wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "indian-eleven",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import tweepy\n",
    "import json\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "from io import BytesIO\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "operating-technology",
   "metadata": {},
   "source": [
    "# Gather\n",
    "\n",
    "   * Load `twitter_archive_enhanced.csv` from manually downloaded files\n",
    "   * Download the tweet image predictions .tsv file programmatically from this url `https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv`\n",
    "   * Query Tweepy for any intersting data using the tweet IDs we have and store them in `tweet_json.txt`, each tweet data need its own line, then read the `tweet_json.txt` into pandas DF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "worst-british",
   "metadata": {},
   "source": [
    "## Code\n",
    "\n",
    "* Load `twitter_archive_enhanced.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "induced-instruction",
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive = pd.read_csv(\"twitter-archive-enhanced.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "resident-estimate",
   "metadata": {},
   "source": [
    "* Download the tweet image predictions .tsv file programmatically from this url `https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "knowing-viking",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"\"\"https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv\"\"\"\n",
    "with open(\"image-predictions.tsv\", 'wb') as f:\n",
    "    f.write(requests.get(url).content)\n",
    "image_predictions = pd.read_csv(\"image-predictions.tsv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effective-denial",
   "metadata": {},
   "source": [
    "* Query Tweepy for any intersting data using the tweet IDs we have and store them in `tweet_json.txt`, each tweet data need its own line, then read the `tweet_json.txt` into pandas DF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "composed-customs",
   "metadata": {},
   "source": [
    "# Query Twitter API for each tweet in the Twitter archive and save JSON in a text file\n",
    "# These are hidden to comply with Twitter's API terms and conditions\n",
    "consumer_key = 'HIDDEN'\n",
    "consumer_secret = 'HIDDEN'\n",
    "access_token = 'HIDDEN'\n",
    "access_secret = 'HIDDEN'\n",
    "\n",
    "auth = 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",
    "# df_1 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 = df_1.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": "code",
   "execution_count": 4,
   "id": "stainless-providence",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading data from tweet-json.txt\n",
    "head = ['id', 'retweet_count', 'favorite_count', 'created_at']\n",
    "tweet_API_data = pd.read_json('tweet.json', lines=True)\n",
    "for col in list(tweet_API_data.columns):\n",
    "    if col in head:\n",
    "        continue\n",
    "    else:\n",
    "        tweet_API_data.drop(col, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attached-needle",
   "metadata": {},
   "source": [
    "# Assess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "wanted-livestock",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <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",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <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",
       "      <td>NaN</td>\n",
       "      <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",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2356 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0     892420643555336193                    NaN                  NaN   \n",
       "1     892177421306343426                    NaN                  NaN   \n",
       "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "monetary-investment",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.168086</td>\n",
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       "    <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",
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       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.097049</td>\n",
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       "      <td>bagel</td>\n",
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       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "purple-consumer",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>532</td>\n",
       "      <td>2535</td>\n",
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       "<p>2354 rows × 4 columns</p>\n",
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      ],
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       "                    created_at                  id  retweet_count  \\\n",
       "0    2017-08-01 16:23:56+00:00  892420643555336193           8853   \n",
       "1    2017-08-01 00:17:27+00:00  892177421306343426           6514   \n",
       "2    2017-07-31 00:18:03+00:00  891815181378084864           4328   \n",
       "3    2017-07-30 15:58:51+00:00  891689557279858688           8964   \n",
       "4    2017-07-29 16:00:24+00:00  891327558926688256           9774   \n",
       "...                        ...                 ...            ...   \n",
       "2349 2015-11-16 00:24:50+00:00  666049248165822465             41   \n",
       "2350 2015-11-16 00:04:52+00:00  666044226329800704            147   \n",
       "2351 2015-11-15 23:21:54+00:00  666033412701032449             47   \n",
       "2352 2015-11-15 23:05:30+00:00  666029285002620928             48   \n",
       "2353 2015-11-15 22:32:08+00:00  666020888022790149            532   \n",
       "\n",
       "      favorite_count  \n",
       "0              39467  \n",
       "1              33819  \n",
       "2              25461  \n",
       "3              42908  \n",
       "4              41048  \n",
       "...              ...  \n",
       "2349             111  \n",
       "2350             311  \n",
       "2351             128  \n",
       "2352             132  \n",
       "2353            2535  \n",
       "\n",
       "[2354 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_API_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "auburn-reality",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "infectious-maldives",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "employed-uniform",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                         0\n",
       "in_reply_to_status_id         2278\n",
       "in_reply_to_user_id           2278\n",
       "timestamp                        0\n",
