wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrangle data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import all required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "from timeit import default_timer as timer\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import tweepy\n",
    "import re\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gathering data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The WeRateDogs Twitter archive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a CSV file with data that I downloaded manually from the Udacity website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_arch = pd.read_csv('data/twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The tweet image predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download a TSV file from Udacity using requests library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "resp = requests.get(url)\n",
    "with open('data/' + url.split('/')[-1], mode='wb') as file:\n",
    "    file.write(resp.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the TSV file with pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_pred = pd.read_csv('data/image-predictions.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Twitter API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the tweet IDs in the WeRateDogs Twitter archive, let's query the Twitter API for each tweet's JSON data using Python's Tweepy library and store each tweet's entire set of JSON data in a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_twitter_data():\n",
    "    # Query Twitter API for each tweet in the Twitter archive \n",
    "    # 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 = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "    auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "    api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)\n",
    "\n",
    "    # Query Twitter's API for JSON data for each tweet ID \n",
    "    # in the Twitter archive\n",
    "    fails_dict = {}\n",
    "    start = timer()\n",
    "    # Save each tweet's returned JSON as a new line in a .txt file\n",
    "    with open('data/tweet_json.txt', 'w') as outfile:\n",
    "        # This loop will likely take 20-30 minutes to run because of \n",
    "        # Twitter's rate limit\n",
    "        for tweet_id in tw_arch.tweet_id.values:\n",
    "            try:\n",
    "                tweet = api.get_status(tweet_id)\n",
    "                json.dump(tweet._json, outfile)\n",
    "                outfile.write('\\n')\n",
    "            except tweepy.TweepError as e:\n",
    "                print(\"Fail\", tweet_id)\n",
    "                fails_dict[tweet_id] = e\n",
    "                pass\n",
    "\n",
    "    end = timer()\n",
    "    print(end - start)\n",
    "    print(fails_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail 888202515573088257\n",
      "Fail 873697596434513921\n",
      "Fail 872668790621863937\n",
      "Fail 872261713294495745\n",
      "Fail 869988702071779329\n",
      "Fail 866816280283807744\n",
      "Fail 861769973181624320\n",
      "Fail 856602993587888130\n",
      "Fail 851953902622658560\n",
      "Fail 845459076796616705\n",
      "Fail 844704788403113984\n",
      "Fail 842892208864923648\n",
      "Fail 837366284874571778\n",
      "Fail 837012587749474308\n",
      "Fail 829374341691346946\n",
      "Fail 827228250799742977\n",
      "Fail 812747805718642688\n",
      "Fail 802247111496568832\n",
      "Fail 779123168116150273\n",
      "Fail 775096608509886464\n",
      "Fail 771004394259247104\n",
      "Fail 770743923962707968\n",
      "Fail 759566828574212096\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Rate limit reached. Sleeping for: 434\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail 754011816964026368\n",
      "Fail 680055455951884288\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Rate limit reached. Sleeping for: 426\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2104.480035308001\n",
      "{888202515573088257: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 873697596434513921: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 872668790621863937: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 872261713294495745: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 869988702071779329: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 866816280283807744: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 861769973181624320: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 856602993587888130: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 851953902622658560: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 845459076796616705: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 844704788403113984: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 842892208864923648: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 837366284874571778: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 837012587749474308: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 829374341691346946: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 827228250799742977: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 812747805718642688: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 802247111496568832: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 779123168116150273: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 775096608509886464: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 771004394259247104: TweepError([{'code': 179, 'message': 'Sorry, you are not authorized to see this status.'}]), 770743923962707968: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 759566828574212096: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 754011816964026368: TweepError([{'code': 144, 'message': 'No status found with that ID.'}]), 680055455951884288: TweepError([{'code': 144, 'message': 'No status found with that ID.'}])}\n"
     ]
    }
   ],
   "source": [
    "# Uncomment this to update 'tweet_json.txt'\n",
    "download_twitter_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read json file line by line into a pandas DataFrame\n",
    "df_tw = pd.read_json('data/tweet_json.txt', lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only required columns\n",
    "df_tw = df_tw[['id', 'favorite_count', 'retweet_count']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "# View info of the Twitter archive\n",
    "tw_arch.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
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       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
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       "  </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",
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      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View first 5 rows of the archieve\n",
    "tw_arch.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
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       "      <th>rating_denominator</th>\n",
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       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
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       "      <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",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\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",
       "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",
       "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",
       "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",
       "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",
       "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",
       "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  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View last 5 rows of the archieve\n",
    "tw_arch.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2088</th>\n",
       "      <td>670792680469889025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-29 02:33:32 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Antony. He's a Sheraton Tetrahedron. S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/670792680...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Antony</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>683142553609318400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-02 04:27:31 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Brandy. She's a member of the Bloods. Men...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/683142553...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Brandy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>779123168116150273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-09-23 01:00:13 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Reggie. He hugs everyone he meets. 12/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/779123168...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Reggie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>703407252292673536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-02-27 02:32:12 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This pupper doesn't understand gates. 10/10 so...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/703407252...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>747594051852075008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-06-28 00:54:46 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Again w the sharks guys. This week is about do...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/747594051...</td>\n",
       "      <td>11</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",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "2088  670792680469889025                    NaN                  NaN   \n",
       "1657  683142553609318400                    NaN                  NaN   \n",
       "750   779123168116150273                    NaN                  NaN   \n",
       "1357  703407252292673536                    NaN                  NaN   \n",
       "1008  747594051852075008                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "2088  2015-11-29 02:33:32 +0000   \n",
       "1657  2016-01-02 04:27:31 +0000   \n",
       "750   2016-09-23 01:00:13 +0000   \n",
       "1357  2016-02-27 02:32:12 +0000   \n",
       "1008  2016-06-28 00:54:46 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "2088  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1657  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "750   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1357  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1008  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "2088  This is Antony. He's a Sheraton Tetrahedron. S...                  NaN   \n",
       "1657  Meet Brandy. She's a member of the Bloods. Men...                  NaN   \n",
       "750   This is Reggie. He hugs everyone he meets. 12/...                  NaN   \n",
       "1357  This pupper doesn't understand gates. 10/10 so...                  NaN   \n",
       "1008  Again w the sharks guys. This week is about do...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "2088                       NaN                        NaN   \n",
       "1657                       NaN                        NaN   \n",
       "750                        NaN                        NaN   \n",
       "1357                       NaN                        NaN   \n",
       "1008                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "2088  https://twitter.com/dog_rates/status/670792680...                 7   \n",
       "1657  https://twitter.com/dog_rates/status/683142553...                 9   \n",
       "750   https://twitter.com/dog_rates/status/779123168...                12   \n",
       "1357  https://twitter.com/dog_rates/status/703407252...                10   \n",
       "1008  https://twitter.com/dog_rates/status/747594051...                11   \n",
       "\n",
       "      rating_denominator    name doggo floofer  pupper puppo  \n",
       "2088                  10  Antony  None    None    None  None  \n",
       "1657                  10  Brandy  None    None  pupper  None  \n",
       "750                   10  Reggie  None    None    None  None  \n",
       "1357                  10    None  None    None  pupper  None  \n",
       "1008                  10    None  None    None    None  None  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get sample from the archieve\n",
    "tw_arch.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.356000e+03</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>2356.000000</td>\n",
       "      <td>2356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.427716e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>7.720400e+17</td>\n",
       "      <td>1.241698e+16</td>\n",
       "      <td>13.126486</td>\n",
       "      <td>10.455433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.856705e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>6.236928e+16</td>\n",
       "      <td>9.599254e+16</td>\n",
       "      <td>45.876648</td>\n",
       "      <td>6.745237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>6.661041e+17</td>\n",
       "      <td>7.832140e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783989e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>7.186315e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.196279e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>7.804657e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.993373e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>8.203146e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>7.874618e+17</td>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "count  2.356000e+03           7.800000e+01         7.800000e+01   \n",
       "mean   7.427716e+17           7.455079e+17         2.014171e+16   \n",
       "std    6.856705e+16           7.582492e+16         1.252797e+17   \n",
       "min    6.660209e+17           6.658147e+17         1.185634e+07   \n",
       "25%    6.783989e+17           6.757419e+17         3.086374e+08   \n",
       "50%    7.196279e+17           7.038708e+17         4.196984e+09   \n",
       "75%    7.993373e+17           8.257804e+17         4.196984e+09   \n",
       "max    8.924206e+17           8.862664e+17         8.405479e+17   \n",
       "\n",
       "       retweeted_status_id  retweeted_status_user_id  rating_numerator  \\\n",
       "count         1.810000e+02              1.810000e+02       2356.000000   \n",
       "mean          7.720400e+17              1.241698e+16         13.126486   \n",
       "std           6.236928e+16              9.599254e+16         45.876648   \n",
       "min           6.661041e+17              7.832140e+05          0.000000   \n",
       "25%           7.186315e+17              4.196984e+09         10.000000   \n",
       "50%           7.804657e+17              4.196984e+09         11.000000   \n",
       "75%           8.203146e+17              4.196984e+09         12.000000   \n",
