cumberwiggle. 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/706346369...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Koda</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "1320  706346369204748288                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "1320  2016-03-06 05:11:12 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "1320  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "1320  This is Koda. She's a Beneboom Cumberwiggle. 1...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "1320                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "1320  https://twitter.com/dog_rates/status/706346369...                12   \n",
       "\n",
       "      rating_denominator  name stage  \n",
       "1320                  10  Koda        "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3392877",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d1edc42a",
   "metadata": {},
   "source": [
    "## Issue #2: \n",
    "#### Information about one type of observational unit (tweets) is spread across three different files/dataframes.\n",
    "\n",
    "#### There are 2354 tweets in the `additional_info` dataframe. More rows than twitter `archive` table."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9b5357e",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Merge the `archive` table, the `additionnal_info` and the `image_clean` table on the *tweet_id* column using the merge function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9cfea63",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f61b156e",
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean = pd.merge(pd.merge(archive_clean,additional_info_clean,on='tweet_id'),image_clean, on=\"tweet_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "422bb0a8",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "114d18d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2073 entries, 0 to 2072\n",
      "Data columns (total 27 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2073 non-null   int64  \n",
      " 1   in_reply_to_status_id       23 non-null     float64\n",
      " 2   in_reply_to_user_id         23 non-null     float64\n",
      " 3   timestamp                   2073 non-null   object \n",
      " 4   source                      2073 non-null   object \n",
      " 5   text                        2073 non-null   object \n",
      " 6   retweeted_status_id         79 non-null     float64\n",
      " 7   retweeted_status_user_id    79 non-null     float64\n",
      " 8   retweeted_status_timestamp  79 non-null     object \n",
      " 9   expanded_urls               2073 non-null   object \n",
      " 10  rating_numerator            2073 non-null   int64  \n",
      " 11  rating_denominator          2073 non-null   int64  \n",
      " 12  name                        2073 non-null   object \n",
      " 13  stage                       2073 non-null   object \n",
      " 14  retweet_count               2073 non-null   int64  \n",
      " 15  favorite_count              2073 non-null   int64  \n",
      " 16  jpg_url                     2073 non-null   object \n",
      " 17  img_num                     2073 non-null   int64  \n",
      " 18  p1                          2073 non-null   object \n",
      " 19  p1_conf                     2073 non-null   float64\n",
      " 20  p1_dog                      2073 non-null   bool   \n",
      " 21  p2                          2073 non-null   object \n",
      " 22  p2_conf                     2073 non-null   float64\n",
      " 23  p2_dog                      2073 non-null   bool   \n",
      " 24  p3                          2073 non-null   object \n",
      " 25  p3_conf                     2073 non-null   float64\n",
      " 26  p3_dog                      2073 non-null   bool   \n",
      "dtypes: bool(3), float64(7), int64(6), object(11)\n",
      "memory usage: 411.0+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88e79cc3",
   "metadata": {},
   "source": [
    "### Quality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "691088ed",
   "metadata": {},
   "source": [
    "## Issue #1: \n",
    "#### The timestamp in the twitter archive dataframe is not in datetime format."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "213ef591",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Convert the *timestamp* to datetime format using `to_datetime`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a3c02b2",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "7c111427",
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean.timestamp=pd.to_datetime(archive.timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47018f73",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "8d1c1d79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2073 entries, 0 to 2072\n",
      "Data columns (total 27 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2073 non-null   int64              \n",
      " 1   in_reply_to_status_id       23 non-null     float64            \n",
      " 2   in_reply_to_user_id         23 non-null     float64            \n",
      " 3   timestamp                   2073 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2073 non-null   object             \n",
      " 5   text                        2073 non-null   object             \n",
      " 6   retweeted_status_id         79 non-null     float64            \n",
      " 7   retweeted_status_user_id    79 non-null     float64            \n",
      " 8   retweeted_status_timestamp  79 non-null     object             \n",
      " 9   expanded_urls               2073 non-null   object             \n",
      " 10  rating_numerator            2073 non-null   int64              \n",
      " 11  rating_denominator          2073 non-null   int64              \n",
      " 12  name                        2073 non-null   object             \n",
      " 13  stage                       2073 non-null   object             \n",
      " 14  retweet_count               2073 non-null   int64              \n",
      " 15  favorite_count              2073 non-null   int64              \n",