       "source                           0\n",
       "text                             0\n",
       "retweeted_status_id           2175\n",
       "retweeted_status_user_id      2175\n",
       "retweeted_status_timestamp    2175\n",
       "expanded_urls                   59\n",
       "rating_numerator                 0\n",
       "rating_denominator               0\n",
       "name                             0\n",
       "doggo                            0\n",
       "floofer                          0\n",
       "pupper                           0\n",
       "puppo                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "returning-spyware",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       745\n",
       "a           55\n",
       "Charlie     12\n",
       "Oliver      11\n",
       "Cooper      11\n",
       "          ... \n",
       "Remy         1\n",
       "Lucky        1\n",
       "Brutus       1\n",
       "Charl        1\n",
       "Jaspers      1\n",
       "Name: name, Length: 957, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "brief-participation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "image_predictions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "complicated-warren",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "infectious-florida",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id    0\n",
       "jpg_url     0\n",
       "img_num     0\n",
       "p1          0\n",
       "p1_conf     0\n",
       "p1_dog      0\n",
       "p2          0\n",
       "p2_conf     0\n",
       "p2_dog      0\n",
       "p3          0\n",
       "p3_conf     0\n",
       "p3_dog      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "useful-ethnic",
   "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>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.384514e+17</td>\n",
       "      <td>1.203855</td>\n",
       "      <td>0.594548</td>\n",
       "      <td>1.345886e-01</td>\n",
       "      <td>6.032417e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.785203e+16</td>\n",
       "      <td>0.561875</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>1.006657e-01</td>\n",
       "      <td>5.090593e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.764835e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364412</td>\n",
       "      <td>5.388625e-02</td>\n",
       "      <td>1.622240e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.119988e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588230</td>\n",
       "      <td>1.181810e-01</td>\n",
       "      <td>4.944380e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.932034e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843855</td>\n",
       "      <td>1.955655e-01</td>\n",
       "      <td>9.180755e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id      img_num      p1_conf       p2_conf       p3_conf\n",
       "count  2.075000e+03  2075.000000  2075.000000  2.075000e+03  2.075000e+03\n",
       "mean   7.384514e+17     1.203855     0.594548  1.345886e-01  6.032417e-02\n",
       "std    6.785203e+16     0.561875     0.271174  1.006657e-01  5.090593e-02\n",
       "min    6.660209e+17     1.000000     0.044333  1.011300e-08  1.740170e-10\n",
       "25%    6.764835e+17     1.000000     0.364412  5.388625e-02  1.622240e-02\n",
       "50%    7.119988e+17     1.000000     0.588230  1.181810e-01  4.944380e-02\n",
       "75%    7.932034e+17     1.000000     0.843855  1.955655e-01  9.180755e-02\n",
       "max    8.924206e+17     4.000000     1.000000  4.880140e-01  2.734190e-01"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "authorized-edward",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype              \n",
      "---  ------          --------------  -----              \n",
      " 0   created_at      2354 non-null   datetime64[ns, UTC]\n",
      " 1   id              2354 non-null   int64              \n",
      " 2   retweet_count   2354 non-null   int64              \n",
      " 3   favorite_count  2354 non-null   int64              \n",
      "dtypes: datetime64[ns, UTC](1), int64(3)\n",
      "memory usage: 73.7 KB\n"
     ]
    }
   ],
   "source": [
    "tweet_API_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "laughing-confidence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_API_data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "prerequisite-thompson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "created_at        0\n",
       "id                0\n",
       "retweet_count     0\n",
       "favorite_count    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_API_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "invisible-relay",
   "metadata": {},
   "source": [
    "# Clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dependent-oregon",
   "metadata": {},
   "source": [
    "## Quality Problems to fix\n",
    "\n",
    "#### twitter_archive Problems\n",
    "\n",
    "   * drop retweets\n",
    "   * drop tweets with no expanded URLs\n",
    "   * drop retweet related columns\n",
    "   * Fix `source` column\n",
    "   * Add URL column\n",
    "   * Fix Data Types\n",
    "       * `in_reply_to_status_id` from float to string\n",
    "       * `in_reply_to_user_id` form flaot to string\n",
    "       * `timestamp` from string to datetime\n",
    "   * convert \"None\" string to NaN values\n",
    "   * Fix `name` column\n",
    "    \n",
    "#### image_predictions Problems\n",
    "\n",
    "   * Title each prediction & replace the `\"_\"` with `\" \"`\n",
    "   \n",
    "#### General problems\n",
    "   * Fix ID data type in order to be able to merge properly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "serial-watts",
   "metadata": {},
   "source": [
    "## Tideness Problems to fix\n",
    "\n",
    "#### `twitter_archive` Problems\n",
    "\n",
    "   * switch the columns `['doggo', 'floofer', 'pupper', 'puppo']` to `['dog_stage']` only\n",
    "   \n",
    " \n",
    "* Merge all 3 data frames where they all have the `'tweet_id'` column\n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "capital-panel",
   "metadata": {},
   "source": [
    "## Code\n",
    "\n",
    "### Quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "modified-survival",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating copies\n",
    "tweet_API_data_clean = tweet_API_data.copy()\n",
    "image_predictions_clean = image_predictions.copy()\n",
    "twitter_archive_clean = twitter_archive.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "falling-chassis",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Droping retweets\n",