       "max           8.874740e+17              7.874618e+17       1776.000000   \n",
       "\n",
       "       rating_denominator  \n",
       "count         2356.000000  \n",
       "mean            10.455433  \n",
       "std              6.745237  \n",
       "min              0.000000  \n",
       "25%             10.000000  \n",
       "50%             10.000000  \n",
       "75%             10.000000  \n",
       "max            170.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View descriptive statistics of the archive\n",
    "tw_arch.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "tw_arch.tweet_id.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a               55\n",
       "the              8\n",
       "an               7\n",
       "very             5\n",
       "one              4\n",
       "just             4\n",
       "quite            4\n",
       "getting          2\n",
       "actually         2\n",
       "not              2\n",
       "mad              2\n",
       "my               1\n",
       "unacceptable     1\n",
       "all              1\n",
       "incredibly       1\n",
       "officially       1\n",
       "such             1\n",
       "light            1\n",
       "life             1\n",
       "old              1\n",
       "by               1\n",
       "his              1\n",
       "space            1\n",
       "infuriating      1\n",
       "this             1\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find lowercase names and check them \n",
    "tw_arch[tw_arch.name.str.islower()].name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>I have a new hero and his name is Howard. 14/10 https://t.co/gzLHboL7Sk</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Ladies and gentlemen... I found Pipsy. He may have changed his name to Pablo, but he never changed his love for the sea. Pupgraded to 14/10 https:...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Sorry for the lack of posts today. I came home from school and had to spend quality time with my puppo. Her name is Zoey and she's 13/10 https://t...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>RT @dog_rates: This a Norwegian Pewterschmidt named Tickles. Ears for days. 12/10 I care deeply for Tickles https://t.co/0aDF62KVP7</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>His name is Charley and he already has a new set of wheels thanks to donations. I heard his top speed was also increased. 13/10 for Charley</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>This is my dog. Her name is Zoey. She knows I've been rating other dogs. She's not happy. 13/10 no bias at all https://t.co/ep1NkYoiwB</td>\n",
       "      <td>my</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>When bae calls your name from across the room. 12/10 (vid by @christinemcc98) https://t.co/xolcXA6gxe</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>We normally don't rate bears but this one seems nice. Her name is Thea. Appears rather fluffy. 10/10 good bear https://t.co/fZc7MixeeT</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>This pup's name is Sabertooth (parents must be cool). Ears for days. Jumps unannounced. 9/10 would pet diligently https://t.co/iazoiNUviP</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>This is a Sizzlin Menorah spaniel from Brooklyn named Wylie. Lovable eyes. Chiller as hell. 10/10 and I'm out.. poof https://t.co/7E0AiJXPmI</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>This is a Lofted Aphrodisiac Terrier named Kip. Big fan of bed n breakfasts. Fits perfectly. 10/10 would pet firmly https://t.co/gKlLpNzIl3</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>This is a Tuscaloosa Alcatraz named Jacob (Yacōb). Loves to sit in swing. Stellar tongue. 11/10 look at his feet https://t.co/2IslQ8ZSc7</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>This is a Helvetica Listerine named Rufus. This time Rufus will be ready for the UPS guy. He'll never expect it 9/10 https://t.co/34OhVhMkVr</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086</th>\n",
       "      <td>I would do radical things in the name of Dog God. I'd believe every word in that book. 10/10 https://t.co/9ZuGAmLZDR</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>This is a Deciduous Trimester mix named Spork. Only 1 ear works. No seat belt. Incredibly reckless. 9/10 still cute https://t.co/CtuJoLHiDo</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>This is a Rich Mahogany Seltzer named Cherokee. Just got destroyed by a snowball. Isn't very happy about it. 9/10 https://t.co/98ZBi6o4dj</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>This is a Speckled Cauliflower Yosemite named Hemry. He's terrified of intruder dog. Not one bit comfortable. 9/10 https://t.co/yV3Qgjh8iN</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2146</th>\n",
       "      <td>This is a spotted Lipitor Rumpelstiltskin named Alphred. He can't wait for the Turkey. 10/10 would pet really well https://t.co/6GUGO7azNX</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>This is a Coriander Baton Rouge named Alfredo. Loves to cuddle with smaller well-dressed dog. 10/10 would hug lots https://t.co/eCRdwouKCl</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2166</th>\n",
       "      <td>Here we have a Gingivitis Pumpernickel named Zeus. Unmatched tennis ball capacity. 10/10 would highly recommend https://t.co/jPkd7hhX7m</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>This is a Slovakian Helter Skelter Feta named Leroi. Likes to skip on roofs. Good traction. Much balance. 10/10 wow! https://t.co/Dmy2mY2Qj5</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg</td>\n",
       "      <td>an</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>This is a Birmingham Quagmire named Chuk. Loves to relax and watch the game while sippin on that iced mocha. 10/10 https://t.co/HvNg9JWxFt</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>Here we have an Azerbaijani Buttermilk named Guss. He sees a demon baby Hitler behind his owner. 10/10 stays alert https://t.co/aeZykWwiJN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>This is a Trans Siberian Kellogg named Alfonso. Huge ass eyeballs. Actually Dobby from Harry Potter. 7/10 https://t.co/XpseHBlAAb</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>This is a Shotokon Macadamia mix named Cheryl. Sophisticated af. Looks like a disappointed librarian. Shh (lol) 9/10 https://t.co/J4GnJ5Swba</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>This is a rare Hungarian Pinot named Jessiga. She is either mid-stroke or got stuck in the washing machine. 8/10 https://t.co/ZU0i0KJyqD</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>This is a southwest Coriander named Klint. Hat looks expensive. Still on house arrest :(\\n9/10 https://t.co/IQTOMqDUIe</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td>Another topnotch dog. His name is Big Jumpy Rat. Massive ass feet. Superior tail. Jumps high af. 12/10 great pup https://t.co/seESNzgsdm</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269</th>\n",
       "      <td>This a Norwegian Pewterschmidt named Tickles. Ears for days. 12/10 I care deeply for Tickles https://t.co/0aDF62KVP7</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>This is a northern Wahoo named Kohl. He runs this town. Chases tumbleweeds. Draws gun wicked fast. 11/10 legendary https://t.co/J4vn2rOYFk</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>This is a Dasani Kingfisher from Maine. His name is Daryl. Daryl doesn't like being swallowed by a panda. 8/10 https://t.co/jpaeu6LNmW</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>This is a curly Ticonderoga named Pepe. No feet. Loves to jet ski. 11/10 would hug until forever https://t.co/cyDfaK8NBc</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2306</th>\n",
       "      <td>These are Peruvian Feldspars. Their names are Cupit and Prencer. Both resemble Rand Paul. Sick outfits 10/10 &amp;amp; 10/10 https://t.co/ZnEMHBsAs1</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2311</th>\n",
       "      <td>This is a purebred Bacardi named Octaviath. Can shoot spaghetti out of mouth. 10/10 https://t.co/uEvsGLOFHa</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>This is a golden Buckminsterfullerene named Johm. Drives trucks. Lumberjack (?). Enjoys wall. 8/10 would hug softly https://t.co/uQbZJM2DQB</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                       text  \\\n",
       "35                                                                                  I have a new hero and his name is Howard. 14/10 https://t.co/gzLHboL7Sk   \n",
       "149   Ladies and gentlemen... I found Pipsy. He may have changed his name to Pablo, but he never changed his love for the sea. Pupgraded to 14/10 https:...   \n",
       "168   Sorry for the lack of posts today. I came home from school and had to spend quality time with my puppo. Her name is Zoey and she's 13/10 https://t...   \n",
       "603                     RT @dog_rates: This a Norwegian Pewterschmidt named Tickles. Ears for days. 12/10 I care deeply for Tickles https://t.co/0aDF62KVP7   \n",
       "843             His name is Charley and he already has a new set of wheels thanks to donations. I heard his top speed was also increased. 13/10 for Charley   \n",
       "852                  This is my dog. Her name is Zoey. She knows I've been rating other dogs. She's not happy. 13/10 no bias at all https://t.co/ep1NkYoiwB   \n",
       "1596                                                  When bae calls your name from across the room. 12/10 (vid by @christinemcc98) https://t.co/xolcXA6gxe   \n",
       "1678                 We normally don't rate bears but this one seems nice. Her name is Thea. Appears rather fluffy. 10/10 good bear https://t.co/fZc7MixeeT   \n",
       "1734              This pup's name is Sabertooth (parents must be cool). Ears for days. Jumps unannounced. 9/10 would pet diligently https://t.co/iazoiNUviP   \n",
       "1853           This is a Sizzlin Menorah spaniel from Brooklyn named Wylie. Lovable eyes. Chiller as hell. 10/10 and I'm out.. poof https://t.co/7E0AiJXPmI   \n",
       "1955            This is a Lofted Aphrodisiac Terrier named Kip. Big fan of bed n breakfasts. Fits perfectly. 10/10 would pet firmly https://t.co/gKlLpNzIl3   \n",
       "2034               This is a Tuscaloosa Alcatraz named Jacob (Yacōb). Loves to sit in swing. Stellar tongue. 11/10 look at his feet https://t.co/2IslQ8ZSc7   \n",
       "2066           This is a Helvetica Listerine named Rufus. This time Rufus will be ready for the UPS guy. He'll never expect it 9/10 https://t.co/34OhVhMkVr   \n",
       "2086                                   I would do radical things in the name of Dog God. I'd believe every word in that book. 10/10 https://t.co/9ZuGAmLZDR   \n",
       "2116            This is a Deciduous Trimester mix named Spork. Only 1 ear works. No seat belt. Incredibly reckless. 9/10 still cute https://t.co/CtuJoLHiDo   \n",
       "2125              This is a Rich Mahogany Seltzer named Cherokee. Just got destroyed by a snowball. Isn't very happy about it. 9/10 https://t.co/98ZBi6o4dj   \n",
       "2128             This is a Speckled Cauliflower Yosemite named Hemry. He's terrified of intruder dog. Not one bit comfortable. 9/10 https://t.co/yV3Qgjh8iN   \n",
       "2146             This is a spotted Lipitor Rumpelstiltskin named Alphred. He can't wait for the Turkey. 10/10 would pet really well https://t.co/6GUGO7azNX   \n",
       "2161             This is a Coriander Baton Rouge named Alfredo. Loves to cuddle with smaller well-dressed dog. 10/10 would hug lots https://t.co/eCRdwouKCl   \n",
       "2166                Here we have a Gingivitis Pumpernickel named Zeus. Unmatched tennis ball capacity. 10/10 would highly recommend https://t.co/jPkd7hhX7m   \n",
       "2191           This is a Slovakian Helter Skelter Feta named Leroi. Likes to skip on roofs. Good traction. Much balance. 10/10 wow! https://t.co/Dmy2mY2Qj5   \n",
       "2204            This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg   \n",
       "2218             This is a Birmingham Quagmire named Chuk. Loves to relax and watch the game while sippin on that iced mocha. 10/10 https://t.co/HvNg9JWxFt   \n",
       "2227             Here we have an Azerbaijani Buttermilk named Guss. He sees a demon baby Hitler behind his owner. 10/10 stays alert https://t.co/aeZykWwiJN   \n",
       "2235                      This is a Trans Siberian Kellogg named Alfonso. Huge ass eyeballs. Actually Dobby from Harry Potter. 7/10 https://t.co/XpseHBlAAb   \n",
       "2249           This is a Shotokon Macadamia mix named Cheryl. Sophisticated af. Looks like a disappointed librarian. Shh (lol) 9/10 https://t.co/J4GnJ5Swba   \n",
       "2255               This is a rare Hungarian Pinot named Jessiga. She is either mid-stroke or got stuck in the washing machine. 8/10 https://t.co/ZU0i0KJyqD   \n",
       "2264                                 This is a southwest Coriander named Klint. Hat looks expensive. Still on house arrest :(\\n9/10 https://t.co/IQTOMqDUIe   \n",
       "2267               Another topnotch dog. His name is Big Jumpy Rat. Massive ass feet. Superior tail. Jumps high af. 12/10 great pup https://t.co/seESNzgsdm   \n",
       "2269                                   This a Norwegian Pewterschmidt named Tickles. Ears for days. 12/10 I care deeply for Tickles https://t.co/0aDF62KVP7   \n",
       "2273             This is a northern Wahoo named Kohl. He runs this town. Chases tumbleweeds. Draws gun wicked fast. 11/10 legendary https://t.co/J4vn2rOYFk   \n",
       "2287                 This is a Dasani Kingfisher from Maine. His name is Daryl. Daryl doesn't like being swallowed by a panda. 8/10 https://t.co/jpaeu6LNmW   \n",
       "2304                               This is a curly Ticonderoga named Pepe. No feet. Loves to jet ski. 11/10 would hug until forever https://t.co/cyDfaK8NBc   \n",
       "2306       These are Peruvian Feldspars. Their names are Cupit and Prencer. Both resemble Rand Paul. Sick outfits 10/10 &amp; 10/10 https://t.co/ZnEMHBsAs1   \n",
       "2311                                            This is a purebred Bacardi named Octaviath. Can shoot spaghetti out of mouth. 10/10 https://t.co/uEvsGLOFHa   \n",
       "2314            This is a golden Buckminsterfullerene named Johm. Drives trucks. Lumberjack (?). Enjoys wall. 8/10 would hug softly https://t.co/uQbZJM2DQB   \n",
       "\n",
       "      name  \n",
       "35    None  \n",
       "149   None  \n",
       "168   None  \n",
       "603   None  \n",
       "843   None  \n",
       "852     my  \n",
       "1596  None  \n",
       "1678  None  \n",
       "1734  None  \n",
       "1853     a  \n",
       "1955     a  \n",
       "2034     a  \n",
       "2066     a  \n",
       "2086  None  \n",
       "2116     a  \n",
       "2125     a  \n",
       "2128     a  \n",
       "2146     a  \n",
       "2161     a  \n",
       "2166  None  \n",
       "2191     a  \n",