      " 16  jpg_url                     2073 non-null   object             \n",
      " 17  img_num                     2073 non-null   int64              \n",
      " 18  p1                          2073 non-null   object             \n",
      " 19  p1_conf                     2073 non-null   float64            \n",
      " 20  p1_dog                      2073 non-null   bool               \n",
      " 21  p2                          2073 non-null   object             \n",
      " 22  p2_conf                     2073 non-null   float64            \n",
      " 23  p2_dog                      2073 non-null   bool               \n",
      " 24  p3                          2073 non-null   object             \n",
      " 25  p3_conf                     2073 non-null   float64            \n",
      " 26  p3_dog                      2073 non-null   bool               \n",
      "dtypes: bool(3), datetime64[ns, UTC](1), float64(7), int64(6), object(10)\n",
      "memory usage: 411.0+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c9eaae5",
   "metadata": {},
   "source": [
    "## Issue #2:\n",
    "#### Erronous dog *names* such as *quite\", \"an\", \"a\", \"such\" etc..*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a9a6244",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Replace the rows with lowercase names to the value *None* using regular expression."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e619092",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "d41ae251",
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean['name'].replace(r'^[a-z]+$', \"None\",inplace=True,regex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "798c356c",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "7b801ae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Phineas\n",
       "1          Tilly\n",
       "2         Archie\n",
       "3          Darla\n",
       "4       Franklin\n",
       "          ...   \n",
       "2068        None\n",
       "2069        None\n",
       "2070        None\n",
       "2071        None\n",
       "2072        None\n",
       "Name: name, Length: 2073, dtype: object"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e756465",
   "metadata": {},
   "source": [
    "## Issue #3:\n",
    "#### Some ratings have a denominator lower than 10 (0, 2 and 7)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8864786f",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Drop rows with rating denominators lower than 10. Drop the *denominator* column when finished."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd3d7ab",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "dcc3dc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean=archive_clean[archive_clean.rating_denominator>=10]\n",
    "archive_clean.drop(['rating_denominator'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7442bc5c",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "84cce2cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tweet_id',\n",
       " 'in_reply_to_status_id',\n",
       " 'in_reply_to_user_id',\n",
       " 'timestamp',\n",
       " 'source',\n",
       " 'text',\n",
       " 'retweeted_status_id',\n",
       " 'retweeted_status_user_id',\n",
       " 'retweeted_status_timestamp',\n",
       " 'expanded_urls',\n",
       " 'rating_numerator',\n",
       " 'name',\n",
       " 'stage',\n",
       " 'retweet_count',\n",
       " 'favorite_count',\n",
       " 'jpg_url',\n",
       " 'img_num',\n",
       " 'p1',\n",
       " 'p1_conf',\n",
       " 'p1_dog',\n",
       " 'p2',\n",
       " 'p2_conf',\n",
       " 'p2_dog',\n",
       " 'p3',\n",
       " 'p3_conf',\n",
       " 'p3_dog']"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(archive_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69596039",
   "metadata": {},
   "source": [
    "## Issue #4:\n",
    "#### There are 181 retweeted tweets in the twitter archive dataset. They are not original."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32912b15",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Drop the rows corresponding to retweets. Drop the *retweeted_id*, *retweeted_status_user_id*, *retweeted_status_timestamp* columns when finished."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94635a0a",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "376476bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean = archive_clean[archive_clean.retweeted_status_id.isnull()]\n",
    "archive_clean = archive_clean[archive_clean.retweeted_status_user_id.isnull()]\n",
    "archive_clean = archive_clean[archive_clean.retweeted_status_timestamp.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3b07b89",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "e7e274e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1992 entries, 0 to 2072\n",
      "Data columns (total 26 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    1992 non-null   int64              \n",
      " 1   in_reply_to_status_id       23 non-null     float64            \n",
      " 2   in_reply_to_user_id         23 non-null     float64            \n",
      " 3   timestamp                   1992 non-null   datetime64[ns, UTC]\n",
      " 4   source                      1992 non-null   object             \n",
      " 5   text                        1992 non-null   object             \n",
      " 6   retweeted_status_id         0 non-null      float64            \n",
      " 7   retweeted_status_user_id    0 non-null      float64            \n",
      " 8   retweeted_status_timestamp  0 non-null      object             \n",
      " 9   expanded_urls               1992 non-null   object             \n",
      " 10  rating_numerator            1992 non-null   int64              \n",
      " 11  name                        1992 non-null   object             \n",
      " 12  stage                       1992 non-null   object             \n",
      " 13  retweet_count               1992 non-null   int64              \n",
      " 14  favorite_count              1992 non-null   int64              \n",
      " 15  jpg_url                     1992 non-null   object             \n",