    "twitter_archive_clean = twitter_archive_clean[twitter_archive_clean.retweeted_status_id.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "purple-rachel",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Droping tweets with no expanded URLs\n",
    "twitter_archive_clean = twitter_archive_clean[twitter_archive_clean.expanded_urls.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "defensive-report",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Droping ['retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp']\n",
    "to_drop = ['retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp']\n",
    "twitter_archive_clean.drop(to_drop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "accepting-chocolate",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fix 'source' column\n",
    "twitter_archive_clean.source = twitter_archive_clean.source.str.replace('<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>', 'Twitter for iPhone')\n",
    "twitter_archive_clean.source = twitter_archive_clean.source.str.replace('<a href=\"http://vine.co\" rel=\"nofollow\">Vine - Make a Scene</a>','Vine - Make a Scene')\n",
    "twitter_archive_clean.source = twitter_archive_clean.source.str.replace('<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>','Twitter Web Client')\n",
    "twitter_archive_clean.source = twitter_archive_clean.source.str.replace('<a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>','TweetDeck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "anticipated-humanity",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding 'URL' column using 'tweet_id' column\n",
    "# FORMAT --> https://twitter.com/dog_rates/status/889531135344209921\n",
    "base_URL = \"https://twitter.com/dog_rates/status/\"\n",
    "twitter_archive_clean['URL'] = base_URL + twitter_archive_clean.tweet_id.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "liquid-movie",
   "metadata": {},
   "source": [
    "##### Fix data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "social-afternoon",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'in_reply_to_status_id' from float to int\n",
    "#'in_reply_to_user_id' form flaot to int\n",
    "#'timestamp' from string to datetime\n",
    "\n",
    "twitter_archive_clean.in_reply_to_status_id = twitter_archive_clean.in_reply_to_status_id.astype(str)\n",
    "twitter_archive_clean.in_reply_to_user_id = twitter_archive_clean.in_reply_to_user_id.astype(str)\n",
    "twitter_archive_clean.timestamp = pd.to_datetime(twitter_archive_clean.timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stock-operations",
   "metadata": {},
   "source": [
    "##### Fix nan STR values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "nonprofit-wallet",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert \"None\" string values in ['doggo', 'floofer', 'pupper', 'puppo'] to nan\n",
    "twitter_archive_clean.replace('None', np.NaN, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "equivalent-directory",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fix 'name' column\n",
    "names_to_change = ['None', 'a', 'not', 'officially', 'old', 'one', 'quite', 'space', 'such', 'the', 'this', 'unacceptable', 'very'\n",
    "                  'mad', 'just', 'his', 'by', 'an', 'all', 'actually', 'O']\n",
    "for name in names_to_change:\n",
    "    twitter_archive_clean.name.replace(name, np.NaN, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "immediate-acrylic",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing '_' with ' ' and fixing capitalization in image_predictions\n",
    "columns = ['p1', 'p2', 'p3']\n",
    "for column in columns:\n",
    "    image_predictions_clean[column] = image_predictions_clean[column].str.replace('_', ' ')\n",
    "    image_predictions_clean[column] = image_predictions_clean[column].str.title()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cross-boxing",
   "metadata": {},
   "source": [
    "## Test\n",
    "\n",
    "### Quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "informal-throw",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2117 entries, 0 to 2355\n",
      "Data columns (total 15 columns):\n",
      " #   Column                 Non-Null Count  Dtype              \n",
      "---  ------                 --------------  -----              \n",
      " 0   tweet_id               2117 non-null   int64              \n",
      " 1   in_reply_to_status_id  2117 non-null   object             \n",
      " 2   in_reply_to_user_id    2117 non-null   object             \n",
      " 3   timestamp              2117 non-null   datetime64[ns, UTC]\n",
      " 4   source                 2117 non-null   object             \n",
      " 5   text                   2117 non-null   object             \n",
      " 6   expanded_urls          2117 non-null   object             \n",
      " 7   rating_numerator       2117 non-null   int64              \n",
      " 8   rating_denominator     2117 non-null   int64              \n",
      " 9   name                   1402 non-null   object             \n",
      " 10  doggo                  84 non-null     object             \n",
      " 11  floofer                10 non-null     object             \n",
      " 12  pupper                 232 non-null    object             \n",
      " 13  puppo                  24 non-null     object             \n",
      " 14  URL                    2117 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), int64(3), object(11)\n",
      "memory usage: 264.6+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_archive_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "played-pension",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                    0\n",
       "in_reply_to_status_id       0\n",
       "in_reply_to_user_id         0\n",
       "timestamp                   0\n",
       "source                      0\n",
       "text                        0\n",
       "expanded_urls               0\n",
       "rating_numerator            0\n",
       "rating_denominator          0\n",
       "name                      715\n",
       "doggo                    2033\n",
       "floofer                  2107\n",
       "pupper                   1885\n",
       "puppo                    2093\n",
       "URL                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive_clean.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "representative-brunswick",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                 2117\n",
       "in_reply_to_status_id    2117\n",
       "in_reply_to_user_id      2117\n",
       "timestamp                2117\n",
       "source                   2117\n",
       "text                     2117\n",