       "2204    an  \n",
       "2218     a  \n",
       "2227  None  \n",
       "2235     a  \n",
       "2249     a  \n",
       "2255     a  \n",
       "2264     a  \n",
       "2267  None  \n",
       "2269  None  \n",
       "2273     a  \n",
       "2287     a  \n",
       "2304     a  \n",
       "2306  None  \n",
       "2311     a  \n",
       "2314     a  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set wider column width to see more information\n",
    "pd.set_option('max_colwidth', 150)\n",
    "# Try to find word name in rows where name is lowercase or None\n",
    "tw_arch[((tw_arch.name == 'None') | tw_arch.name.str.islower())\n",
    "        & tw_arch.text.str.contains('name')][['text', 'name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>@jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>@docmisterio account started on 11/15/15</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t ...</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE</td>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp;amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3</td>\n",
       "      <td>143</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55</td>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq</td>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw</td>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                       text  \\\n",
       "313                                                      @jonnysun @Lin_Manuel ok jomny I know you're excited but 960/00 isn't a valid rating, 13/10 is tho   \n",
       "342                                                                                                                @docmisterio account started on 11/15/15   \n",
       "433                                                     The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd   \n",
       "516   Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t ...   \n",
       "784            RT @dog_rates: After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https:/…   \n",
       "902                                                                          Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE   \n",
       "1068           After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ   \n",
       "1120                              Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv   \n",
       "1165                                                                                       Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a   \n",
       "1202                                  This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq   \n",
       "1228                                                          Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1   \n",
       "1254                                           Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12   \n",
       "1274     From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK   \n",
       "1351                                                                     Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa   \n",
       "1433                                                           Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ   \n",
       "1598               Yes I do realize a rating of 4/20 would've been fitting. However, it would be unjust to give these cooperative pups that low of a rating   \n",
       "1634                         Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3   \n",
       "1635           Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55   \n",
       "1662            This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5   \n",
       "1663            I'm aware that I could've said 20/16, but here at WeRateDogs we are very professional. An inconsistent rating scale is simply irresponsible   \n",
       "1779                                                                                 IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq   \n",
       "1843                                            Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw   \n",
       "2335               This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv   \n",
       "\n",
       "      rating_numerator  rating_denominator  \n",
       "313                960                   0  \n",
       "342                 11                  15  \n",
       "433                 84                  70  \n",
       "516                 24                   7  \n",
       "784                  9                  11  \n",
       "902                165                 150  \n",
       "1068                 9                  11  \n",
       "1120               204                 170  \n",
       "1165                 4                  20  \n",
       "1202                50                  50  \n",
       "1228                99                  90  \n",
       "1254                80                  80  \n",
       "1274                45                  50  \n",
       "1351                60                  50  \n",
       "1433                44                  40  \n",
       "1598                 4                  20  \n",
       "1634               143                 130  \n",
       "1635               121                 110  \n",
       "1662                 7                  11  \n",
       "1663                20                  16  \n",
       "1779               144                 120  \n",
       "1843                88                  80  \n",
       "2335                 1                   2  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show wrong ratings rows\n",
    "tw_arch[tw_arch.rating_denominator != 10][['text', 'rating_numerator', 'rating_denominator']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>     2221\n",
       "<a href=\"http://vine.co\" rel=\"nofollow\">Vine - Make a Scene</a>                          91\n",
       "<a href=\"http://twitter.com\" rel=\"nofollow\">Twitter Web Client</a>                       33\n",
       "<a href=\"https://about.twitter.com/products/tweetdeck\" rel=\"nofollow\">TweetDeck</a>      11\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show all source options\n",
    "tw_arch.source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "# View info of the tweet image predictions dataframe\n",
    "tw_pred.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for jpg_url duplicates\n",
    "tw_pred.jpg_url.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View first 5 rows of the predictions\n",
    "tw_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2070</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "2070  891327558926688256  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "2071  891689557279858688  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "2072  891815181378084864  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "2073  892177421306343426  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2074  892420643555336193  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num           p1   p1_conf  p1_dog                  p2   p2_conf  \\\n",
       "2070        2       basset  0.555712    True    English_springer  0.225770   \n",
       "2071        1  paper_towel  0.170278   False  Labrador_retriever  0.168086   \n",
       "2072        1    Chihuahua  0.716012    True            malamute  0.078253   \n",
       "2073        1    Chihuahua  0.323581    True            Pekinese  0.090647   \n",
       "2074        1       orange  0.097049   False               bagel  0.085851   \n",
       "\n",
       "      p2_dog                           p3   p3_conf  p3_dog  \n",
       "2070    True  German_short-haired_pointer  0.175219    True  \n",
       "2071    True                      spatula  0.040836   False  \n",
       "2072    True                       kelpie  0.031379    True  \n",
       "2073    True                     papillon  0.068957    True  \n",
       "2074   False                       banana  0.076110   False  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View last 5 rows of the predictions\n",
    "tw_pred.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>1380</th>\n",
       "      <td>765222098633691136</td>\n",
       "      <td>https://pbs.twimg.com/media/Cp6db4-XYAAMmqL.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>dalmatian</td>\n",
       "      <td>0.556595</td>\n",
       "      <td>True</td>\n",
       "      <td>whippet</td>\n",
       "      <td>0.151047</td>\n",
       "      <td>True</td>\n",
       "      <td>American_Staffordshire_terrier</td>\n",
       "      <td>0.096435</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>735635087207878657</td>\n",
       "      <td>https://pbs.twimg.com/media/CjWANBlVAAAaN-a.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.891871</td>\n",
       "      <td>True</td>\n",
       "      <td>goose</td>\n",
       "      <td>0.014377</td>\n",
       "      <td>False</td>\n",
       "      <td>fur_coat</td>\n",
       "      <td>0.008451</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>831939777352105988</td>\n",
       "      <td>https://pbs.twimg.com/media/C4uk0EWWQAAaZm1.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.153862</td>\n",
       "      <td>True</td>\n",
       "      <td>marmot</td>\n",
       "      <td>0.091234</td>\n",
       "      <td>False</td>\n",
       "      <td>grey_fox</td>\n",
       "      <td>0.090644</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>672975131468300288</td>\n",
       "      <td>https://pbs.twimg.com/media/CVbjRSIWsAElw2s.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.836421</td>\n",
       "      <td>True</td>\n",
       "      <td>Brabancon_griffon</td>\n",
       "      <td>0.044668</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.036570</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1180</th>\n",
       "      <td>738166403467907072</td>\n",
       "      <td>https://pbs.twimg.com/media/Cj5-aUQUgAAb43p.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>keeshond</td>\n",
       "      <td>0.878886</td>\n",
       "      <td>True</td>\n",
       "      <td>Norwegian_elkhound</td>\n",
       "      <td>0.086659</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.021280</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "1380  765222098633691136  https://pbs.twimg.com/media/Cp6db4-XYAAMmqL.jpg   \n",
       "1166  735635087207878657  https://pbs.twimg.com/media/CjWANBlVAAAaN-a.jpg   \n",
       "1801  831939777352105988  https://pbs.twimg.com/media/C4uk0EWWQAAaZm1.jpg   \n",
       "370   672975131468300288  https://pbs.twimg.com/media/CVbjRSIWsAElw2s.jpg   \n",
       "1180  738166403467907072  https://pbs.twimg.com/media/Cj5-aUQUgAAb43p.jpg   \n",
       "\n",
       "      img_num          p1   p1_conf  p1_dog                  p2   p2_conf  \\\n",
       "1380        1   dalmatian  0.556595    True             whippet  0.151047   \n",
       "1166        1         pug  0.891871    True               goose  0.014377   \n",
       "1801        1  Pomeranian  0.153862    True              marmot  0.091234   \n",
       "370         1         pug  0.836421    True   Brabancon_griffon  0.044668   \n",
       "1180        2    keeshond  0.878886    True  Norwegian_elkhound  0.086659   \n",
       "\n",
       "      p2_dog                              p3   p3_conf  p3_dog  \n",
       "1380    True  American_Staffordshire_terrier  0.096435    True  \n",
       "1166   False                        fur_coat  0.008451   False  \n",
       "1801   False                        grey_fox  0.090644   False  \n",
       "370     True                  French_bulldog  0.036570    True  \n",
       "1180    True                        malamute  0.021280    True  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get sample from the predictions\n",
    "tw_pred.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View descriptive statistics of the predictions\n",
    "tw_pred.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2331 entries, 0 to 2330\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   id              2331 non-null   int64\n",
      " 1   favorite_count  2331 non-null   int64\n",
      " 2   retweet_count   2331 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 54.8 KB\n"
     ]
    }
   ],
   "source": [
    "# View info of the downloaded tweet data dataframe\n",
    "df_tw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>id</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>36113</td>\n",
       "      <td>7690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>31149</td>\n",
       "      <td>5689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>23458</td>\n",
       "      <td>3772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>39398</td>\n",
       "      <td>7856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>37621</td>\n",
       "      <td>8462</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                   id  favorite_count  retweet_count\n",
       "0  892420643555336193           36113           7690\n",
       "1  892177421306343426           31149           5689\n",
       "2  891815181378084864           23458           3772\n",
       "3  891689557279858688           39398           7856\n",
       "4  891327558926688256           37621           8462"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View first 5 rows of the downloaded data\n",
    "df_tw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>96</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>272</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>112</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2329</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>121</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2408</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id  favorite_count  retweet_count\n",
       "2326  666049248165822465              96             40\n",
       "2327  666044226329800704             272            131\n",
       "2328  666033412701032449             112             41\n",
       "2329  666029285002620928             121             42\n",
       "2330  666020888022790149            2408            460"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View last 5 rows of the downloaded data\n",
    "df_tw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>700747788515020802</td>\n",
       "      <td>22674</td>\n",
       "      <td>9248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>780858289093574656</td>\n",
       "      <td>7280</td>\n",
       "      <td>2043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>777641927919427584</td>\n",
       "      <td>0</td>\n",
       "      <td>4290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>808344865868283904</td>\n",
       "      <td>43276</td>\n",
       "      <td>21300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>747651430853525504</td>\n",
       "      <td>1380</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id  favorite_count  retweet_count\n",