      " 16  img_num                     1992 non-null   int64              \n",
      " 17  p1                          1992 non-null   object             \n",
      " 18  p1_conf                     1992 non-null   float64            \n",
      " 19  p1_dog                      1992 non-null   bool               \n",
      " 20  p2                          1992 non-null   object             \n",
      " 21  p2_conf                     1992 non-null   float64            \n",
      " 22  p2_dog                      1992 non-null   bool               \n",
      " 23  p3                          1992 non-null   object             \n",
      " 24  p3_conf                     1992 non-null   float64            \n",
      " 25  p3_dog                      1992 non-null   bool               \n",
      "dtypes: bool(3), datetime64[ns, UTC](1), float64(7), int64(5), object(10)\n",
      "memory usage: 379.3+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b0bcabe",
   "metadata": {},
   "source": [
    "## Issue #5:\n",
    "\n",
    "#### Some ratings are erronous."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ac7f4d",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Extract correct ratings from tweets texts using `str.extract()` function and **regular expressions**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "62b95251",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracting the correct ratings\n",
    "rating = archive_clean.text.str.extract('((?:\\d+\\.)?\\d+)\\/(\\d+)', expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "cc63422e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the new rating values in the rating_numerator column\n",
    "archive_clean.rating_numerator=rating.iloc[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d146e175",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "dd72869e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       13\n",
       "1       13\n",
       "2       12\n",
       "3       13\n",
       "4       12\n",
       "        ..\n",
       "2068     5\n",
       "2069     6\n",
       "2070     9\n",
       "2071     7\n",
       "2072     8\n",
       "Name: rating_numerator, Length: 1992, dtype: object"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.rating_numerator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b40790",
   "metadata": {},
   "source": [
    "## Issue #6:\n",
    "#### There is no need for tweets that are beyond August 1st, 2017."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1781f004",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Drop rows in *archive* table that are beyond August 1st, 2017 using boolean indexing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c4cf69",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "bd46c399",
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean= archive_clean[archive_clean.timestamp<'2017-08-01']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8143cb31",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "c956aa78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2      2017-07-31 00:18:03+00:00\n",
       "3      2017-07-30 15:58:51+00:00\n",
       "4      2017-07-29 16:00:24+00:00\n",
       "5      2017-07-29 00:08:17+00:00\n",
       "6      2017-07-28 16:27:12+00:00\n",
       "                  ...           \n",
       "2068   2015-11-30 01:28:28+00:00\n",
       "2069   2015-11-30 01:10:04+00:00\n",
       "2070   2015-11-30 00:22:57+00:00\n",
       "2071   2015-11-29 23:57:10+00:00\n",
       "2072   2015-11-29 23:30:32+00:00\n",
       "Name: timestamp, Length: 1990, dtype: datetime64[ns, UTC]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "14dffc93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1990 entries, 2 to 2072\n",
      "Data columns (total 26 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    1990 non-null   int64              \n",
      " 1   in_reply_to_status_id       23 non-null     float64            \n",
      " 2   in_reply_to_user_id         23 non-null     float64            \n",
      " 3   timestamp                   1990 non-null   datetime64[ns, UTC]\n",
      " 4   source                      1990 non-null   object             \n",
      " 5   text                        1990 non-null   object             \n",
      " 6   retweeted_status_id         0 non-null      float64            \n",
      " 7   retweeted_status_user_id    0 non-null      float64            \n",
      " 8   retweeted_status_timestamp  0 non-null      object             \n",
      " 9   expanded_urls               1990 non-null   object             \n",
      " 10  rating_numerator            1990 non-null   object             \n",
      " 11  name                        1990 non-null   object             \n",
      " 12  stage                       1990 non-null   object             \n",
      " 13  retweet_count               1990 non-null   int64              \n",
      " 14  favorite_count              1990 non-null   int64              \n",
      " 15  jpg_url                     1990 non-null   object             \n",
      " 16  img_num                     1990 non-null   int64              \n",
      " 17  p1                          1990 non-null   object             \n",
      " 18  p1_conf                     1990 non-null   float64            \n",
      " 19  p1_dog                      1990 non-null   bool               \n",
      " 20  p2                          1990 non-null   object             \n",
      " 21  p2_conf                     1990 non-null   float64            \n",
      " 22  p2_dog                      1990 non-null   bool               \n",
      " 23  p3                          1990 non-null   object             \n",
      " 24  p3_conf                     1990 non-null   float64            \n",
      " 25  p3_dog                      1990 non-null   bool               \n",
      "dtypes: bool(3), datetime64[ns, UTC](1), float64(7), int64(4), object(11)\n",
      "memory usage: 379.0+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff3c94c4",
   "metadata": {},
   "source": [
    "## Issue #7:\n",