       "expanded_urls            2117\n",
       "rating_numerator         2117\n",
       "rating_denominator       2117\n",
       "name                     1402\n",
       "doggo                      84\n",
       "floofer                    10\n",
       "pupper                    232\n",
       "puppo                      24\n",
       "URL                      2117\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive_clean.notnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "defensive-halifax",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',\n",
       "       'source', 'text', 'expanded_urls', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo',\n",
       "       'URL'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive_clean.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fleet-sampling",
   "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>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>URL</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+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>At first I thought this was a shy doggo, but i...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/854010172...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>doggo</td>\n",
       "      <td>floofer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/854010172...</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              source  \\\n",
       "200 2017-04-17 16:34:26+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                  text  \\\n",
       "200  At first I thought this was a shy doggo, but i...   \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  NaN  doggo  floofer    NaN   NaN   \n",
       "\n",
       "                                                   URL  \n",
       "200  https://twitter.com/dog_rates/status/854010172...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive_clean[twitter_archive_clean.floofer.notnull()][twitter_archive_clean.doggo.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "filled-ridge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lucy       11\n",
       "Charlie    11\n",
       "Oliver     10\n",
       "Cooper     10\n",
       "Penny       9\n",
       "           ..\n",
       "Barclay     1\n",
       "Mitch       1\n",
       "Brandy      1\n",
       "Karl        1\n",
       "Jaspers     1\n",
       "Name: name, Length: 937, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive_clean.name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "separate-reality",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "arranged-meaning",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "image_predictions_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "lesser-equality",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype              \n",
      "---  ------          --------------  -----              \n",
      " 0   created_at      2354 non-null   datetime64[ns, UTC]\n",
      " 1   id              2354 non-null   int64              \n",
      " 2   retweet_count   2354 non-null   int64              \n",
      " 3   favorite_count  2354 non-null   int64              \n",
      "dtypes: datetime64[ns, UTC](1), int64(3)\n",
      "memory usage: 73.7 KB\n"
     ]
    }
   ],
   "source": [
    "tweet_API_data_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flying-decline",
   "metadata": {},
   "source": [
    "## Code\n",
    "\n",
    "### Tidiness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "rural-struggle",
   "metadata": {},
   "outputs": [],
   "source": [
    "#preparing for merging tables by setting all ID related columns into strings\n",
    "twitter_archive_clean.tweet_id = twitter_archive_clean.tweet_id.astype(str)\n",
    "image_predictions_clean.tweet_id = image_predictions_clean.tweet_id.astype(str)\n",
    "tweet_API_data_clean.id = tweet_API_data_clean.id.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "narrow-foundation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>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>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>URL</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2117</th>\n",
       "      <td>670421925039075328</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-28 02:00:17+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Meet Herb. 12/10 https://t.co/tLRyYvCci3</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670421925...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Herb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670421925...</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2118</th>\n",
       "      <td>670420569653809152</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-28 01:54:54+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Damon. The newest presidential candida...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670420569...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Damon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670420569...</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2119</th>\n",
       "      <td>670417414769758208</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-28 01:42:22+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Sharp dog here. Introverted. Loves purple. Not...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670417414...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670417414...</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2120</th>\n",
       "      <td>670411370698022913</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-28 01:18:21+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Meet Scooter. He's ready for his first day of ...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670411370...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Scooter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670411370...</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2121</th>\n",
       "      <td>670408998013820928</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-28 01:08:55+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Peanut. He was the World Table Tennis ...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670408998...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Peanut</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670408998...</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-16 00:24:50+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-16 00:04:52+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-15 23:21:54+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-15 23:05:30+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-15 22:32:08+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id in_reply_to_status_id in_reply_to_user_id  \\\n",