       "1361  700747788515020802           22674           9248\n",
       "719   780858289093574656            7280           2043\n",
       "748   777641927919427584               0           4290\n",
       "512   808344865868283904           43276          21300\n",
       "981   747651430853525504            1380            155"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get sample from the downloaded data\n",
    "df_tw.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.331000e+03</td>\n",
       "      <td>2331.000000</td>\n",
       "      <td>2331.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.419079e+17</td>\n",
       "      <td>7540.806521</td>\n",
       "      <td>2698.289146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.823170e+16</td>\n",
       "      <td>11705.340727</td>\n",
       "      <td>4561.608355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.782670e+17</td>\n",
       "      <td>1317.000000</td>\n",
       "      <td>546.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.182469e+17</td>\n",
       "      <td>3277.000000</td>\n",
       "      <td>1266.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.986692e+17</td>\n",
       "      <td>9234.000000</td>\n",
       "      <td>3137.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>155731.000000</td>\n",
       "      <td>77603.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  favorite_count  retweet_count\n",
       "count  2.331000e+03     2331.000000    2331.000000\n",
       "mean   7.419079e+17     7540.806521    2698.289146\n",
       "std    6.823170e+16    11705.340727    4561.608355\n",
       "min    6.660209e+17        0.000000       1.000000\n",
       "25%    6.782670e+17     1317.000000     546.000000\n",
       "50%    7.182469e+17     3277.000000    1266.000000\n",
       "75%    7.986692e+17     9234.000000    3137.000000\n",
       "max    8.924206e+17   155731.000000   77603.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View descriptive statistics of the downloaded data\n",
    "df_tw.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality issues\n",
    "We only want original ratings (no retweets) that have images. Though there are 5000+ tweets in the dataset, not all are dog ratings and some are retweets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retweets\n",
    "We can determine if the row is a retweet when the \"retweeted_status_id\" column contains a value. Filter out retweet rows and drop unneeded columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replies\n",
    "We can determine if the row is a reply when the \"in_reply_to_status_id\" column contains a value. Filter out all reply rows and drop unneeded columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Datatypes\n",
    "Fix datatypes of \"timestamp\", \"rating_numerator\", \"rating_denomerator\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Source\n",
    "Remove HTML tags from source and make it more readable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Names\n",
    "We can notice that all lowercase names are wrong. Also, there are dogs with 'None' names. For several of these rows we can find a name in a text column. Get names from text where possible and filter out others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ratings\n",
    "There are rows where text contains several ratings and they were extracted wrongly. Extract right rating where it is possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Image URL duplicates\n",
    "There are duplicated img urls. Drop duplicates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Format p1, p2, p3 values\n",
    "We can notice that some of the values are lowercase and some of them uppercase. Names also have '_' separator. Capitalise all the breeds and replace '_' character with space."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidyness Issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stage\n",
    "There are separate columns for every stage. Combine them into a single one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neural network predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 3 separate predictions with 3 columns in each. Combine them into 2 columns: breed and conf."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Join to a single Master DataFrame\n",
    "This will allow to get rid of rows without images also."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create copies for every DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_arch_clean = tw_arch.copy()\n",
    "tw_pred_clean = tw_pred.copy()\n",
    "df_tw_clean = df_tw.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out all retweets\n",
    "tw_arch_clean = tw_arch_clean[tw_arch_clean.retweeted_status_id.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2175 entries, 0 to 2355\n",
      "Data columns (total 3 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   retweeted_status_id         0 non-null      float64\n",
      " 1   retweeted_status_user_id    0 non-null      float64\n",
      " 2   retweeted_status_timestamp  0 non-null      object \n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 68.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check before drop\n",
    "tw_arch_clean[[\n",
    "    'retweeted_status_id', 'retweeted_status_user_id',\n",
    "    'retweeted_status_timestamp'\n",
    "]].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unneeded columns\n",
    "tw_arch_clean.drop([\n",
    "    'retweeted_status_id', 'retweeted_status_user_id',\n",
    "    'retweeted_status_timestamp'\n",
    "],\n",
    "                   axis=1,\n",
    "                   inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2175 entries, 0 to 2355\n",
      "Data columns (total 14 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   tweet_id               2175 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              2175 non-null   object \n",
      " 4   source                 2175 non-null   object \n",
      " 5   text                   2175 non-null   object \n",
      " 6   expanded_urls          2117 non-null   object \n",
      " 7   rating_numerator       2175 non-null   int64  \n",
      " 8   rating_denominator     2175 non-null   int64  \n",
      " 9   name                   2175 non-null   object \n",
      " 10  doggo                  2175 non-null   object \n",
      " 11  floofer                2175 non-null   object \n",
      " 12  pupper                 2175 non-null   object \n",
      " 13  puppo                  2175 non-null   object \n",
      "dtypes: float64(2), int64(3), object(9)\n",
      "memory usage: 254.9+ KB\n"
     ]
    }
   ],
   "source": [
    "tw_arch_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out all replies\n",
    "tw_arch_clean = tw_arch_clean[tw_arch_clean.in_reply_to_status_id.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2097 entries, 0 to 2355\n",
      "Data columns (total 2 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   in_reply_to_status_id  0 non-null      float64\n",
      " 1   in_reply_to_user_id    0 non-null      float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 49.1 KB\n"
     ]
    }
   ],
   "source": [
    "# Check before drop\n",
    "tw_arch_clean[['in_reply_to_status_id', 'in_reply_to_user_id']].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unneeded columns\n",
    "tw_arch_clean.drop(['in_reply_to_status_id', 'in_reply_to_user_id'],\n",
    "                   axis=1,\n",
    "                   inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2097 entries, 0 to 2355\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   tweet_id            2097 non-null   int64 \n",
      " 1   timestamp           2097 non-null   object\n",
      " 2   source              2097 non-null   object\n",
      " 3   text                2097 non-null   object\n",
      " 4   expanded_urls       2094 non-null   object\n",
      " 5   rating_numerator    2097 non-null   int64 \n",
      " 6   rating_denominator  2097 non-null   int64 \n",
      " 7   name                2097 non-null   object\n",
      " 8   doggo               2097 non-null   object\n",
      " 9   floofer             2097 non-null   object\n",
      " 10  pupper              2097 non-null   object\n",
      " 11  puppo               2097 non-null   object\n",
      "dtypes: int64(3), object(9)\n",
      "memory usage: 213.0+ KB\n"
     ]
    }
   ],
   "source": [
    "tw_arch_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datatypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change datatype to datetime\n",
    "tw_arch_clean['timestamp'] = pd.to_datetime(tw_arch_clean['timestamp'])\n",
    "# Change datatype to float\n",
    "tw_arch_clean['rating_numerator'] = tw_arch_clean['rating_numerator'].astype('float')\n",
    "tw_arch_clean['rating_denominator'] = tw_arch_clean['rating_denominator'].astype('float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2097 entries, 0 to 2355\n",
      "Data columns (total 12 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2097 non-null   int64              \n",
      " 1   timestamp           2097 non-null   datetime64[ns, UTC]\n",
      " 2   source              2097 non-null   object             \n",
      " 3   text                2097 non-null   object             \n",
      " 4   expanded_urls       2094 non-null   object             \n",
      " 5   rating_numerator    2097 non-null   float64            \n",
      " 6   rating_denominator  2097 non-null   float64            \n",
      " 7   name                2097 non-null   object             \n",
      " 8   doggo               2097 non-null   object             \n",
      " 9   floofer             2097 non-null   object             \n",
      " 10  pupper              2097 non-null   object             \n",
      " 11  puppo               2097 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(1), object(8)\n",
      "memory usage: 213.0+ KB\n"
     ]
    }
   ],
   "source": [
    "tw_arch_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_arch_clean.loc[tw_arch_clean['source'].str.contains('iPhone'), 'source'] = 'iPhone'\n",
    "tw_arch_clean.loc[tw_arch_clean['source'].str.contains('Vine'), 'source'] = 'Vine'\n",
    "tw_arch_clean.loc[tw_arch_clean['source'].str.contains('Web'), 'source'] = 'Web'\n",
    "tw_arch_clean.loc[tw_arch_clean['source'].str.contains('TweetDeck'), 'source'] = 'TweetDeck'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iPhone       1964\n",
       "Vine           91\n",
       "Web            31\n",
       "TweetDeck      11\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_arch_clean.source.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>I have a new hero and his name is Howard. 14/10 https://t.co/gzLHboL7Sk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Sorry for the lack of posts today. I came home from school and had to spend quality time with my puppo. Her name is Zoey and she's 13/10 https://t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>This is my dog. Her name is Zoey. She knows I've been rating other dogs. She's not happy. 13/10 no bias at all https://t.co/ep1NkYoiwB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>When bae calls your name from across the room. 12/10 (vid by @christinemcc98) https://t.co/xolcXA6gxe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>We normally don't rate bears but this one seems nice. Her name is Thea. Appears rather fluffy. 10/10 good bear https://t.co/fZc7MixeeT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>This pup's name is Sabertooth (parents must be cool). Ears for days. Jumps unannounced. 9/10 would pet diligently https://t.co/iazoiNUviP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>This is a Sizzlin Menorah spaniel from Brooklyn named Wylie. Lovable eyes. Chiller as hell. 10/10 and I'm out.. poof https://t.co/7E0AiJXPmI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>This is a Lofted Aphrodisiac Terrier named Kip. Big fan of bed n breakfasts. Fits perfectly. 10/10 would pet firmly https://t.co/gKlLpNzIl3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>This is a Tuscaloosa Alcatraz named Jacob (Yacōb). Loves to sit in swing. Stellar tongue. 11/10 look at his feet https://t.co/2IslQ8ZSc7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>This is a Helvetica Listerine named Rufus. This time Rufus will be ready for the UPS guy. He'll never expect it 9/10 https://t.co/34OhVhMkVr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086</th>\n",
       "      <td>I would do radical things in the name of Dog God. I'd believe every word in that book. 10/10 https://t.co/9ZuGAmLZDR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>This is a Deciduous Trimester mix named Spork. Only 1 ear works. No seat belt. Incredibly reckless. 9/10 still cute https://t.co/CtuJoLHiDo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>This is a Rich Mahogany Seltzer named Cherokee. Just got destroyed by a snowball. Isn't very happy about it. 9/10 https://t.co/98ZBi6o4dj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>This is a Speckled Cauliflower Yosemite named Hemry. He's terrified of intruder dog. Not one bit comfortable. 9/10 https://t.co/yV3Qgjh8iN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2146</th>\n",
       "      <td>This is a spotted Lipitor Rumpelstiltskin named Alphred. He can't wait for the Turkey. 10/10 would pet really well https://t.co/6GUGO7azNX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>This is a Coriander Baton Rouge named Alfredo. Loves to cuddle with smaller well-dressed dog. 10/10 would hug lots https://t.co/eCRdwouKCl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2166</th>\n",
       "      <td>Here we have a Gingivitis Pumpernickel named Zeus. Unmatched tennis ball capacity. 10/10 would highly recommend https://t.co/jPkd7hhX7m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>This is a Slovakian Helter Skelter Feta named Leroi. Likes to skip on roofs. Good traction. Much balance. 10/10 wow! https://t.co/Dmy2mY2Qj5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2204</th>\n",
       "      <td>This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>This is a Birmingham Quagmire named Chuk. Loves to relax and watch the game while sippin on that iced mocha. 10/10 https://t.co/HvNg9JWxFt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>Here we have an Azerbaijani Buttermilk named Guss. He sees a demon baby Hitler behind his owner. 10/10 stays alert https://t.co/aeZykWwiJN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>This is a Trans Siberian Kellogg named Alfonso. Huge ass eyeballs. Actually Dobby from Harry Potter. 7/10 https://t.co/XpseHBlAAb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>This is a Shotokon Macadamia mix named Cheryl. Sophisticated af. Looks like a disappointed librarian. Shh (lol) 9/10 https://t.co/J4GnJ5Swba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>This is a rare Hungarian Pinot named Jessiga. She is either mid-stroke or got stuck in the washing machine. 8/10 https://t.co/ZU0i0KJyqD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>This is a southwest Coriander named Klint. Hat looks expensive. Still on house arrest :(\\n9/10 https://t.co/IQTOMqDUIe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td>Another topnotch dog. His name is Big Jumpy Rat. Massive ass feet. Superior tail. Jumps high af. 12/10 great pup https://t.co/seESNzgsdm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269</th>\n",