    "#### There are missing images for some tweets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e10bbd",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "Drop rows in `archive` table that don't have corresponding images in `image` table using `isin`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b243e7",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "5f4d4345",
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean = archive_clean[archive_clean['tweet_id'].isin(image_clean['tweet_id'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3943979",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "cffa0047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, name, stage, retweet_count, favorite_count, jpg_url, img_num, p1, p1_conf, p1_dog, p2, p2_conf, p2_dog, p3, p3_conf, p3_dog]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 26 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean[~archive_clean['tweet_id'].isin(image_clean['tweet_id'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c6263f2",
   "metadata": {},
   "source": [
    "## Storing Data\n",
    "<a id=\"storing\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78befff6",
   "metadata": {},
   "source": [
    "### Store the twitter archive table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "2fd82a88",
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_clean.to_csv('twitter_archive_master.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66158f92",
   "metadata": {},
   "source": [
    "## Analyzing and Visualizing Data\n",
    "<a id=\"analyzing\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "377c270d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>822872901745569793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-02-20 00:53:27+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here's a super supportive puppo participating ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/822872901...</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Lakeland_terrier</td>\n",
       "      <td>0.196015</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.160329</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.069126</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "329  822872901745569793                    NaN                  NaN   \n",
       "\n",
       "                    timestamp  \\\n",
       "329 2017-02-20 00:53:27+00:00   \n",
       "\n",
       "                                                source  \\\n",
       "329  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                  text  retweeted_status_id  \\\n",
       "329  Here's a super supportive puppo participating ...                  NaN   \n",
       "\n",
       "     retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "329                       NaN                        NaN   \n",
       "\n",
       "                                         expanded_urls  ... img_num  \\\n",
       "329  https://twitter.com/dog_rates/status/822872901...  ...       1   \n",
       "\n",
       "                   p1   p1_conf  p1_dog                  p2   p2_conf  p2_dog  \\\n",
       "329  Lakeland_terrier  0.196015    True  Labrador_retriever  0.160329    True   \n",
       "\n",
       "                p3   p3_conf  p3_dog  \n",
       "329  Irish_terrier  0.069126    True  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# searching for the heighest recorded favorite count\n",
    "archive_clean[archive_clean.favorite_count==archive_clean.favorite_count.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "92242220",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stage\n",
       "                  14233060\n",
       "doggo              1219452\n",
       "doggo, floofer       17169\n",
       "doggo, pupper       112798\n",
       "doggo, puppo         47844\n",
       "floofer              92442\n",
       "pupper             1461141\n",
       "puppo               474806\n",
       "Name: favorite_count, dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating the highest accumulative favorite count for each dog stage\n",
    "archive_clean.groupby('stage')['favorite_count'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "4a61854c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p1_dog\n",
       "False    0.248675\n",
       "True     0.710361\n",
       "Name: p1_conf, dtype: float64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating the percentage of the accuracy of the algorithm\n",
    "archive_clean.groupby('p1_dog')['p1_conf'].size()/image_clean.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "5dfb56e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stage\n",
       "                  4334499\n",
       "doggo              448919\n",
       "doggo, floofer       3433\n",
       "doggo, pupper       36751\n",
       "doggo, puppo        19196\n",
       "floofer             34781\n",
       "pupper             479807\n",
       "puppo              142427\n",
       "Name: retweet_count, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_clean.groupby('stage')['retweet_count'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bcb311e",
   "metadata": {},
   "source": [
    "### Insights:\n",
    "1. The dog stage with the highest accumulative favorite count is pupper\n",
    "2. the highest recorded favorite count for a dog is 132810\n",
    "3. The dog stage with the most retweets is doggo\n",
    "4. 26% of the algorithm's #1 predictions are not dog breeds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf301140",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "bfc900f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# plotting the favorite count of the dog stages\n",
    "favorite_y = archive_clean.groupby('stage')['favorite_count'].mean()\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.bar(x=['None','doggo','doggo, floofer', 'doggo, pupper', 'doggo, puppo',\n",
    "           'floofer','pupper','puppo'],height=favorite_y)\n",
    "plt.title('Average favorite count of dog stages')\n",
    "plt.xlabel('dog stages')\n",
    "plt.ylabel('average favorite count')\n",
    "plt.savefig('fig.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}