       "2117  670421925039075328                   nan                 nan   \n",
       "2118  670420569653809152                   nan                 nan   \n",
       "2119  670417414769758208                   nan                 nan   \n",
       "2120  670411370698022913                   nan                 nan   \n",
       "2121  670408998013820928                   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              source  \\\n",
       "2117 2015-11-28 02:00:17+00:00  Twitter for iPhone   \n",
       "2118 2015-11-28 01:54:54+00:00  Twitter for iPhone   \n",
       "2119 2015-11-28 01:42:22+00:00  Twitter for iPhone   \n",
       "2120 2015-11-28 01:18:21+00:00  Twitter for iPhone   \n",
       "2121 2015-11-28 01:08:55+00:00  Twitter for iPhone   \n",
       "...                        ...                 ...   \n",
       "2351 2015-11-16 00:24:50+00:00  Twitter for iPhone   \n",
       "2352 2015-11-16 00:04:52+00:00  Twitter for iPhone   \n",
       "2353 2015-11-15 23:21:54+00:00  Twitter for iPhone   \n",
       "2354 2015-11-15 23:05:30+00:00  Twitter for iPhone   \n",
       "2355 2015-11-15 22:32:08+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                   text  \\\n",
       "2117           Meet Herb. 12/10 https://t.co/tLRyYvCci3   \n",
       "2118  This is Damon. The newest presidential candida...   \n",
       "2119  Sharp dog here. Introverted. Loves purple. Not...   \n",
       "2120  Meet Scooter. He's ready for his first day of ...   \n",
       "2121  This is Peanut. He was the World Table Tennis ...   \n",
       "...                                                 ...   \n",
       "2351  Here we have a 1949 1st generation vulpix. Enj...   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...   \n",
       "2353  Here is a very happy pup. Big fan of well-main...   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "2117  https://twitter.com/dog_rates/status/670421925...                12   \n",
       "2118  https://twitter.com/dog_rates/status/670420569...                10   \n",
       "2119  https://twitter.com/dog_rates/status/670417414...                 6   \n",
       "2120  https://twitter.com/dog_rates/status/670411370...                12   \n",
       "2121  https://twitter.com/dog_rates/status/670408998...                10   \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",
       "2117                  10     Herb   NaN     NaN    NaN   NaN   \n",
       "2118                  10    Damon   NaN     NaN    NaN   NaN   \n",
       "2119                  10      NaN   NaN     NaN    NaN   NaN   \n",
       "2120                  10  Scooter   NaN     NaN    NaN   NaN   \n",
       "2121                  10   Peanut   NaN     NaN    NaN   NaN   \n",
       "...                  ...      ...   ...     ...    ...   ...   \n",
       "2351                  10      NaN   NaN     NaN    NaN   NaN   \n",
       "2352                  10      NaN   NaN     NaN    NaN   NaN   \n",
       "2353                  10      NaN   NaN     NaN    NaN   NaN   \n",
       "2354                  10      NaN   NaN     NaN    NaN   NaN   \n",
       "2355                  10      NaN   NaN     NaN    NaN   NaN   \n",
       "\n",
       "                                                    URL dog_stage  \n",
       "2117  https://twitter.com/dog_rates/status/670421925...     doggo  \n",
       "2118  https://twitter.com/dog_rates/status/670420569...     doggo  \n",
       "2119  https://twitter.com/dog_rates/status/670417414...     doggo  \n",
       "2120  https://twitter.com/dog_rates/status/670411370...     doggo  \n",
       "2121  https://twitter.com/dog_rates/status/670408998...     doggo  \n",
       "...                                                 ...       ...  \n",
       "2351  https://twitter.com/dog_rates/status/666049248...    pupper  \n",
       "2352  https://twitter.com/dog_rates/status/666044226...    pupper  \n",
       "2353  https://twitter.com/dog_rates/status/666033412...    pupper  \n",
       "2354  https://twitter.com/dog_rates/status/666029285...    pupper  \n",
       "2355  https://twitter.com/dog_rates/status/666020888...    pupper  \n",
       "\n",
       "[234 rows x 16 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Melting dog stage columns into 1 column\n",
    "dog_stage_list = ['doggo', 'floofer', 'pupper', 'puppo']\n",
    "result = pd.Series()\n",
    "for first_stage in dog_stage_list:\n",
    "    for second_stage in dog_stage_list:\n",
    "        if first_stage == second_stage:\n",
    "            continue\n",
    "        first = twitter_archive_clean[first_stage].dropna()\n",
    "        second = twitter_archive_clean[second_stage].dropna()\n",
    "        result = result.append((first + \" & \" + second).dropna())\n",
    "\n",
    "twitter_archive_clean['multiple_dog_stages'] = np.nan\n",
    "twitter_archive_clean['multiple_dog_stages'] = twitter_archive_clean['multiple_dog_stages'].append(result, ignore_index=True)\n",
    "result = pd.Series()\n",
    "for dog_stage in dog_stage_list:\n",
    "    result = result.append(twitter_archive_clean[dog_stage][twitter_archive_clean[dog_stage].notnull()])\n",
    "result = result.append(twitter_archive_clean.multiple_dog_stages[twitter_archive_clean.multiple_dog_stages.notnull()])\n",
    "twitter_archive_clean['dog_stage'] = np.nan\n",
    "twitter_archive_clean['dog_stage'] = twitter_archive_clean['dog_stage'].append(result, ignore_index=True, verify_integrity=True)\n",
    "twitter_archive_clean.drop('multiple_dog_stages', inplace=True, axis=1)\n",
    "twitter_archive_clean[twitter_archive_clean.dog_stage.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "identified-globe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable                  Type              Data/Info\n",
      "-----------------------------------------------------\n",
      "BytesIO                   type              <class '_io.BytesIO'>\n",
      "base_URL                  str               https://twitter.com/dog_rates/status/\n",
      "col                       str               quoted_status\n",
      "column                    str               p3\n",
      "columns                   list              n=3\n",
      "dog_stage                 str               puppo\n",
      "dog_stage_list            list              n=4\n",
      "f                         BufferedWriter    <_io.BufferedWriter name='image-predictions.tsv'>\n",
      "first                     Series            12      puppo\\n14      pu<...>ame: puppo, dtype: object\n",