       "      <td>This a Norwegian Pewterschmidt named Tickles. Ears for days. 12/10 I care deeply for Tickles https://t.co/0aDF62KVP7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>This is a northern Wahoo named Kohl. He runs this town. Chases tumbleweeds. Draws gun wicked fast. 11/10 legendary https://t.co/J4vn2rOYFk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>This is a Dasani Kingfisher from Maine. His name is Daryl. Daryl doesn't like being swallowed by a panda. 8/10 https://t.co/jpaeu6LNmW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>This is a curly Ticonderoga named Pepe. No feet. Loves to jet ski. 11/10 would hug until forever https://t.co/cyDfaK8NBc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2306</th>\n",
       "      <td>These are Peruvian Feldspars. Their names are Cupit and Prencer. Both resemble Rand Paul. Sick outfits 10/10 &amp;amp; 10/10 https://t.co/ZnEMHBsAs1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2311</th>\n",
       "      <td>This is a purebred Bacardi named Octaviath. Can shoot spaghetti out of mouth. 10/10 https://t.co/uEvsGLOFHa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>This is a golden Buckminsterfullerene named Johm. Drives trucks. Lumberjack (?). Enjoys wall. 8/10 would hug softly https://t.co/uQbZJM2DQB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                       text\n",
       "35                                                                                  I have a new hero and his name is Howard. 14/10 https://t.co/gzLHboL7Sk\n",
       "168   Sorry for the lack of posts today. I came home from school and had to spend quality time with my puppo. Her name is Zoey and she's 13/10 https://t...\n",
       "852                  This is my dog. Her name is Zoey. She knows I've been rating other dogs. She's not happy. 13/10 no bias at all https://t.co/ep1NkYoiwB\n",
       "1596                                                  When bae calls your name from across the room. 12/10 (vid by @christinemcc98) https://t.co/xolcXA6gxe\n",
       "1678                 We normally don't rate bears but this one seems nice. Her name is Thea. Appears rather fluffy. 10/10 good bear https://t.co/fZc7MixeeT\n",
       "1734              This pup's name is Sabertooth (parents must be cool). Ears for days. Jumps unannounced. 9/10 would pet diligently https://t.co/iazoiNUviP\n",
       "1853           This is a Sizzlin Menorah spaniel from Brooklyn named Wylie. Lovable eyes. Chiller as hell. 10/10 and I'm out.. poof https://t.co/7E0AiJXPmI\n",
       "1955            This is a Lofted Aphrodisiac Terrier named Kip. Big fan of bed n breakfasts. Fits perfectly. 10/10 would pet firmly https://t.co/gKlLpNzIl3\n",
       "2034               This is a Tuscaloosa Alcatraz named Jacob (Yacōb). Loves to sit in swing. Stellar tongue. 11/10 look at his feet https://t.co/2IslQ8ZSc7\n",
       "2066           This is a Helvetica Listerine named Rufus. This time Rufus will be ready for the UPS guy. He'll never expect it 9/10 https://t.co/34OhVhMkVr\n",
       "2086                                   I would do radical things in the name of Dog God. I'd believe every word in that book. 10/10 https://t.co/9ZuGAmLZDR\n",
       "2116            This is a Deciduous Trimester mix named Spork. Only 1 ear works. No seat belt. Incredibly reckless. 9/10 still cute https://t.co/CtuJoLHiDo\n",
       "2125              This is a Rich Mahogany Seltzer named Cherokee. Just got destroyed by a snowball. Isn't very happy about it. 9/10 https://t.co/98ZBi6o4dj\n",
       "2128             This is a Speckled Cauliflower Yosemite named Hemry. He's terrified of intruder dog. Not one bit comfortable. 9/10 https://t.co/yV3Qgjh8iN\n",
       "2146             This is a spotted Lipitor Rumpelstiltskin named Alphred. He can't wait for the Turkey. 10/10 would pet really well https://t.co/6GUGO7azNX\n",
       "2161             This is a Coriander Baton Rouge named Alfredo. Loves to cuddle with smaller well-dressed dog. 10/10 would hug lots https://t.co/eCRdwouKCl\n",
       "2166                Here we have a Gingivitis Pumpernickel named Zeus. Unmatched tennis ball capacity. 10/10 would highly recommend https://t.co/jPkd7hhX7m\n",
       "2191           This is a Slovakian Helter Skelter Feta named Leroi. Likes to skip on roofs. Good traction. Much balance. 10/10 wow! https://t.co/Dmy2mY2Qj5\n",
       "2204            This is an Irish Rigatoni terrier named Berta. Completely made of rope. No eyes. Quite large. Loves to dance. 10/10 https://t.co/EM5fDykrJg\n",
       "2218             This is a Birmingham Quagmire named Chuk. Loves to relax and watch the game while sippin on that iced mocha. 10/10 https://t.co/HvNg9JWxFt\n",
       "2227             Here we have an Azerbaijani Buttermilk named Guss. He sees a demon baby Hitler behind his owner. 10/10 stays alert https://t.co/aeZykWwiJN\n",
       "2235                      This is a Trans Siberian Kellogg named Alfonso. Huge ass eyeballs. Actually Dobby from Harry Potter. 7/10 https://t.co/XpseHBlAAb\n",
       "2249           This is a Shotokon Macadamia mix named Cheryl. Sophisticated af. Looks like a disappointed librarian. Shh (lol) 9/10 https://t.co/J4GnJ5Swba\n",
       "2255               This is a rare Hungarian Pinot named Jessiga. She is either mid-stroke or got stuck in the washing machine. 8/10 https://t.co/ZU0i0KJyqD\n",
       "2264                                 This is a southwest Coriander named Klint. Hat looks expensive. Still on house arrest :(\\n9/10 https://t.co/IQTOMqDUIe\n",
       "2267               Another topnotch dog. His name is Big Jumpy Rat. Massive ass feet. Superior tail. Jumps high af. 12/10 great pup https://t.co/seESNzgsdm\n",
       "2269                                   This a Norwegian Pewterschmidt named Tickles. Ears for days. 12/10 I care deeply for Tickles https://t.co/0aDF62KVP7\n",
       "2273             This is a northern Wahoo named Kohl. He runs this town. Chases tumbleweeds. Draws gun wicked fast. 11/10 legendary https://t.co/J4vn2rOYFk\n",
       "2287                 This is a Dasani Kingfisher from Maine. His name is Daryl. Daryl doesn't like being swallowed by a panda. 8/10 https://t.co/jpaeu6LNmW\n",
       "2304                               This is a curly Ticonderoga named Pepe. No feet. Loves to jet ski. 11/10 would hug until forever https://t.co/cyDfaK8NBc\n",
       "2306       These are Peruvian Feldspars. Their names are Cupit and Prencer. Both resemble Rand Paul. Sick outfits 10/10 &amp; 10/10 https://t.co/ZnEMHBsAs1\n",
       "2311                                            This is a purebred Bacardi named Octaviath. Can shoot spaghetti out of mouth. 10/10 https://t.co/uEvsGLOFHa\n",
       "2314            This is a golden Buckminsterfullerene named Johm. Drives trucks. Lumberjack (?). Enjoys wall. 8/10 would hug softly https://t.co/uQbZJM2DQB"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get rows with word 'name' in text where name is lowercase or None\n",
    "wrdf = tw_arch_clean[(\n",
    "    (tw_arch_clean.name == 'None') | tw_arch_clean.name.str.islower())\n",
    "                    & tw_arch_clean.text.str.contains('name')][['text', 'name']]\n",
    "wrdf[['text']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can notice that names of a dogs go after 'name is' and 'named'. Also there is a special case 'Big Jumpy Rat' - process it manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get text column index\n",
    "text_idx = wrdf.columns.get_loc(\"text\")\n",
    "\n",
    "# Iterate every row\n",
    "for index, row in wrdf.iterrows():\n",
    "    t = row[text_idx]\n",
    "    \n",
    "    # Check for name is\n",
    "    _, _, after = t.partition('name is')\n",
    "    if after:\n",
    "        name = after.split()[0].strip(' ,.')\n",
    "        tw_arch_clean.loc[index, 'name'] = name\n",
    "        continue\n",
    "\n",
    "    # Check for named\n",
    "    _, _, after = t.partition('named')\n",
    "    if after:\n",
    "        name = after.split()[0].strip(' ,.')\n",
    "        tw_arch_clean.loc[index, 'name'] = name\n",
    "        continue\n",
    "\n",
    "    # Special case\n",
    "    if 'Big Jumpy Rat' in t:\n",
    "        tw_arch_clean.loc[index, 'name'] = 'Big Jumpy Rat'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can filter out the rest cases with wrong names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_arch_clean = tw_arch_clean[(tw_arch_clean.name != 'None')\n",
    "                              & (tw_arch_clean.name.str.islower() == False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at short names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>This is Mo. No one will push him around in the grocery cart. He's quite pupset about it. 11/10 I volunteer https://t.co/feNwTq12S5</td>\n",
       "      <td>Mo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>This is Bo. He was a very good First Doggo. 14/10 would be an absolute honor to pet https://t.co/AdPKrI8BZ1</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>This is Bo. He's going to make me cry. 13/10 please get off the bus for him Carly https://t.co/U7FvBZo6Bq</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>This is Bo. He's a West Congolese Bugaboop Snuggle. Rather exotic. Master of the head tilt. 12/10 would pay to pet https://t.co/2jwxxtNzoN</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>This is O'Malley. That is how he sleeps. Doesn't care what you think about it. 10/10 comfy af https://t.co/Pq150LeRaC</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>Meet Al Cabone. He's a gangsta puppa. Rather h*ckin ruthless. Shows no mercy sometimes. 11/10 pet w extreme caution https://t.co/OUwWbEKOUV</td>\n",
       "      <td>Al</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>This is Bo and Ty. Bo eats paper and Ty felt left out. 11/10 for both https://t.co/1acHQS8rvK</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>This is Bo. He emanates happiness. 12/10 I could cut the freedom with a knife https://t.co/c7LNFt39eR</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>This is Bo. He's a Benedoop Cumbersnatch. Seems frustrated with own feet. Portable as hell. 11/10 very solid pupper https://t.co/TONMhRoQh7</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>This is Ed. He's not mad, just disappointed. 10/10 https://t.co/BIljU0zhLN</td>\n",
       "      <td>Ed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>This is JD (stands for \"just dog\"). He's like Airbud but with trading card games instead of sports. 10/10 much skill https://t.co/zzueJV9jCF</td>\n",
       "      <td>JD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2219</th>\n",
       "      <td>This is Jo. Jo is a Swedish Queso. Tongue bigger than face. Tiny lil legs. Still no seatbelt. Simply careless. 8/10 https://t.co/Edy7B5vOp2</td>\n",
       "      <td>Jo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                              text  \\\n",
       "393             This is Mo. No one will push him around in the grocery cart. He's quite pupset about it. 11/10 I volunteer https://t.co/feNwTq12S5   \n",
       "449                                    This is Bo. He was a very good First Doggo. 14/10 would be an absolute honor to pet https://t.co/AdPKrI8BZ1   \n",
       "553                                      This is Bo. He's going to make me cry. 13/10 please get off the bus for him Carly https://t.co/U7FvBZo6Bq   \n",
       "679     This is Bo. He's a West Congolese Bugaboop Snuggle. Rather exotic. Master of the head tilt. 12/10 would pay to pet https://t.co/2jwxxtNzoN   \n",
       "775                          This is O'Malley. That is how he sleeps. Doesn't care what you think about it. 10/10 comfy af https://t.co/Pq150LeRaC   \n",
       "820    Meet Al Cabone. He's a gangsta puppa. Rather h*ckin ruthless. Shows no mercy sometimes. 11/10 pet w extreme caution https://t.co/OUwWbEKOUV   \n",
       "876                                                  This is Bo and Ty. Bo eats paper and Ty felt left out. 11/10 for both https://t.co/1acHQS8rvK   \n",
       "978                                          This is Bo. He emanates happiness. 12/10 I could cut the freedom with a knife https://t.co/c7LNFt39eR   \n",
       "1683   This is Bo. He's a Benedoop Cumbersnatch. Seems frustrated with own feet. Portable as hell. 11/10 very solid pupper https://t.co/TONMhRoQh7   \n",
       "1988                                                                    This is Ed. He's not mad, just disappointed. 10/10 https://t.co/BIljU0zhLN   \n",
       "2041  This is JD (stands for \"just dog\"). He's like Airbud but with trading card games instead of sports. 10/10 much skill https://t.co/zzueJV9jCF   \n",
       "2219   This is Jo. Jo is a Swedish Queso. Tongue bigger than face. Tiny lil legs. Still no seatbelt. Simply careless. 8/10 https://t.co/Edy7B5vOp2   \n",
       "\n",
       "     name  \n",
       "393    Mo  \n",
       "449    Bo  \n",
       "553    Bo  \n",
       "679    Bo  \n",
       "775     O  \n",
       "820    Al  \n",
       "876    Bo  \n",
       "978    Bo  \n",
       "1683   Bo  \n",
       "1988   Ed  \n",
       "2041   JD  \n",
       "2219   Jo  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_arch_clean[tw_arch_clean.name.str.len() < 3][['text', 'name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can notice 2 wrong records here. Fix them manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_arch_clean.loc[775, 'name'] = \"O'Malley\"\n",
    "tw_arch_clean.loc[820, 'name'] = \"Al Cabone\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_arch_clean[(tw_arch_clean.name == 'None')\n",
    "              | tw_arch_clean.name.str.islower()].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>This is Mo. No one will push him around in the grocery cart. He's quite pupset about it. 11/10 I volunteer https://t.co/feNwTq12S5</td>\n",
       "      <td>Mo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>This is Bo. He was a very good First Doggo. 14/10 would be an absolute honor to pet https://t.co/AdPKrI8BZ1</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>This is Bo. He's going to make me cry. 13/10 please get off the bus for him Carly https://t.co/U7FvBZo6Bq</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>This is Bo. He's a West Congolese Bugaboop Snuggle. Rather exotic. Master of the head tilt. 12/10 would pay to pet https://t.co/2jwxxtNzoN</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>This is Bo and Ty. Bo eats paper and Ty felt left out. 11/10 for both https://t.co/1acHQS8rvK</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>This is Bo. He emanates happiness. 12/10 I could cut the freedom with a knife https://t.co/c7LNFt39eR</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>This is Bo. He's a Benedoop Cumbersnatch. Seems frustrated with own feet. Portable as hell. 11/10 very solid pupper https://t.co/TONMhRoQh7</td>\n",