      "first_stage               str               puppo\n",
      "head                      list              n=4\n",
      "image_predictions         DataFrame                         tweet_id <...>n[2075 rows x 12 columns]\n",
      "image_predictions_clean   DataFrame                         tweet_id <...>n[2075 rows x 12 columns]\n",
      "json                      module            <module 'json' from 'C:\\\\<...>\\lib\\\\json\\\\__init__.py'>\n",
      "matplotlib                module            <module 'matplotlib' from<...>matplotlib\\\\__init__.py'>\n",
      "name                      str               O\n",
      "names_to_change           list              n=20\n",
      "np                        module            <module 'numpy' from 'C:\\<...>ges\\\\numpy\\\\__init__.py'>\n",
      "os                        module            <module 'os' from 'C:\\\\Us<...>taWrangling\\\\lib\\\\os.py'>\n",
      "pd                        module            <module 'pandas' from 'C:<...>es\\\\pandas\\\\__init__.py'>\n",
      "plt                       module            <module 'matplotlib.pyplo<...>\\\\matplotlib\\\\pyplot.py'>\n",
      "requests                  module            <module 'requests' from '<...>\\\\requests\\\\__init__.py'>\n",
      "result                    Series            9                doggo\\n4<...>ength: 374, dtype: object\n",
      "second                    Series            29      pupper\\n49      p<...>ength: 232, dtype: object\n",
      "second_stage              str               puppo\n",
      "to_drop                   list              n=3\n",
      "tweepy                    module            <module 'tweepy' from 'C:<...>es\\\\tweepy\\\\__init__.py'>\n",
      "tweet_API_data            DataFrame                             creat<...>\\n[2354 rows x 4 columns]\n",
      "tweet_API_data_clean      DataFrame                             creat<...>\\n[2354 rows x 4 columns]\n",
      "twitter_archive           DataFrame                         tweet_id <...>n[2356 rows x 17 columns]\n",
      "twitter_archive_clean     DataFrame                         tweet_id <...>n[2117 rows x 16 columns]\n",
      "url                       str               https://d17h27t6h515a5.cl<...>ons/image-predictions.tsv\n",
      "warnings                  module            <module 'warnings' from '<...>gling\\\\lib\\\\warnings.py'>\n"
     ]
    }
   ],
   "source": [
    "whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "welcome-output",
   "metadata": {},
   "outputs": [],
   "source": [
    "#finding most probable prediction from image_predictions dataframe\n",
    "\n",
    "image_predictions_clean['predicted_breed'] = np.NaN\n",
    "for i in range(len(image_predictions_clean)):\n",
    "    if image_predictions_clean['p1_dog'][i] == 1:\n",
    "        image_predictions_clean['predicted_breed'][i] = image_predictions_clean['p1'][i]\n",
    "    elif image_predictions_clean['p2_dog'][i] == 1:\n",
    "        image_predictions_clean['predicted_breed'][i] = image_predictions_clean['p2'][i]\n",
    "    elif image_predictions_clean['p3_dog'][i] == 1:\n",
    "        image_predictions_clean['predicted_breed'][i] = image_predictions_clean['p3'][i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "indonesian-hormone",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename 'id' column to 'tweet_id' to match the other tables\n",
    "tweet_API_data_clean['tweet_id'] = tweet_API_data_clean.id\n",
    "tweet_API_data_clean.drop('id', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "stopped-spank",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mergin dataframes\n",
    "#twitter_archive_clean['predicted_breed'] = image_predictions_clean['predicted_breed']\n",
    "#twitter_archive_clean['retweet_count'] = tweet_API_data_clean.retweet_count.astype('Int64')\n",
    "#twitter_archive_clean['favorite_count'] = tweet_API_data_clean.favorite_count.astype('Int64')\n",
    "twitter_archive_clean = pd.merge(twitter_archive_clean,image_predictions_clean[['tweet_id','predicted_breed']], on = 'tweet_id', how='left')\n",
    "twitter_archive_clean = pd.merge(twitter_archive_clean,tweet_API_data_clean, on = 'tweet_id', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "durable-guide",
   "metadata": {},
   "source": [
    "## Test\n",
    "\n",
    "### Tidiness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "lasting-person",
   "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>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>URL</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>predicted_breed</th>\n",
       "      <th>created_at</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>8853</td>\n",
       "      <td>39467</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+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</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>NaN</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>NaN</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>6514</td>\n",
       "      <td>33819</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+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</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>NaN</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>NaN</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>4328</td>\n",
       "      <td>25461</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+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</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>NaN</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>NaN</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>8964</td>\n",
       "      <td>42908</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+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</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>NaN</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>NaN</td>\n",
       "      <td>Basset</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "      <td>9774</td>\n",
       "      <td>41048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-16 00:24:50+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>pupper</td>\n",
       "      <td>Miniature Pinscher</td>\n",
       "      <td>2015-11-16 00:24:50+00:00</td>\n",
       "      <td>41</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2113</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-16 00:04:52+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>pupper</td>\n",
       "      <td>Rhodesian Ridgeback</td>\n",
       "      <td>2015-11-16 00:04:52+00:00</td>\n",
       "      <td>147</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2114</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-15 23:21:54+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>pupper</td>\n",
       "      <td>German Shepherd</td>\n",
       "      <td>2015-11-15 23:21:54+00:00</td>\n",
       "      <td>47</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-15 23:05:30+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>pupper</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>2015-11-15 23:05:30+00:00</td>\n",
       "      <td>48</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>2015-11-15 22:32:08+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>pupper</td>\n",
       "      <td>Welsh Springer Spaniel</td>\n",
       "      <td>2015-11-15 22:32:08+00:00</td>\n",
       "      <td>532</td>\n",
       "      <td>2535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2117 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id in_reply_to_status_id in_reply_to_user_id  \\\n",
       "0     892420643555336193                   nan                 nan   \n",
       "1     892177421306343426                   nan                 nan   \n",
       "2     891815181378084864                   nan                 nan   \n",
       "3     891689557279858688                   nan                 nan   \n",
       "4     891327558926688256                   nan                 nan   \n",
       "...                  ...                   ...                 ...   \n",
       "2112  666049248165822465                   nan                 nan   \n",
       "2113  666044226329800704                   nan                 nan   \n",
       "2114  666033412701032449                   nan                 nan   \n",
       "2115  666029285002620928                   nan                 nan   \n",
       "2116  666020888022790149                   nan                 nan   \n",
       "\n",
       "                     timestamp              source  \\\n",
       "0    2017-08-01 16:23:56+00:00  Twitter for iPhone   \n",
       "1    2017-08-01 00:17:27+00:00  Twitter for iPhone   \n",
       "2    2017-07-31 00:18:03+00:00  Twitter for iPhone   \n",
       "3    2017-07-30 15:58:51+00:00  Twitter for iPhone   \n",
       "4    2017-07-29 16:00:24+00:00  Twitter for iPhone   \n",
       "...                        ...                 ...   \n",
       "2112 2015-11-16 00:24:50+00:00  Twitter for iPhone   \n",
       "2113 2015-11-16 00:04:52+00:00  Twitter for iPhone   \n",
       "2114 2015-11-15 23:21:54+00:00  Twitter for iPhone   \n",
       "2115 2015-11-15 23:05:30+00:00  Twitter for iPhone   \n",
       "2116 2015-11-15 22:32:08+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                   text  \\\n",
       "0     This is Phineas. He's a mystical boy. Only eve...   \n",
       "1     This is Tilly. She's just checking pup on you....   \n",
       "2     This is Archie. He is a rare Norwegian Pouncin...   \n",
       "3     This is Darla. She commenced a snooze mid meal...   \n",
       "4     This is Franklin. He would like you to stop ca...   \n",
       "...                                                 ...   \n",
       "2112  Here we have a 1949 1st generation vulpix. Enj...   \n",
       "2113  This is a purebred Piers Morgan. Loves to Netf...   \n",
       "2114  Here is a very happy pup. Big fan of well-main...   \n",
       "2115  This is a western brown Mitsubishi terrier. Up...   \n",
       "2116  Here we have a Japanese Irish Setter. Lost eye...   \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",
       "2112  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2113  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2114  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2115  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2116  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator      name doggo floofer pupper puppo  \\\n",
       "0                     10   Phineas   NaN     NaN    NaN   NaN   \n",
       "1                     10     Tilly   NaN     NaN    NaN   NaN   \n",
       "2                     10    Archie   NaN     NaN    NaN   NaN   \n",
       "3                     10     Darla   NaN     NaN    NaN   NaN   \n",
       "4                     10  Franklin   NaN     NaN    NaN   NaN   \n",
       "...                  ...       ...   ...     ...    ...   ...   \n",
       "2112                  10       NaN   NaN     NaN    NaN   NaN   \n",
       "2113                  10       NaN   NaN     NaN    NaN   NaN   \n",
       "2114                  10       NaN   NaN     NaN    NaN   NaN   \n",
       "2115                  10       NaN   NaN     NaN    NaN   NaN   \n",
       "2116                  10       NaN   NaN     NaN    NaN   NaN   \n",
       "\n",
       "                                                    URL dog_stage  \\\n",
       "0     https://twitter.com/dog_rates/status/892420643...       NaN   \n",
       "1     https://twitter.com/dog_rates/status/892177421...       NaN   \n",
       "2     https://twitter.com/dog_rates/status/891815181...       NaN   \n",
       "3     https://twitter.com/dog_rates/status/891689557...       NaN   \n",
       "4     https://twitter.com/dog_rates/status/891327558...       NaN   \n",
       "...                                                 ...       ...   \n",
       "2112  https://twitter.com/dog_rates/status/666049248...    pupper   \n",
       "2113  https://twitter.com/dog_rates/status/666044226...    pupper   \n",
       "2114  https://twitter.com/dog_rates/status/666033412...    pupper   \n",
       "2115  https://twitter.com/dog_rates/status/666029285...    pupper   \n",
       "2116  https://twitter.com/dog_rates/status/666020888...    pupper   \n",
       "\n",
       "             predicted_breed                created_at  retweet_count  \\\n",
       "0                        NaN 2017-08-01 16:23:56+00:00           8853   \n",
       "1                  Chihuahua 2017-08-01 00:17:27+00:00           6514   \n",
       "2                  Chihuahua 2017-07-31 00:18:03+00:00           4328   \n",
       "3         Labrador Retriever 2017-07-30 15:58:51+00:00           8964   \n",
       "4                     Basset 2017-07-29 16:00:24+00:00           9774   \n",
       "...                      ...                       ...            ...   \n",
       "2112      Miniature Pinscher 2015-11-16 00:24:50+00:00             41   \n",
       "2113     Rhodesian Ridgeback 2015-11-16 00:04:52+00:00            147   \n",
       "2114         German Shepherd 2015-11-15 23:21:54+00:00             47   \n",
       "2115                 Redbone 2015-11-15 23:05:30+00:00             48   \n",
       "2116  Welsh Springer Spaniel 2015-11-15 22:32:08+00:00            532   \n",