       "      <td>Bo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>This is Ed. He's not mad, just disappointed. 10/10 https://t.co/BIljU0zhLN</td>\n",
       "      <td>Ed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>This is JD (stands for \"just dog\"). He's like Airbud but with trading card games instead of sports. 10/10 much skill https://t.co/zzueJV9jCF</td>\n",
       "      <td>JD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2219</th>\n",
       "      <td>This is Jo. Jo is a Swedish Queso. Tongue bigger than face. Tiny lil legs. Still no seatbelt. Simply careless. 8/10 https://t.co/Edy7B5vOp2</td>\n",
       "      <td>Jo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                              text  \\\n",
       "393             This is Mo. No one will push him around in the grocery cart. He's quite pupset about it. 11/10 I volunteer https://t.co/feNwTq12S5   \n",
       "449                                    This is Bo. He was a very good First Doggo. 14/10 would be an absolute honor to pet https://t.co/AdPKrI8BZ1   \n",
       "553                                      This is Bo. He's going to make me cry. 13/10 please get off the bus for him Carly https://t.co/U7FvBZo6Bq   \n",
       "679     This is Bo. He's a West Congolese Bugaboop Snuggle. Rather exotic. Master of the head tilt. 12/10 would pay to pet https://t.co/2jwxxtNzoN   \n",
       "876                                                  This is Bo and Ty. Bo eats paper and Ty felt left out. 11/10 for both https://t.co/1acHQS8rvK   \n",
       "978                                          This is Bo. He emanates happiness. 12/10 I could cut the freedom with a knife https://t.co/c7LNFt39eR   \n",
       "1683   This is Bo. He's a Benedoop Cumbersnatch. Seems frustrated with own feet. Portable as hell. 11/10 very solid pupper https://t.co/TONMhRoQh7   \n",
       "1988                                                                    This is Ed. He's not mad, just disappointed. 10/10 https://t.co/BIljU0zhLN   \n",
       "2041  This is JD (stands for \"just dog\"). He's like Airbud but with trading card games instead of sports. 10/10 much skill https://t.co/zzueJV9jCF   \n",
       "2219   This is Jo. Jo is a Swedish Queso. Tongue bigger than face. Tiny lil legs. Still no seatbelt. Simply careless. 8/10 https://t.co/Edy7B5vOp2   \n",
       "\n",
       "     name  \n",
       "393    Mo  \n",
       "449    Bo  \n",
       "553    Bo  \n",
       "679    Bo  \n",
       "876    Bo  \n",
       "978    Bo  \n",
       "1683   Bo  \n",
       "1988   Ed  \n",
       "2041   JD  \n",
       "2219   Jo  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_arch_clean[tw_arch_clean.name.str.len() < 3][['text', 'name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixed rows 5\n",
      "Dropped rows 13\n"
     ]
    }
   ],
   "source": [
    "# Get column indexes\n",
    "text_idx = tw_arch_clean.columns.get_loc(\"text\")\n",
    "numerator_idx = tw_arch_clean.columns.get_loc(\"rating_numerator\")\n",
    "denominator_idx = tw_arch_clean.columns.get_loc(\"rating_denominator\")\n",
    "name_idx = tw_arch_clean.columns.get_loc(\"name\")\n",
    "\n",
    "drop_rows = []\n",
    "fixed_rows = 0\n",
    "\n",
    "# Iterate every row\n",
    "for index, row in tw_arch_clean.iterrows():\n",
    "    ratings = re.findall(r\"\\d+\\.?\\d*\\/\\d+\\.?\\d*\", row[text_idx])\n",
    "\n",
    "    rat = []\n",
    "    for r in ratings:\n",
    "        n, d = r.split('/')\n",
    "        if d.strip(' ,.') == '10':\n",
    "            rat.append(float(n))\n",
    "\n",
    "    if len(rat) == 1:\n",
    "        # Check rating\n",
    "        if row[denominator_idx] != 10 or row[numerator_idx] != rat[0]:\n",
    "            fixed_rows += 1\n",
    "            tw_arch_clean.loc[index, 'rating_numerator'] = rat[0]\n",
    "            tw_arch_clean.loc[index, 'rating_denominator'] = 10\n",
    "\n",
    "    # Drop pictures with more than 1 rating\n",
    "    if len(rat) > 1:\n",
    "        drop_rows.append(index)\n",
    "\n",
    "print(\"Fixed rows\", fixed_rows)\n",
    "print(\"Dropped rows\", len(drop_rows))\n",
    "\n",
    "if len(drop_rows):\n",
    "    tw_arch_clean.drop(drop_rows, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrong ratings 0\n"
     ]
    }
   ],
   "source": [
    "wrong_rat = 0\n",
    "# Iterate every row\n",
    "for index, row in tw_arch_clean.iterrows():\n",
    "    ratings = re.findall(r\"\\d+\\.?\\d*\\/\\d+\\.?\\d*\", row[text_idx])\n",
    "\n",
    "    rat = []\n",
    "    for r in ratings:\n",
    "        n, d = r.split('/')\n",
    "        if d.strip(' ,.') == '10':\n",
    "            rat.append(float(n))\n",
    "\n",
    "    if len(rat) == 1:\n",
    "        # Check rating\n",
    "        if row[denominator_idx] != 10 or row[numerator_idx] != rat[0]:\n",
    "            wrong_rat += 1\n",
    "\n",
    "    if len(rat) > 1:\n",
    "        wrong_rat += 1\n",
    "\n",
    "print(\"Wrong ratings\", wrong_rat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n",
    "Now we can drop denumerator column as it has only 10 values there and rename numerator to rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_arch_clean.drop('rating_denominator', axis=1, inplace=True)\n",
    "tw_arch_clean.rename({'rating_numerator':'rating'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1407 entries, 0 to 2325\n",
      "Data columns (total 11 columns):\n",
      " #   Column         Non-Null Count  Dtype              \n",
      "---  ------         --------------  -----              \n",
      " 0   tweet_id       1407 non-null   int64              \n",
      " 1   timestamp      1407 non-null   datetime64[ns, UTC]\n",
      " 2   source         1407 non-null   object             \n",
      " 3   text           1407 non-null   object             \n",
      " 4   expanded_urls  1407 non-null   object             \n",
      " 5   rating         1407 non-null   float64            \n",
      " 6   name           1407 non-null   object             \n",
      " 7   doggo          1407 non-null   object             \n",
      " 8   floofer        1407 non-null   object             \n",
      " 9   pupper         1407 non-null   object             \n",
      " 10  puppo          1407 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(1), int64(1), object(8)\n",
      "memory usage: 131.9+ KB\n"
     ]
    }
   ],
   "source": [
    "tw_arch_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Format p1, p2, p3 values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_pred_clean['p1'] = tw_pred_clean['p1'].str.replace('_', ' ')\n",
    "tw_pred_clean['p2'] = tw_pred_clean['p2'].str.replace('_', ' ')\n",
    "tw_pred_clean['p3'] = tw_pred_clean['p3'].str.replace('_', ' ')\n",
    "tw_pred_clean['p1'] = tw_pred_clean['p1'].str.title()\n",
    "tw_pred_clean['p2'] = tw_pred_clean['p2'].str.title()\n",
    "tw_pred_clean['p3'] = tw_pred_clean['p3'].str.title()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 0, 0, 0, 0)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_pred_clean['p1'].str.contains('_').sum(), \\\n",
    "tw_pred_clean['p2'].str.contains('_').sum(), \\\n",
    "tw_pred_clean['p3'].str.contains('_').sum(), \\\n",
    "tw_pred_clean['p1'].str.islower().sum(), \\\n",
    "tw_pred_clean['p2'].str.islower().sum(), \\\n",
    "tw_pred_clean['p3'].str.islower().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image URL duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_pred_clean.drop_duplicates(subset=\"jpg_url\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_pred_clean.jpg_url.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to dummy\n",
    "for st in ['doggo', 'floofer', 'pupper', 'puppo']:\n",
    "    tw_arch_clean[st].replace('None', 0, inplace=True)\n",
    "    tw_arch_clean[st].replace(st, 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>text</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>460</th>\n",
       "      <td>This is Dido. She's playing the lead role in \"Pupper Stops to Catch Snow Before Resuming Shadow Box with Dried Apple.\" 13/10 (IG: didodoggo) https...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>This is Bones. He's being haunted by another doggo of roughly the same size. 12/10 deep breaths pupper everything's fine https://t.co/55Dqe0SJNj</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>This is Pinot. He's a sophisticated doggo. You can tell by the hat. Also pointier than your average pupper. Still 10/10 would pet cautiously https...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>Meet Maggie &amp;amp; Lila. Maggie is the doggo, Lila is the pupper. They are sisters. Both 12/10 would pet at the same time https://t.co/MYwR4DQKll</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                      text  \\\n",
       "460  This is Dido. She's playing the lead role in \"Pupper Stops to Catch Snow Before Resuming Shadow Box with Dried Apple.\" 13/10 (IG: didodoggo) https...   \n",
       "575       This is Bones. He's being haunted by another doggo of roughly the same size. 12/10 deep breaths pupper everything's fine https://t.co/55Dqe0SJNj   \n",
       "705  This is Pinot. He's a sophisticated doggo. You can tell by the hat. Also pointier than your average pupper. Still 10/10 would pet cautiously https...   \n",
       "889       Meet Maggie &amp; Lila. Maggie is the doggo, Lila is the pupper. They are sisters. Both 12/10 would pet at the same time https://t.co/MYwR4DQKll   \n",
       "\n",
       "     doggo  floofer  pupper  puppo  \n",
       "460      1        0       1      0  \n",
       "575      1        0       1      0  \n",
       "705      1        0       1      0  \n",
       "889      1        0       1      0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find wrong values (more than 1 in a row)\n",
    "tw_arch_clean[tw_arch_clean.doggo + tw_arch_clean.floofer +\n",
    "              tw_arch_clean.pupper + tw_arch_clean.puppo > 1][[\n",
    "                  'text', 'doggo', 'floofer', 'pupper', 'puppo'\n",
    "              ]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fix errors manualy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_arch_clean.loc[460, 'doggo'] = 0\n",
    "tw_arch_clean.loc[575, 'doggo'] = 0\n",
    "tw_arch_clean.loc[705, 'pupper'] = 0\n",
    "#drop row with two dogs\n",
    "tw_arch_clean.drop(889, inplace=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 5, 121, 17)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view numbers before transform\n",
    "tw_arch_clean['doggo'].sum(), \\\n",
    "tw_arch_clean['floofer'].sum(), \\\n",
    "tw_arch_clean['pupper'].sum(), \\\n",
    "tw_arch_clean['puppo'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a technique from here https://stackoverflow.com/questions/50332270/most-efficient-way-to-un-dummy-variables-in-pandas-df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add dummy column. It's needed for None cases\n",
    "tw_arch_clean['None'] = 0.5\n",
    "\n",
    "\n",
    "def undummy(d):\n",
    "    return d.columns[d.values.argmax(1)]\n",
    "\n",
    "\n",
    "tw_arch_clean = tw_arch_clean.assign(stage=tw_arch_clean[\n",
    "    ['doggo', 'floofer', 'pupper', 'puppo', 'None']].pipe(undummy))\n",
    "\n",
    "tw_arch_clean['stage'].replace('None', np.nan, inplace=True)\n",
    "\n",
    "# Drop unneeded columns\n",
    "tw_arch_clean.drop(['doggo', 'floofer', 'pupper', 'puppo', 'None'],\n",
    "                   axis=1,\n",
    "                   inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper     121\n",
       "doggo       39\n",
       "puppo       17\n",
       "floofer      5\n",
       "Name: stage, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_arch_clean.stage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1406 entries, 0 to 2325\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count  Dtype              \n",
      "---  ------         --------------  -----              \n",
      " 0   tweet_id       1406 non-null   int64              \n",
      " 1   timestamp      1406 non-null   datetime64[ns, UTC]\n",
      " 2   source         1406 non-null   object             \n",
      " 3   text           1406 non-null   object             \n",
      " 4   expanded_urls  1406 non-null   object             \n",
      " 5   rating         1406 non-null   float64            \n",
      " 6   name           1406 non-null   object             \n",
      " 7   stage          182 non-null    object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(1), int64(1), object(5)\n",
      "memory usage: 98.9+ KB\n"
     ]
    }
   ],
   "source": [
    "tw_arch_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural network predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create dummy columns\n",
    "tw_pred_clean['breed'] = 'None'\n",
    "tw_pred_clean['conf'] = 0.0\n",
    "\n",
    "\n",
    "def get_breed(row):\n",
    "    breed = np.nan\n",
    "    conf = 0\n",
    "    for i in range(1, 4):\n",
    "        if row['p{}_dog'.format(i)]:\n",
    "            if row['p{}_conf'.format(i)] > conf:\n",
    "                conf = row['p{}_conf'.format(i)]\n",
    "                breed = row['p{}'.format(i)]\n",
    "    row['breed'] = breed\n",
    "    row['conf'] = conf\n",
    "    return row\n",
    "\n",
    "\n",
    "tw_pred_clean[['breed',\n",
    "               'conf']] = tw_pred_clean.apply(get_breed,\n",
    "                                              axis=1)[['breed', 'conf']]\n",
    "\n",
    "# Drop unneeded columns\n",
    "for i in range(1, 4):\n",
    "    a = 'p{}'.format(i)\n",
    "    b = 'p{}_dog'.format(i)\n",
    "    c = 'p{}_conf'.format(i)\n",
    "    tw_pred_clean.drop([a, b, c], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Golden Retriever      158\n",
       "Labrador Retriever    108\n",
       "Pembroke               95\n",
       "Chihuahua              91\n",
       "Pug                    63\n",
       "                     ... \n",
       "Entlebucher             1\n",
       "Scotch Terrier          1\n",
       "Standard Schnauzer      1\n",
       "Clumber                 1\n",
       "Japanese Spaniel        1\n",
       "Name: breed, Length: 113, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw_pred_clean.breed.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2009 entries, 0 to 2074\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2009 non-null   int64  \n",
      " 1   jpg_url   2009 non-null   object \n",
      " 2   img_num   2009 non-null   int64  \n",
      " 3   breed     1691 non-null   object \n",