       "\n",
       "      favorite_count  \n",
       "0              39467  \n",
       "1              33819  \n",
       "2              25461  \n",
       "3              42908  \n",
       "4              41048  \n",
       "...              ...  \n",
       "2112             111  \n",
       "2113             311  \n",
       "2114             128  \n",
       "2115             132  \n",
       "2116            2535  \n",
       "\n",
       "[2117 rows x 20 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fantastic-celtic",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2117 entries, 0 to 2116\n",
      "Data columns (total 20 columns):\n",
      " #   Column                 Non-Null Count  Dtype              \n",
      "---  ------                 --------------  -----              \n",
      " 0   tweet_id               2117 non-null   object             \n",
      " 1   in_reply_to_status_id  2117 non-null   object             \n",
      " 2   in_reply_to_user_id    2117 non-null   object             \n",
      " 3   timestamp              2117 non-null   datetime64[ns, UTC]\n",
      " 4   source                 2117 non-null   object             \n",
      " 5   text                   2117 non-null   object             \n",
      " 6   expanded_urls          2117 non-null   object             \n",
      " 7   rating_numerator       2117 non-null   int64              \n",
      " 8   rating_denominator     2117 non-null   int64              \n",
      " 9   name                   1402 non-null   object             \n",
      " 10  doggo                  84 non-null     object             \n",
      " 11  floofer                10 non-null     object             \n",
      " 12  pupper                 232 non-null    object             \n",
      " 13  puppo                  24 non-null     object             \n",
      " 14  URL                    2117 non-null   object             \n",
      " 15  dog_stage              234 non-null    object             \n",
      " 16  predicted_breed        1686 non-null   object             \n",
      " 17  created_at             2117 non-null   datetime64[ns, UTC]\n",
      " 18  retweet_count          2117 non-null   int64              \n",
      " 19  favorite_count         2117 non-null   int64              \n",
      "dtypes: datetime64[ns, UTC](2), int64(4), object(14)\n",
      "memory usage: 347.3+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_archive_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "collectible-plate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 13 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   tweet_id         2075 non-null   object \n",
      " 1   jpg_url          2075 non-null   object \n",
      " 2   img_num          2075 non-null   int64  \n",
      " 3   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",
      " 12  predicted_breed  1751 non-null   object \n",
      "dtypes: bool(3), float64(3), int64(1), object(6)\n",
      "memory usage: 168.3+ KB\n"
     ]
    }
   ],
   "source": [
    "image_predictions_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "continuous-swift",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype              \n",
      "---  ------          --------------  -----              \n",
      " 0   created_at      2354 non-null   datetime64[ns, UTC]\n",
      " 1   retweet_count   2354 non-null   int64              \n",
      " 2   favorite_count  2354 non-null   int64              \n",
      " 3   tweet_id        2354 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), int64(2), object(1)\n",
      "memory usage: 73.7+ KB\n"
     ]
    }
   ],
   "source": [
    "tweet_API_data_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elegant-drawing",
   "metadata": {},
   "source": [
    "# Saving cleaned Master Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "green-radiation",
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive_clean.to_csv(\"twitter_archive_master.csv.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frozen-steal",
   "metadata": {},
   "source": [
    "# Visualizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "suburban-remedy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',\n",
       "       'source', 'text', 'expanded_urls', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo',\n",
       "       'URL', 'dog_stage', 'predicted_breed', 'created_at', 'retweet_count',\n",
       "       'favorite_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData = pd.read_csv(\"twitter_archive_master.csv.csv\")\n",
    "myData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "cordless-petersburg",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myData.source.value_counts().plot(kind='bar', xlabel='Source', ylabel='Number of tweets', title=\"Number of tweets collected from each source\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "elder-radio",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myData.dog_stage.value_counts().plot(kind='bar', xlabel = 'Dog Stage', ylabel='Number of dogs', title=\"Number of dogs in each dog stage\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "rolled-singing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper    9\n",
       "doggo     6\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "myData[myData.predicted_breed == 'Golden Retriever'].dog_stage.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "compact-method",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Golden Retriever        158\n",
       "Labrador Retriever      108\n",
       "Pembroke                 95\n",
       "Chihuahua                91\n",
       "Pug                      62\n",
       "                       ... \n",
       "Irish Wolfhound           1\n",
       "Scotch Terrier            1\n",
       "Bouvier Des Flandres      1\n",
       "Japanese Spaniel          1\n",
       "Standard Schnauzer        1\n",
       "Name: predicted_breed, Length: 113, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData.predicted_breed.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "rational-narrow",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2826.723665564478"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData.retweet_count.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "significant-showcase",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8936.635805384978"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData.favorite_count.mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-DataWrangling] *",
   "language": "python",
   "name": "conda-env-.conda-DataWrangling-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}