      " 4   conf      2009 non-null   float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 94.2+ KB\n"
     ]
    }
   ],
   "source": [
    "tw_pred_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join to a single Master DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns\n",
    "df_tw_clean.rename({'id': 'tweet_id'}, axis=1, inplace=True)\n",
    "\n",
    "master_df = pd.merge(left=tw_arch_clean,\n",
    "                     right=df_tw_clean,\n",
    "                     left_on='tweet_id',\n",
    "                     right_on='tweet_id',\n",
    "                     how='inner')\n",
    "\n",
    "master_df = pd.merge(left=master_df,\n",
    "                     right=tw_pred_clean,\n",
    "                     left_on='tweet_id',\n",
    "                     right_on='tweet_id',\n",
    "                     how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1357 entries, 0 to 1356\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count  Dtype              \n",
      "---  ------          --------------  -----              \n",
      " 0   tweet_id        1357 non-null   int64              \n",
      " 1   timestamp       1357 non-null   datetime64[ns, UTC]\n",
      " 2   source          1357 non-null   object             \n",
      " 3   text            1357 non-null   object             \n",
      " 4   expanded_urls   1357 non-null   object             \n",
      " 5   rating          1357 non-null   float64            \n",
      " 6   name            1357 non-null   object             \n",
      " 7   stage           174 non-null    object             \n",
      " 8   favorite_count  1357 non-null   int64              \n",
      " 9   retweet_count   1357 non-null   int64              \n",
      " 10  jpg_url         1357 non-null   object             \n",
      " 11  img_num         1357 non-null   int64              \n",
      " 12  breed           1190 non-null   object             \n",
      " 13  conf            1357 non-null   float64            \n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(4), object(7)\n",
      "memory usage: 159.0+ KB\n"
     ]
    }
   ],
   "source": [
    "master_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Store the clean DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df.to_csv('data/twitter_archive_master.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze and visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load our data from file into separate DataFrame\n",
    "df = pd.read_csv('data/twitter_archive_master.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between favorites and retweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 430
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(color_codes=True)\n",
    "rp = sns.regplot(x=\"favorite_count\", y=\"retweet_count\", data=df)\n",
    "plt.title(\"Relationship between favorites and retweets\")\n",
    "plt.xlabel('Favorites')\n",
    "plt.ylabel('Retweets')\n",
    "rp.get_figure().savefig('img/fav_retweet_regplot.png', dpi=200, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9256212882997505"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['favorite_count'].corr(df['retweet_count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most popular breed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_barh(df, title, xlabel, filename):\n",
    "    ax = df.plot(kind='barh')\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(None)\n",
    "    plt.savefig(filename, dpi=200, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 463
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_barh(\n",
    "    df.groupby('breed').favorite_count.mean().sort_values().tail(10),\n",
    "    'Top 10 breeds by favorites', 'Favorites', 'img/top10_breed_fav.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 463
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_barh(\n",
    "    df.groupby('breed').retweet_count.mean().sort_values().tail(10),\n",
    "    'Top 10 breeds by retweets', 'Retweets', 'img/top10_breed_retweet.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 470
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_barh(\n",
    "    df.groupby('breed').rating.mean().sort_values().tail(10),\n",
    "    'Top 10 breeds by rating', 'Rating', 'img/top10_breed_rat.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Network Confidence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 486
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_barh(\n",
    "    df.groupby('breed').conf.mean().sort_values().tail(10),\n",
    "    'Top 10 breeds by neural network confidence', 'Neural network confidence',\n",
    "    'img/top10_breed_conf.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PEPPWBs+xpmPHjub333/3Wren/EMPPVTq9uP8xBgO4BxQo0YNPfDAA5JO3Rdf2L3Zp4uLi1NycrIaNmyoLl26eF2nUaNGuvrqq5WTk6O4uLhybbN0aoae0+9TLolGjRqpSZMmOnHihH788cdybtmpW9W83e7gGW/hmX3IY926dcrIyFDr1q3VsmVLr+8ZERGhRo0aKS0trdAxA4UJCgpSixYtJOW/hcrzubRp00aRkZEyxji3UJ2+/MzbqdauXStJzjiGM9WtW1f9+vWTJMXGxnptk5+fnwYOHFis9mdmZmr48OH6+OOP1aBBA73//vtq2rRpscqebuXKlTLG6C9/+YvuvvvuYpXZtGmT/vjjDwUEBOjBBx8ssNzPz88ZO5SQkFDo2JWS7hNlsX79euXl5alevXq64447CiyvVauW7rnnHq9l9+7dq2+++UbBwcHOZ3imsLAw53P33P5ypoEDByowMLDA6507d5ZUcHu/+uor/frrr/Lz89OTTz5Z+MadJjMz09kXBw0a5HWdwMBAde3atci2ns1DDz3k9bN7+OGHJUl79uzRTz/95Lx+wQUXqGPHjpKkDz/8sEC5jz76SJJ0yy23ODNMFUdQUJDmz5+vv/71r5JOzVr1ySefaNKkSRowYICioqL08MMP69tvvy32e5ZUZGSkatasqeTkZB06dMh5PS8vzxlL1b9/f6+f/f3331/orVye8SW9evVSrVq1vK7TvXt3SafGpJxtHAn+XBjDAZwjoqOjNX/+fB05ckQLFizQ0KFDi1z/m2++kSQdPnxY7du3L3S99PR0SSp0EG5ZFDU2wGPTpk1atmyZtm3bppSUFK8DxQ8fPlzubfOc3J+pfv36kk6dKJzOc4Kwbdu2IvszLS1N0qn+bNWqVYnaFBUVpS1btiguLs452faEjzZt2ujQoUNasGCB4uLinOeynL78dJ5xG0XN5d+uXTvNnj1bu3fvVkZGRoETjUsuuUR16tQ5a7uPHz+uIUOGKC4uTo0bN9Y777yjiy66qJhbnZ9nPMONN95Y7DKecNe0adNCH7gXFRUlf39/5eTk6IcffvD6/iXdJ8rC0+bIyMhCx8WcGSI9PN/t7OzsfBMSnMnzw0Rh3+3CtrdevXqSCm6v57Np2rSp0ydn8/333zsTUxQ1/szzvS/NcSggIECtW7f2uqxx48YKDw9XSkqKtm/friuvvNJZdtddd+mTTz7R6tWr9eSTTzon4MeOHXNOzO+6664St6d+/fqaPXu2du3apX//+9/asmWLtm/frpSUFGVnZ+uzzz7T559/rvHjx2vAgAElfn+PtWvXatWqVfrhhx909OhRZyzP6Q4fPux8Vvv27XMmA7jmmmu8vme1atXUrFmzfD96eHiOgfPmzdOiRYuKbNuJEyeUmpqqCy64oETbhPMXgQM4R1SrVk1Dhw7VSy+9pH/961+699578w1SPZPnV9zs7GwdOXLkrO9vY4rGs52segYkewQEBKhWrVrOQMe0tDRlZ2frxIkT5d62wgZGBgUFSVKBGcE8/ZmZmVmsvipNf0ZFRenNN990/tgbYxQfH6+QkBA1a9ZMjRo1ko+Pj7M8KytL27Ztc8qe7ujRo5JU5ImhZ5kxRr///nuBwFGcsCGdGrwsSf7+/nr77bdLHTYkOftqgwYNil2mONsaFBSkWrVq6ciRI876ZyrpPlEWnjZ4Tu69KWx7PPtibm5umb7bhf1yX9j2luazOf3HAlvHoVq1ann9td6jfv36SklJKfC533DDDWrQoIEOHjyozz77zLnKsnr1amVlZemyyy4r9MS8OC6//HJdfvnlzr937dqlNWvW6F//+pdOnDihV155RZGRkWrWrFmJ3jcnJ0ejRo3S+vXrndcCAwNVu3ZtZ7KCo0ePKi8vL9+x8/QZuYra7wpb5tnv0tPTnR+qimLjuI1zF4EDOIf069dPc+fO1cGDBzV37lyNHDmy0HU9M5B06dLFecp1RStqRqP//Oc/WrBggXO7S48ePZwTao97771XW7ZsKTDVbGXw9OfAgQM1fvx4K3W0bt1a/v7+OnTokPbu3avMzEylpqbq+uuvl7+/v+rUqaMmTZpox44dSktL008//aSTJ0+qbt26uuyyy7y+59mm+C1KcWZakk6FnT179ujw4cOaMGGC5syZU+pb6cryWZdlW6uiwvrC8/pVV13l3OZSme0pTpmwsDArt22WpA1n8vX1Ve/evTV9+nR99NFHTuBYtmyZpFO3DpWnyy+/XI899pjatWun6Oho5eXlafny5SUOHB988IHWr1+vatWq6fHHH9fNN99cYAreG2+8Ub/++mu+bS/rcdRzDJw5c2aRV9YAbxjDAZxDAgMDnXvR58+fX+gvtdL/n361uPP5n85zountEr1HcX7hKornvu677rpLw4cP1yWXXFLgHuzffvutTHWUp7L0Z3F5rmRIp8ZmeK5knH5bVFRUlDOOw7Pc2603nqsTBw4cKLQ+z/3dPj4+ql27dqnbffHFF2vevHmqW7euvv76aw0bNqzUJ//h4eGSim73mYqzrVlZWc40osW9cmOTpw1F3S5Y2FgTz20qu3fvLterLmdTms/G09a0tLRCt6esUlNTi9zfPPV6+9x79+4tX19fbdy4UYcPH9aOHTu0fft2+fn5eR1bUx6ioqJ06aWXSjr1GZaU59j56KOPqn///gXCRm5urtfni5y+/aXZ7zzHwF27dpW4zQCBAzjH9OrVS5dccomOHz+ut956q9D1POMnfvnllxIPdq1Zs6akUyf8hf0h/+6770r0nmfynOxeddVVXpcnJydrz549ZaqjPHn6Mz4+vsiHhXlz+pWes/3KGBkZ6dTjbUC45/8LW+7h6df4+PhC69y8ebOkU/e5l/VJ2ZdffrnmzZun2rVr68svv9Rjjz1WqtBx9dVXS5K++OKLYpfxhLQ9e/bkGyR7uvj4eOfkvLB9riJ52lzUFTxv99FL/39fzMjI0KZNm+w00AvPZ7Njx45C+/lMzZs3d26R9Nx6V96ys7O1detWr8s8V94keb2ScNFFF+m6665Tbm6uVqxY4Vzd6NChQ5G3HZWV5/t25nNOPD+6FHWc8PT96eNRTvfNN994/bGoUaNGzm2DW7Zs8Vo2MzOz0Ac3eva7devWFdo2oDAEDuAc4+/vr+HDh0uSFi5cWOgvVddee61zL/3pD+nyxjPQ2aNx48YKDAyUMUafffZZgfX37NlT5pMHzx8+z5N5z/TGG29UiVupPG655RaFhIQoKytLr732WpHrntmfp48NONvAY8/g77i4OCUkJCgkJETNmzd3lnvCxaZNm5xBvGcOGPe0Vzo109C///3vAsuPHDmixYsXS5JuvfXWIttUXFdccYXeeecdhYWF6fPPP9fo0aNL/At8z5495ePjo//9739O+86mffv2qlGjhrKzs/M9lNEjNzfXeWhfZGSk80t9ZerSpYt8fX116NAhrVy5ssDytLS0Qrf/8ssvd07+Jk2aVOSsdZmZmeV2q9m1116r+vXrKzc396zfAY8aNWo4ExzMmjWryHEcOTk5On78eKnaNnv2bK/Hi9mzZ0s6dUwrbNY0z2D2ZcuWOU8mL81gcenUpBJnu/r7888/OzNmnRkaPMeKoo4TRR07c3JyNGXKFK/lfH19nVuh3n33XWcw/+kWLlxY6P505513Sjo1EYDnAZuFOfMYCBA4gHNQ9+7d1aRJE2VmZurrr7/2uk5AQICefvpp+fj4aNOmTRo8eLASExOdP8o5OTn6/vvvNWnSJGcaTI/AwEDnD5Pn6bN5eXnKy8vTl19+qUGDBpX6Hn0Pz0xPS5Ys0YcffuicFB04cEBjx47Vxx9/XOiMQ5Whdu3aevzxxyWdmjJz5MiR+f7gZ2VlKSEhQc8991yB6Uxr1qzp/FrqmW6zMNdcc418fX114MABHTlyRK1atcr3K2h4eLgaN26spKQkZWZmqnbt2mrSpEmB94mMjNQNN9wgSXrqqae0du1aJ3R+//33Gjx4sNLS0lS3bl3179+/FD3i3ZVXXqm5c+cqNDRUGzZs0JgxY0o0PeYVV1zhzND1/PPPa9q0ac6tdbm5udq9e7emTZuWb5ackJAQZ9a2BQsWaNasWc6J66FDh/T4449ry5Yt8vX11ahRo8prU8ukYcOG6t27tyRp4sSJWrFihXMCuGPHDj344INF3tL49NNPKzAwUElJSbrvvvv03//+1wl3eXl5+vnnnzVz5kx17ty53GZ5CwgI0Lhx4yRJMTExGjlyZL7baw4fPqwPPvhAL774Yr5yTzzxhGrVqqWUlBT169dP69evzxeC9uzZo3nz5qlbt26F/rpelGrVqunrr7/WU0895ewrx44d0+uvv+5csRg+fHihT/2+6aabdMEFF2j37t06evSoLrjgghLNkna62NhYdezYUX/729/03//+15kVSjo1aHvhwoUaNGiQ8vLyFBISoj59+uQrf8UVV0iSc4uXN55j58yZM7Vhwwbn+7Vr1y49/PDD2rZtW6FXLIcOHaqAgAAlJSXpscceU3JysqRTx6/3339fb7zxhnOF+0wdOnRwwuNTTz2lqVOn5mtjWlqaNmzYoEceeUSvvPLKWfsKfy4MGgfOQb6+vhoxYoRGjBhR5HqdOnXSSy+9pL/97W/avHmz+vbtq6CgIFWrVk3p6elFngg+8cQT+uqrr3Tw4EHdd999qlatmvLy8pSVlaUrr7xSAwcO1EsvvVTqbbjzzjv10UcfaevWrZowYYKeffZZVa9e3fllb8SIEdq8eXOlDTT1Jjo6Wunp6Zo6darWrl2rtWvXqlq1agoMDFR6erozqLJhw4YFyvbp00czZszQK6+8oqlTpzpjJvr375/vWRehoaFq2rSpM62tt6sXbdq0ce79joqKKvRE6rXXXtPgwYP1448/auTIkQoKCpK/v79zMh4WFqbp06eXafyGN82bN9fbb7+twYMHKzY2VgEBAXr11VeLnETgdBMmTFBaWppiY2M1ffp0TZ8+XTVr1lRGRoZzUu25yufxwAMPaNeuXVqxYoWmTJmiadOmqUaNGjp27JiMMfL19dXTTz9d6FSzlWH8+PFKSkpSYmKixo4dq2eeeUZBQUFKT09XSEiIXnjhBT3xxBNey7Zo0ULTp0/XE088oR9++EGDBg1SQECAqlevruPHj+f79bqw/aM0unXrpkOHDum1115zvgMhISHKy8tzZpg6c5+9+OKL9dZbb2nYsGHat2+fhg8fLn9/f9WoUUMZGRn5wkdp2lqnTh31799fL7/8spYvX66aNWvm+z7ed999zvMhvAkICFDPnj01d+5cSaeusp15q1Nx+fv7Kz09XYsXL3auUNWoUUO5ubn5Zm2qWbOm3njjjQIzfnXu3FmTJ0/W7t27deONN+qCCy5wZuD69NNPJcn5Xu3du1fDhg1TQECAgoKC9Mcff8jPz08vvviipk+f7vVKxeWXX67nnntOEyZM0GeffabPPvtMYWFhysjIUHZ2tm655RYFBwdrxYoVXmf+evXVV53necyYMUMzZsxQaGiojDH5wlV5D7jHuY8rHMA56uabby7W7Ca9e/fW2rVrNWDAAF1xxRXy8/PTH3/8oVq1aqlt27YaO3as84fsdI0aNdLSpUt1++23q06dOsrNzdWFF16ohx9+WIsWLSp0CtHiCgwM1DvvvKOHHnpIjRo1kq+vr/z8/NS+fXu9+eabGjZsWJne35ZHH31UK1eu1N13363GjRvLGKOMjAyFh4erQ4cOmjhxopYuXVqg3LBhwzRmzBi53W4ZY5ScnKzk5GSvt194G7NR2HLPmA9v6tSpoyVLlmjcuHHOvfTZ2dlq3LixBgwYoJiYmBI/K6S4IiIiNGfOHIWEhGjVqlV6+umni32LXGBgoKZMmaKZM2eqY8eOqlu3rk6cOKHatWsrIiJCo0ePLvBMBz8/P7366quaOnWqrr/+eoWGhjqfy+23366lS5fqvvvus7GppVa9enUtWLBAI0aMUOPGjSWdmpK2W7duWrp06VmfY3PjjTdq3bp1euSRR9SsWTMnrFSvXl2tWrXSiBEjtGbNGq8BuCwGDRqk5cuXq1evXmrYsKFycnIUFBQkt9ut/v37e+NH26YAACAASURBVJ3FrWXLloqNjdWYMWPUqlUrVa9eXenp6QoODlbz5s01ZMgQffjhh14DdnEMHDhQs2bNUlRUlPLy8hQUFKSIiAi9/vrrevbZZ89a3vPLvSTnylNpPP7441q4cKEeeeQR5xa0rKws5eTkqE6dOoqKitLo0aO1bt065wrk6erUqaP58+fr5ptvVp06dXT06FHnWOFRq1YtLVmyRPfcc48zYDw4OFidO3fWggULznqy37t3b7333nu64YYbFBoaqpMnT+ryyy/XhAkT9I9//MM5Jnmbdj0kJEQzZszQ7NmzdfPNN6t+/frKzMxUdna2Lr30Ut1+++2aOnWq/va3v5W6D3F+8jFV6SZpAACACjZr1ixNmTJFV199tT744IPKbk6lMcaoY8eOOnjwoN59990iHxwKlARXOAAAwJ9Wbm6uc1WyqKeh/xl8/PHHOnjwoGrUqKGWLVtWdnNwHmEMBwAA+FMyxmjGjBlKTk5W3bp1dfvtt1d2k6x78803Vb16dXXu3Fn169eXr6+v0tLStGLFCr3xxhuSTj10tVq1apXcUpxPCBwAAOBPZevWrXr88ceVlpbmDHYePXp0mWffOxfs3LlTq1ev1osvvqiAgACFhIQ4kytI0nXXXVdgUgagrAgcAADgTyUrK0vJyckKCAjQX/7yFw0aNKjUz94419x7772qUaOGtmzZopSUFKWnpyssLExut1s9evTQHXfc4TysESgvDBoHAAAAYA2DxgEAAABYQ+AAAAAAYA2BAwAAAIA1BA4AAAAA1hA4AAAAAFhD4AAAAABgDYEDAAAAgDU82eU8kJdnlJOTW9nNOK8FBp76qpw8mVPJLTm/0c8Vh76uOPR1xaCfKw59XTGqWj/7+/vJ19endGXLuS2oBDk5uUpLO1HZzTivhYeHShL9bBn9XHHo64pDX1cM+rni0NcVo6r1c1hYNScElRS3VAEAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAa3yMMaayGwEAAADg7FJS0iul3rCwagoM9C9VWa5wAAAAALCmdDEFVcp3O4/oqVmbKrsZAAAAsGT15J6V3YRS4woHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArPGvqIrcbrckaceOHVqyZIkWLVqkX375RcHBwYqKitKIESPkcrnyldm/f786deqkhg0b6tNPPz3r+5a1vrKUk6Tk5GS99dZb2rhxow4dOqRq1aqpadOm6tu3r7p3716C3gIAAADODxV+hePvf/+7Jk6cqNDQUHXq1Em1atXS+vXr1bdvXyUkJFSZ+kpaLjExUXfccYcWLVokSerSpYuaN2+ub7/9VmPGjNGTTz4pY0y5bx8AAABQlVXYFQ6PDz74QO+++66ioqIkScYYvfHGG5ozZ47GjBmjdevWKSgoqNLrK0m5rKwsjRw5UseOHdOAAQM0duxY+fn5SZKSkpI0cOBArVy5Uq1bt1a/fv3KbdsAAADw5xIeHlrZTSixCr/Ccc899zgn8ZLk4+OjUaNGqVGjRjp48KDWrVtXJeorSbnY2FgdPHhQDRs21P/93/85YUOSXC6Xhg8fLkmaO3duuW4bAAAAUNVVeODo0aNHgdf8/Px02223SZLi4uKqRH0lKRcfHy9J6t69uwICAgqU6927t3x8fLRnzx4dOnSo5BsBAAAASEpJSa+U/06ezCl1mys8cFx88cVFvv7rr79WifpKUs4TIgorExQUpHr16uVbFwAAAPgzqHLT4vr4+BR73by8vAqtr7ByxRkMzoBxAAAA/BlVeOBITk72+vr+/fslybkSIMm5Pen48eMleq/S1lfachdeeGG+ZWfKyspSSkqKJKl+/fpnbTMAAABwvqjwwLFq1aoCr+Xm5io2NlaS1KZNG+f1OnXqKCAgQKmpqTp69GiBcl988UW51lfacp7B5TExMcrJKXh/2/Lly2WM0aWXXkrgAAAAwJ9KhQeORYsW5XuOhTFGU6dO1Z49e1S/fn117drVWRYQEKDIyEhJ0j//+c98tyUlJCRo6tSp5VpfacvdeuutatCggfbv36/Jkyfnu9Vr586dmjZtmiRp8ODBZ20vAAAAcD6p8Odw9OnTR9HR0YqMjFS9evW0fft250nekyZNUnBwcL71R4wYoYSEBC1evFjx8fFq0qSJDhw4oO3bt2vo0KGaNWtWudZXmnJBQUGaMmWKhgwZorlz52rDhg1q0aKF0tLS9PXXXys7O1s9e/bU3XffXfYOBAAAAM4hPqaCRjO73W5J0k8//aSFCxdqyZIl2rNnj4KCghQVFaURI0Y465wpISFB06dPV2JioowxatKkifr3768ePXo4ZXbs2FEu9ZWlncnJyZozZ442btyow4cPKzg4WE2bNlXfvn3VvXv3Ug9QP5vvdh7RU7M2WXlvAAAAVL7Vk3tKOjUtbmUIC6umwMDSXauo8MBxZjCoavVVdDvLA4EDAADg/HYuB44qNy0uAAAAgPMHgQMAAACANQQOAAAAANZU2CxVFT0morT1nUtjNwAAAICqjiscAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAa3yMMaayGwEAAADg7FJS0iul3rCwagoM9C9VWa5wAAAAALCmdDEFVcrJkzlKSztR2c04r4WHh0qqvF8V/izo54pDX1cc+rpi0M8Vh76uGOdTP3OFAwAAAIA1BA4AAAAA1hA4AAAAAFhD4AAAAABgDYEDAAAAgDUEDgAAAADWEDgAAAAAWEPgAAAAAGANgQMAAACANQQOAAAAANYQOAAAAABYQ+AAAAAAYA2BAwAAAIA1BA4AAAAA1hA4AAAAAFhD4AAAAABgDYEDAAAAgDUEDgAAAADWEDgAAAAAWEPgAAAAAGANgQMAAACANQQOAAAAANYQOAAAAABYQ+AAAAAAYA2BAwAAAIA1BA4AAAAA1hA4AAAAAFhD4AAAAABgDYEDAAAAgDUEDgAAAADWEDgAAAAAWEPgAAAAAGANgQMAAACANQQOAAAAANYQOAAAAABYQ+AAAAAAYA2BAwAAAIA1BA4AAAAA1hA4AAAAAFhD4AAAAABgDYEDAAAAgDUEDgAAAADWEDgAAAAAWEPgAAAAAGANgQMAAACANQQOAAAAANYQOAAAAABYQ+AAAAAAYA2BAwAAAIA1BA4AAAAA1hA4AAAAAFhD4AAAAABgjY8xxlR2IwAAAFA5UlLSS7R+eHhoqcqhZKpaP4eFVVNgoH+pynKFAwAAAIA1pYspqFK+23lET83aVNnNAAAA55DVk3tWdhPwJ8EVDgAAAADWEDgAAAAAWEPgAAAAAGANgQMAAACANQQOAAAAANYQOAAAAABYQ+AAAAAAYA2BAwAAAIA1BA4AAAAA1hA4AAAAAFhD4AAAAABgDYEDAAAAgDUEDgAAAADWEDgAAAAAWEPgAAAAAGANgQMAAACANQQOAAAAANZUSODYsGGD+vXrp9atW8vtdsvtduvHH3+U2+3WTTfdVBFNKFX7AAAAAJSNv+0KfvjhB40cOVKS1K5dO4WHh0uS/vjjD9tVF0th7QsLC6vMZgEAAADnBeuBY8OGDcrJydHDDz+s0aNHO6/v37/fdtXFUlj7AAAAAJSd9VuqDh48KEm69NJLbVdVKlW9fQAAAMC5zFrgmDZtmtxutz766CNJ0vjx453xEePGjTtr+eTkZE2cOFGdOnVS8+bNFRUVpejoaK1evbrQMsYYrVixQtHR0YqKilKLFi3UuXNnPffcc06wKE37Dh48qBdffFFdu3ZVy5Yt1bp1a/Xr108fffSRjDEF2hEdHS23262vv/5a8fHxeuihh9S2bVs1bdpUGzZsOOu2AwAAAOcLa7dUXXnllbrzzju1ZcsW7d27V61bt3auIlxzzTVFlk1MTNSDDz6oY8eO6eKLL1aXLl2Umpqq+Ph4xcXFaePGjXr11Vfl4+PjlDHGaMyYMYqJiVFAQIDatGmjsLAwbdu2TQsXLtSaNWv01ltvqWXLliVq3+bNmzV8+HClp6fr0ksv1Q033KCMjAxt3bpV48eP1+bNm/Xaa6953Y61a9dq8eLFatKkidq3b6/ff/9d/v7W72IDAAAAqgxrZ7+dO3dW586dNW7cOO3du1d9+vRRr169nOWFjeHIysrSyJEjdezYMQ0YMEBjx46Vn5+fJCkpKUkDBw7UypUrnasMHgsXLlRMTIzq1q2refPm6YorrpAk5ebm6uWXX9aCBQs0atQorV27VoGBgWdtnyQdPnxYI0aMUEZGhl555RXdcccdTsg5ePCgHnnkEa1cuVLt2rUrUNbTpueff15333132ToTAAAAOEdVuZ/bY2NjdfDgQTVs2FD/93//54QNSXK5XBo+fLiee+45zZ07N1/geOeddyRJI0eOdMKGJPn5+enJJ5/Uhg0blJycrLVr16pHjx7Fasv8+fOVlpamBx98UHfeeWe+ZQ0aNNALL7ygu+66S++9957XwNG+fXvCBgAAqNLCw0MrtBxK5nzo5yr34L/4+HhJUvfu3RUQEFBgee/eveXj46M9e/bo0KFDkqRff/1V+/btk6+vr3r27FmgTGBgoLp37y5JiouLK3ZbvvjiC0nSLbfc4nV58+bNFRISoh9//FFZWVkFlnfp0qXYdQEAAADnoyp3hcMTIi6++GKvy4OCglSvXj0dOnRIhw4dUv369Z0y4eHhCgoK8lrukksuyff+xbFv3z5J0l133XXWdVNTU1W/fv18r1100UXFrgsAAKAypKSkl2h9zy/uJS2Hkqlq/RwWVk2BgaWLDlUucHib9els63j+ffog8tK875lyc3MlSd26dSs0yHh4uxoTHBxc4joBAACA80mVCxwXXnihpKIHlaekpEiSc0XBU+bw4cM6efKkAgMDC5TzvN+ZVyGK0qBBA+3Zs0ePPvpovnEhAAAAAIqnyo3hiIqKkiTFxMQoJyenwPLly5fLGKNLL700X+Bo1KiR8vLytHLlygJlsrOzFRMTI0lq06ZNsdvSoUMHSaemtwUAAABQclUucNx6661q0KCB9u/fr8mTJysvL89ZtnPnTk2bNk2SNHjw4HzlBg0aJEn65z//qV27djmv5+bm6vXXX1dycrIaNmxY6ABwbx544AHVqFFDs2fP1vvvv+81AG3dulWxsbEl2kYAAADgz6LK3VIVFBSkKVOmaMiQIZo7d642bNigFi1aKC0tTV9//bWys7PVs2fPAtPN3nvvvfrmm28UExOjnj17qm3bts6D//bt26ewsDBNmTLF6+1WhWnQoIFmzJihkSNH6vnnn9esWbN0xRVXqFatWjp8+LD27t2rw4cPq1u3brr11lvLuysAAACAc16VCxySFBERoRUrVmjOnDnauHGjPvnkEwUHBysiIkJ9+/ZV9+7dCwwQ9/Hx0aRJk3TDDTdo6dKlSkxMVGZmpurVq6d77rlHQ4cOVYMGDUrclnbt2ikmJkbvvfeePv/8c23dulU5OTkKDw9X48aNFR0dXaKrJgAAAMCfiY8pzfRNqFK+23lET83aVNnNAAAA55DVk089u4xpcaumqtbPZZkWt8qN4QAAAABw/iBwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACs8THGmMpuBAAAACpHSkp6idYPDw8tVTmUTFXr57CwagoM9C9VWa5wAAAAALCmdDEFVcrJkzlKSztR2c04r1W1XxnOV/RzxaGvKw59XTHoZ6Dq4goHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsMa/shuAsgsM9Fd4eGhlN+NPgX6uGPRzxaGvKw59XXIpKemV3QQA5YArHAAAAACs4QrHeeC7nUf01KxNld0MAADKxerJPSu7CQDKEVc4AAAAAFhD4AAAAABgDYEDAAAAgDUEDgAAAADWEDgAAAAAWEPgAAAAAGANgQMAAACANQQOAAAAANYQOAAAAABYQ+AAAAAAYA2BAwAAAIA1BA4AAAAA1hA4AAAAAFhD4AAAAABgDYEDAAAAgDUEDgAAAADWEDgAAAAAWEPgAAAAAGANgQMAAACANQQOAAAAANYQOAAAAABYQ+AAAAAAYE2ZA4fb7Zbb7ZYkLVmyRHfccYeuvvpqtW3bVsOHD1dSUlKBMvv375fb7dZNN91UrPcta31lKSdJGRkZeuutt9S7d2+1bt1aLVu21G233aZp06bp+PHjBdafNm2a3G63pk2bpuTkZI0fP14dOnTQVVddpZdeeqnQegAAAIDzTbld4fj73/+uiRMnKjQ0VJ06dVKtWrW0fv169e3bVwkJCeVVTZnrK2m5X3/9VX369NGkSZN04MABtWrVSu3bt1daWpqmT5+ue+65R2lpaV7r2r17t+68805t3LhRERER6tixo2rWrFlufQAAAABUdf7l9UYffPCB3n33XUVFRUmSjDF64403NGfOHI0ZM0br1q1TUFBQeVVX6vpKUs4Yo1GjRmnnzp26//77NWbMGFWrVk2SlJmZqWeeeUarVq3Syy+/rFdeeaVAXTExMerVq5eee+45BQYGltu2AwAAAOeKcgsc99xzj3MSL0k+Pj4aNWqUYmNjtW/fPq1bt049evQor+pKXV9Jyn3xxRf69ttvFRERoQkTJsjX9/9fEAoODtZzzz2nTZs2afXq1Ro/frzCwsLy1VWrVi1NmDCBsAEAQCmEh4dWSBmUDn1dMc6Hfi63W6q8ndz7+fnptttukyTFxcWVV1Vlqq8k5b744gtJ0s0335wvbHiEhISoefPmysnJ0XfffVdg+XXXXacaNWoUY2sAAACA81O5XeG4+OKLi3z9119/La+qylRfScrt27dPkvTaa6/ptddeK7I9R48eLfDaRRddVGQZAABQuJSU9GKv6/kVuCRlUDr0dcWoav0cFlZNgYGliw7lFjjOxsfHp9jr5uXlVWh9hZXLzc2VJLVp00YNGzYsspy3cBEcHFyqNgAAAADni3ILHMnJyWratGmB1/fv3y9JqlevnvNaQECAJHmdUtbzXuVZX2nLNWjQQJJ0yy236L777jtrmwAAAADkV25jOFatWlXgtdzcXMXGxko6dZXAo06dOgoICFBqaqrXW5E8YyfKq77SluvQoYMkae3atWdtDwAAAICCyi1wLFq0KN9zLIwxmjp1qvbs2aP69eura9euzrKAgABFRkZKkv75z3/KGOMsS0hI0NSpU8u1vtKW69y5s5o1a6a4uDg9++yzSk1NLfB++/bt0/vvv3/W9gIAAAB/RuV2S1WfPn0UHR2tyMhI1atXT9u3b9cvv/yi4OBgTZo0qcB4hhEjRighIUGLFy9WfHy8mjRpogMHDmj79u0aOnSoZs2aVa71laacr6+vZs6cqSFDhmjJkiWKiYlR06ZNdeGFF+r333/XgQMHtHv3btWtW5dbrgAAAAAvyi1wjB8/XpdeeqmWLFmibdu2KSgoSJ07d9aIESPkdrsLrN+6dWvNmzdP06dPV2Jiog4cOKAmTZro1VdfVY8ePc4aOEpaX2nLXXjhhfrwww/14YcfKjY2VklJSdq2bZtq1aqlevXqafDgwerSpUvJOwwAAAD4E/Axp9/PVAqek/QdO3aUS4Ns1VfR7axI3+08oqdmbarsZgAAUC5WT+4piWlxqyr6umJUtX4uy7S45TaGAwAAAADOROAAAAAAYA2BAwAAAIA1ZR40XtFjIkpb3/k4dgMAAACo6rjCAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAa/8puAMquRZO6Wj25Z2U3AwAAACiAKxwAAAAArOEKx3ng5MkcpaWdqOxmnNfCw0MlSSkp6ZXckvMb/Vxx6OuKQ18D+LPjCgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwxr+yG4CyCwz0V3h4aGU340+Bfq4Y9HPFoa8rzvnS1ykp6ZXdBADnGK5wAAAAALCGKxznge92HtFTszZVdjMAAOex1ZN7VnYTAJyjuMIBAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGsIHAAAAACsIXAAAAAAsIbAAQAAAMAaAgcAAAAAawgcAAAAAKwhcAAAAACwhsABAAAAwBoCBwAAAABrCBwAAAAArCFwAAAAALCGwAEAAADAGgIHAAAAAGuqTOBwu91yu92V3QwAAAAA5ajKBA4AAAAA5x8CBwAAAABrCBwAAAAArKnQwLFjxw4NGzZMbdq0UUREhO68804tXbq0yDLJycmaOHGiOnXqpObNmysqKkrR0dFavXp1oWWys7M1Z84c3XrrrWrRooXat2+vJ598UgcOHNC0adPkdrs1bdq0citnjNGKFSsUHR2tqKgotWjRQp07d9Zzzz2ngwcPlryjAAAAgPOEf0VVFBcXpyFDhigzM1OXXXaZrrrqKqWkpOjZZ5/Vzp07vZZJTEzUgw8+qGPHjuniiy9Wly5dlJqaqvj4eMXFxWnjxo169dVX5ePj45TJzc3VI488oo0bNyo4OFjt2rVT9erVtXnzZvXq1UsdO3b0WldpyxljNGbMGMXExCggIEBt2rRRWFiYtm3bpoULF2rNmjV666231LJly7J3IgAAAHCOqZDAkZmZqTFjxigzM1NDhw7V6NGjnZAQFxenhx56qECZrKwsjRw5UseOHdOAAQM0duxY+fn5SZKSkpI0cOBArVy5Uq1bt1a/fv2ccgsWLNDGjRvVsGFDzZ8/X40aNdL/a+/Ow6qq9j6Af5kOyqBi4oT4kukGUxQLUbOr91qOaZaW80zae9Mc0qspalqYmV4zcB6QLpmVA045pLdRMwEHnEFMEVAZHBBlOpyz3j949n45nnMQ4Ww4wPfzPD3p2XvtvdaPxXH/9l5rbQDIz8/Hhx9+iJ07d5qsY2nLffPNN9i3bx/q1auH8PBwtGjRAkBhArN48WJERERg6tSpOHjwIDQaTSkjSEREZB3c3V0rugrFsvb6VSWMdfmoCnEulyFVBw8eRGpqKpo2bYopU6YYPJEICAgwSBhkBw4cwK1bt+Dh4YF//etfSrIBAJIkYdKkSQCAsLAwg3IREREAgClTpihJAwBoNBrMmzcPTk5OJutY2nKbN29WysnJBgDY2dlh5syZaNSoEVJSUnDw4EGT5YmIiIiIqrJyecIRHR0NAOjTp49B4iDr37+/cuH+eJl+/frBwcHBqMzAgQPx8ccfIzExEampqWjQoAFu3bqF5ORk2NnZoU+fPkZl3Nzc8NJLL+HIkSMGn5e23O3bt5GUlARbW1v079/fqJxGo0G/fv2wfv16REVF4fXXXzfah4iIqDJJT8+q6CqYJN8Fttb6VSWMdfmwtjjXrl0TGk3pUodyecJx+/ZtAECTJk1Mbvfw8DD6LDU1tdgyjo6OqF+/vsG+8v/d3d1NJikA0LhxY7PnKks5R0dHZt/sggAAIABJREFUk+WaNm1qsC8RERERUXViFcviFh1iJRNCPLGcuX1MHU9ma2u+yU9bTj5/ceVK0g4iIiIioqqqXBKOBg0aAChc4taU5ORko88aNmxodhtQOKk8PT3d4PjyE4+0tDRotVqT5UzVobTl5DqmpaUhPz/fZDm5/nIdiYiIiIiqk3JJOAICAgAA+/fvh06nM9q+Z88eo8/at28PANi3bx8KCgqMtkdGRkIIgf/5n/9RLuYbN24MDw8P6HQ6k5O079+/j2PHjhl9XtpyDRs2hKenJ/R6PXbv3m20XavVYt++fQD+PwZERERERNVJuSQcPXv2hLu7OxITExEaGmowzCgmJgbffvutUZnevXujUaNGSE5Oxr///W/o9XplW0JCgvICvnHjxhmUGzFiBADgiy++MHgqkZ+fj+DgYGRnZ5usY2nLjR07FgDw5Zdf4urVq8rnOp0OS5cuRUpKCjw8PNCrVy+T5YmIiIiIqrJyWaWqZs2aWLp0Kd59912sWbMGhw4dwvPPP4+0tDTExMRg1KhRCA8PNyjj6OiIFStWYPz48QgLC8ORI0fg6+uLzMxMnDhxAlqtFv3798fgwYMNyo0aNQrHjh3D0aNH0adPH3Ts2BE1a9bE6dOnkZubizfeeAO7du0ymhxe2nLDhg3DqVOnsG/fPvTv3x8dOnRQXvyXlJSE2rVrY8WKFXwHBxERERFVS+U2abxTp074/vvv0a1bN2RkZODIkSN48OAB5s+fj9mzZ5ss4+fnh127dmHIkCHQ6XT48ccfERsbCz8/PyxdutToLeMAYG9vjzVr1uCDDz5Ao0aNcOzYMURFRcHf3x87duyAvX1hjuXm5maRcjY2Nli2bBmWLFmCtm3bIjY2Fj/++CP0ej2GDh2K3bt38y3jRERERFRt2YhqtIxSQUEB+vbti2vXrmHHjh1o3bq1quXKy7mEDMxZYzzHhIiIyFL2/rvwfVPW8k6Ax1nbOwuqMsa6fFhbnK3+PRzl7dKlS0arTeXk5CA4OBjXrl2DJEkmk4bSliMiIiIiItPKZQ5Hefv444+RkJAAHx8fuLu74+7du7h8+TLu3buHWrVqYfHixRYtR0REREREplXJhGPw4MHYu3cv4uPjERsbCwBo1KgRevfujcDAQLNvLy9tOSIiIiIiMq1KJhxvvPEG3njjjXIrR0REREREplXJORxERERERGQdmHAQEREREZFqmHAQEREREZFqmHAQEREREZFqmHAQEREREZFqmHAQEREREZFqmHAQEREREZFqmHAQEREREZFqmHAQEREREZFqmHAQEREREZFqmHAQEREREZFqmHAQEREREZFq7Cu6AlR2vs3rYe+/+1d0NYiIiIiIjPAJBxERERERqYZPOKqA/PwCZGbmVHQ1qjR3d1cAQHp6VgXXpGpjnMsPY11+GGsiqu74hIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFTDhIOIiIiIiFRjI4QQFV0JKhu9XqCgQFfR1ajSNBp7AEB+fkEF16RqY5zLD2Ndfhjr8sE4lx/GunxYW5zt7e1ga2tTqrJMOIiIiIiISDUcUkVERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKqxr+gK0P/bu3cvtm7diri4OOj1ejz77LMYOHAghg4dClvbp88Nf/vtN4SHh+P8+fPIy8uDp6cnXnvtNQQGBkKj0ajQgsrDUrG+desWfv75Z5w/fx7nzp3D1atXodPpMHPmTAQGBqrYgsrBEnHW6/U4c+YMfv31V0RHRyM5ORl3795FrVq10KpVKwwePBivvvqqyi2xfpbq03v27MHvv/+Oy5cvIz09HVlZWXByckLz5s3x2muvYfDgwXBwcFCxJdbP0t/VRX333XeYP38+AGD48OHKn6sjS8U5NDQUK1euNLtdo9Hg3LlzlqhypWXpPq3T6bBt2zbs3bsXCQkJyM7ORt26ddGyZUsMGjQI3bp1U6EV1s8ScU5OTsYrr7xSon2//vprtG/fvixVthgbIYSo6EoQsHDhQnzzzTdwdHREp06dYG9vj+PHj+PRo0fo3r07vvzyS9jZ2ZX4eBs2bMCyZctgZ2eHgIAA1KpVC9HR0bh79y78/PwQHh6OmjVrqtgi62XJWIeHh2Px4sVGnzPhsFycExMT0aNHDwBAnTp10Lp1a9SqVQtJSUnKRcKAAQPw6aefwsbGRtU2WStL9umhQ4fizJkzaN68ORo2bAhXV1ekpaXhzJkz0Gq18PPzw+bNm+Hk5KRyq6yTpb+ri0pJSUG/fv2QnZ0NIUS1TjgsGWc54fDx8UHLli2Nttvb2yM4ONjSTag0LN2n79+/j/Hjx+Ps2bNwdXXFCy+8AGdnZ9y6dQuXLl1C3759sWjRIhVbZJ0sFee7d+/i888/N7s9ISEB586dg7OzM44ePWo939WCKtzBgweFJEmic+fO4tq1a8rn6enponfv3kKSJBEeHl7i4509e1Z4e3uLtm3bijNnziifP3z4UAwfPlxIkiQWLVpkySZUGpaO9eHDh0VwcLCIjIwUCQkJ4l//+peQJEls3LhRhdpXHpaMc2Jiohg1apT49ddfRUFBgcG2EydOCD8/PyFJkti+fbslm1BpWLpPx8bGiszMTKPPb926JXr16iUkSRJffvmlJape6Vg61kXp9XoxevRo4efnJ2bNmiUkSRILFy60UM0rF0vHOSQkREiSJEJCQlSobeVm6VjrdDoxePBgIUmSCAoKEo8ePTLY/vDhQxEXF2ep6lcaan53PO6dd94RkiSJuXPnWuR4lsKEwwq8+eabQpIkERkZabTtxIkTSifV6XQlOt77778vJEkSoaGhRttu3LghfHx8RKtWrUxeVFR1lo714+QLheqecKgd56JWrVolJEkSo0aNKvOxKqPyjHVkZKSQJEkMHjy4zMeqjNSM9ZYtW4QkSeI///mPcoFcXRMOS8eZCYd5lo711q1bhSRJYsSIEUKv11u6upVWeX1P3759W7Rs2VJIkmRww9kacNJ4Bbt9+zYuXLgABwcH9OrVy2h7QEAAGjRogPT0dJw5c+aJx8vPz8dvv/0GAHj99deNtnt6esLPzw9arRa//vpr2RtQiVg61mRaecf5+eefV85b3ZR3rO3tC6f9Vcc5YGrGOikpCUuXLsULL7yAESNGWKrKlRK/p8uPGrHesmULAOCdd96ptkNcH1eefXrnzp3Q6XRo0aIF2rZtW6ZjWRoTjgp28eJFAECLFi1Qo0YNk/v4+voCAC5duvTE4127dg05OTmoU6cOmjZtWuzx5HNXF5aONZlW3nG+fv06AKB+/fplPlZlU56xvnv3LjZt2gQA1XLCp1qxFkJgzpw50Ol0WLRoUbW/SFOzT1+4cAFLly7FvHnzsGzZMhw+fBj5+fllq3AlZulYp6enIz4+Hg4ODujYsSPi4uIQGhqK+fPnY/ny5Th27JjlKl+JlOf3dGRkJADgrbfeKtNx1MBVqipYcnIyAKBx48Zm92nUqJHBviU5nlzGFPlcKSkpJa5nVWDpWJNp5RnnnJwcREREAIAysbw6UTPWP/30E3788UfodDqkp6fj1KlTyMvLw4ABAzB8+PDSV7qSUivWX3/9NaKiojB9+nQ0a9asbJWsAtTs0z///DN+/vlng88aNmyIpUuXIiAg4ClrWvlZOtZxcXEAAA8PD6xevRrr1q2DKLIu0bp169C+fXuEhISgbt26Zal6pVJe/yZGRUUhMTERDg4OJke4VDQ+4ahg2dnZAFDsilHOzs4AgEePHlnkePKKBSU5XlVi6ViTaeUZ54ULFyI5ORnNmzfH4MGDy3SsykjNWF++fBmRkZHYs2cPjh8/jry8PIwaNQpz5syplsviqhHrGzduYPny5WjVqlW1X9VOpkacPT09MX36dOzevRsnT57E8ePH8dVXXyEgIAC3b9/GhAkTcPny5bJXvpKxdKwzMzMBFF40r127Fv3798f+/ftx8uRJfPXVV3juuecQHR2NqVOnWqD2lUd5/Zu4Y8cOAIVPoK0xoWPCUcHk7N9Sj9EtfbyqhLEpH+UV51WrViEyMhKurq5YsWJFtZxXoGas33vvPcTFxeHcuXM4dOgQpk6diu3bt6N///5ISEiw+PmsnRrf1XPmzIFWq8Wnn35a6qV0qxo1+vQbb7yBCRMmwMfHBy4uLqhbty46duyIiIgI9OzZEzk5Ofjiiy8sdr7KwtKx1uv1AICCggJ06tQJS5YswXPPPQcXFxd07NgRYWFhqFGjBk6cOIGoqCiLnLMyKI9/Ex8+fIhDhw4BAAYOHKjaecqCCUcFk7NaOQM2Rc545X3Lejx5W0mOV5VYOtZkWnnEefPmzQgJCYGTkxM2bNiAFi1alOo4lV15xFqj0cDLywv//Oc/8dlnnyElJQUzZ840GCpRHVg61v/5z38QHR2tXAhTofL+nn7vvfcAAMeOHYNWqy3z8SoTta4/AGDQoEFG2xs2bIiuXbsCAI4fP/5Uda3MyqNP//DDD8jJyUHDhg3xt7/9rVTHUBvncFQwDw8PAMDNmzfN7iOvviPvW5Lj3bp1y+w+8raSHK8qsXSsyTS14xwREYHPPvsMNWrUwLp169CuXbvSVbQKKO8+3aNHD7i4uODChQtITk6Gp6dnmY9ZWVg61keOHAEA/PHHH4iOjjbYJs+vO3z4MK5cuQInJyesW7euVPWubMq7T8vzZrRaLe7du1etFp9Q6/oDAJo0aWJyH/nzjIyMEtezsiuPPi0Pp3rzzTdL9Wb48sCEo4LJS3peuXIFubm5JlcwkN+mbOoNqY9r1qwZatSogfv37+PGjRsmV6o6e/ZsiY9XlVg61mSamnHesmULgoOD4ejoiDVr1lTLiZ5FlXeftrGxQZ06dfDw4UPcvXu3WiUcasX69OnTZrelpaUhLS0Nrq6uT1nbyqu8+/T9+/eVP1vNG5nLiRrXH05OTsjOzjaIa1H37t0DUL1irXafTkhIQGxsLGxsbKx2OBXAIVUVrlGjRmjVqhW0Wi0OHjxotD0qKgq3b9+Gu7t7ie7kajQadOnSBQCwZ88eo+1JSUk4c+YMHBwc8Pe//73M9a9MLB1rMk2tOG/duhUff/wxNBoNVq1ahZdeesmS1a6UyrtPJyUlISUlBba2ttUq2QAsH+uIiAjExcWZ/G/SpEkAgOHDhyMuLg4xMTEWb4+1Ku8+feDAAQDAs88+CxcXlzIfrzKxdKyLXleYGjKl1WqVvty6deuyVb4SUbtPb9++HQDQoUMHq/5eZsJhBSZMmAAAWLZsGRITE5XP79y5g4ULFwIAxo8fb/CY7Ouvv0avXr0wc+ZMo+ONHz8eNjY22Lhxo/I0AygcIzhnzhzo9XoMGzYMtWrVUqtJVsvSsSbTLB3n77//HgsXLoRGo8HKlSutdoxqRbBkrBMSErB161Y8fPjQ6Dzx8fGYMmUKhBDo3r27Va6CojZ+f5QPS8b55s2b2Lt3r9H7NoQQ2LVrF5YvXw4AGDNmjBpNsXqW7tPvvvsubG1t8c033xgkHTqdDsuWLcONGzfQoEEDdO/eXa0mWSW1vju0Wq1yc9ka371RFIdUWYFevXph6NCh2Lp1K/r164eXXnoJ9vb2OH78OB4+fIhXX33V6O2z9+7dw7Vr1+Du7m50vDZt2mD69OlYtmwZhgwZgo4dO8LV1RXR0dG4c+cO2rZti2nTppVX86yKpWOdlpam3I0ECpe5BAq/KOQVIwBg5cqV1WpssCXjfOnSJcyfPx9CCDRp0gQHDhxQ7koW5ebmhlmzZqnaLmtkyVjfuXMHCxYswJIlS9CqVSs0aNAA+fn5SElJwaVLlyCEQJs2bZR/IKsbS39/kGmWjHNmZiZmzJiBjz76CM8++ywaN24MrVaLK1euKO88GDFiBIYMGVJu7bMmlu7TPj4+mDNnDhYtWoRx48bB19cXDRs2xMWLF5GUlARXV1d8+eWXZl+AV1Wp9d3xyy+/4M6dO6hVq5bVv4uKCYeVWLBgAV588UVs2bIFUVFR0Ov1aNasGQYOHIihQ4c+9SSg8ePHw9vbG5s3b8a5c+eQl5cHT09PjBw5EoGBgdVyCVGZJWOdn5+P2NhYo89v3rxpMEGsOr7N1lJxfvDggbIi0l9//YW//vrL5H4eHh7VMuEALBfrFi1aYOrUqYiJicG1a9dw4cIFFBQUwM3NDV26dEHv3r3x+uuvV+slXC39XU2mWSrODRs2RGBgIM6dO4cbN24gISEBer0e7u7u6NOnDwYNGoROnTqp3BrrZuk+PXLkSEiShLCwMJw5cwYXL16Eu7s7Bg8ejAkTJpidUF7VqfHdIU8W79u3LxwdHS1dZYuyEdVtbUMiIiIiIio3vBVDRERERESqYcJBRERERESqYcJBRERERESqYcJBRERERESqYcJBRERERESqYcJBRERERESqYcJBRERERESqYcJBRERERESqYcJBRERERESqYcJBRERERESqYcJBRERERESqYcJBRGSFTpw4AW9vb3Tr1q2iq1KthYaGwtvbGx9++GFFV6VCpaenIygoCF27dkWrVq3g7e2NkSNHAihbjJKTk+Ht7Q1vb29LV5mIrAgTDiKqEB9++KFyoTFgwAAIIczuO2PGDF70WbkHDx4gNDQUoaGhFV0VsrCCggKMHj0a27dvx+3bt+Hs7Ix69eqhdu3aFV01Iqok7Cu6AkREFy5cwOHDh9GjR4+KrgqV0oMHD7By5UoAwPvvv1/BtSFLOnr0KK5evYo6dergu+++g5eXl8F2Nzc3PPvss3B3d6+YChKR1WPCQURWISQkBK+++ipsbfnglciaXLlyBQDQoUMHo2QDAEaMGIERI0aUc62IqDLhv+xEVKECAgJQs2ZNXLlyBXv37q3o6hDRY/Ly8gAATk5OFVwTIqqsmHAQUYWqV68ehg8fDgBYuXIlCgoKSnWc+Ph4zJ49G926dYOvry/8/f0xZMgQbN26FVqt1mj/kkzK3rlzp8Hk2KLk+SfJycm4evUqZs2apUyofe+995T9kpKSEBYWhtGjRxvUbdCgQQgLC0Nubm6p2mvO45Nw4+PjMW3aNHTu3Bm+vr7o1asXVq1ahfz8/GKPExMTg2nTpqFLly5o3bo1OnTogDFjxmDfvn1G821GjhyJV155Rfm7fH75P3lex+jRo+Ht7Y0tW7YYnW/Tpk3K/vv37zfavmzZMrPzePLz87F582a8/fbbePHFF9GmTRv07NkTixcvRnp6usn2Pf6z3bNnD0aMGIEOHTrA29sbR44cKTY+snXr1sHb2xu+vr4lLlPU77//jsmTJytx7ty5MwYNGoTVq1fj1q1bJsv8+OOPCAwMRMeOHdG6dWt06dIF06dPx4ULF0zuX9o+Ic+zkn9+kZGRBj/X5ORkAE+eNJ6Xl4dVq1ahV69e8PX1xcsvv4xp06YhISGhRDFKTk7GJ598gp49e6Jt27Zo164dBgwYgPXr1yM7O9tkmaJ1vHnzJubOnavEuFu3bliyZAkePnxY7HmvXr2K+fPno2fPnvDz84O/vz/69euH4OBgnD9/3mSZu3fv4t///jf69euHdu3awc/PD3379sUXX3yB+/fvl6i9RFURh1QRUYUbP348vv32W9y4cQM7d+7EoEGDnqr8119/jUWLFkGv1wMovBObnZ2N06dP4/Tp09i/fz/Wr1+PmjVrWrzuMTExWLBgAXJycuDs7Aw7OzuD7VOmTFEuBG1sbODq6oqsrCzExsYiNjYWP/zwA7766iu4uLhYvG5Hjx7FxIkTkZubC1dXVxQUFODatWsICQnBhQsXsHr1apPlli5dio0bNyp/d3Z2xoMHD3D8+HEcP34cP/30E5YtW6YMf6tduzbc3Nxw7949AIVJZFHynfH27dvjzz//RHR0tJJkyqKjow3+3KdPH4PtMTExyjGKunv3LgIDA3Hx4kUAgEajgYODA65fv47w8HBERkZi/fr18PPzMxun4OBgREREwNbWFq6uriUe1ifHycnJCatXr0anTp1KVA4oTJKCgoKwZ88e5TNXV1fcv38fGRkZiI2NhU6nM5gPo9frMXv2bOzatQsAYGdnB2dnZ6SmpmLfvn3Yv38/5s2bh2HDhpk979P0CRcXF9SrVw/Z2dnIzs6Go6MjXF1dle2P93VTHj16hLFjxyI2NhYA4ODggJycHOzfvx+//PILPvnkk2LL//jjj5gxY4bylKVGjRrQarW4cOECLly4gL1792Lz5s1GfU52+fJlBAUF4f79+3B2doYQAikpKQgLC0NUVBS+/fZbODg4GJWLiIjA4sWLodPpABT2Ya1Wi/j4eMTHxyMuLg4REREGZWJiYjBx4kQlsXBwcICdnR2uXLmCK1euYPfu3QgLC0OzZs2eGDeiKkcQEVWAWbNmCUmSxNSpU4UQQoSEhAhJkkTXrl1FXl6ewb7Tp08XkiSJWbNmGR3n8OHDQpIk4efnJ9auXSsyMjKEEELk5+eLo0ePip49ewpJksS8efMMyv35559CkiTxj3/8w2wdd+zYISRJEiNGjDDaJkmSct4RI0aIuLg4IYQQer1eJCYmKvsFBQWJ8PBwkZiYqLQrLy9P/Pe//xU9evQQkiSJBQsWGB2/JPUzJSkpSambv7+/mDJlikhKShJCCPHo0SOxbt064e3tLSRJEr/88otR+fDwcCFJkujYsaPYsmWLyMzMFEIIkZubK/bv3y86d+4sJEkSa9euNXtec+Q2vfTSSwaf63Q64e/vL/z8/ISPj4947bXXDLZnZ2eLVq1aCUmSxI0bNwy2BQYGCkmSRPv27cX+/ftFQUGBEEKIs2fPir59+yrnu3PnjkE5+Wfr5+cnvL29RWhoqNLWrKwspR/J/bJo39PpdGLevHlKjE+dOmW2zeYsWLBASJIkWrZsKUJCQkR6eroQQgitViuuXbsmNm7cKL799luDMuvWrROSJAlvb2+xatUqkZWVJYQQ4vbt22Ly5MlCkiTh4+MjoqKiDMqVtU+YikFJtwcFBQlJkkSbNm3Ejh07RH5+vhBCiEuXLokBAwaIF1980Wy/iY2NFa1atRItW7YUS5cuFSkpKUKv14uCggJx5swZ8fbbbwtJksS4ceOMyhZt76hRo5Tfz7y8PLFt2zbRunVrIUmS+Prrr43K7t+/Xyn//vvvi4SEBCFE4e92amqq2L17t1i8eLFBmeTkZOHv7y8kSRJBQUHi6tWrQqfTCb1eL+Lj45V+2qdPH6WPElUnHFJFRFZh7NixqFOnDm7duoWtW7eWqIxOp8Onn34KAPj888/x7rvv4plnngFQeHexc+fO2LBhA5ycnLBjxw6kpaVZvN7PPPMMNmzYAEmSABQ+xWjatKmyPTg4GKNHj0bTpk2h0WgAFN6F79atGzZu3Ah7e3tERkYiJyfH4nXz9fXFF198gSZNmgAovEs7YcIE/P3vfwcAHDx40GD/Bw8eYMWKFbC3t8e6deswbNgw1KpVCwDg6OiI3r17Y+XKlbCxscGmTZueOCzrcX5+ftBoNMjIyMBff/2lfB4XF4cHDx7A398f3t7eSEhIwN27d5Xtp0+fhlarRcOGDeHp6al8HhMTg99//x1A4ZCr3r17K3fdfX19sXnzZtSuXRsZGRlGd6Nl2dnZmDBhAiZNmqS01cXFRelHj9NqtZgxYwa+++47PPPMM4iIiEC7du2eKg5XrlxR+vhHH32E999/X7lDb29vDy8vLwQGBmLw4MEG9Vy3bh2AwieC7733nvJUrEGDBli+fDlefPFF6PV6rFixwuy5n7ZPlEVKSgp27NihtHPAgAHK0wQfHx9s2rTJ5NMF2eLFi6HVajFz5kzMmDEDjRs3ho2NDezs7NC2bVts2LAB9evXx9GjR3Hu3DmTx2jQoIHB76dGo8Fbb72lPEU9dOiQwf5arRafffYZAKBv374ICQnBc889B6Dwd7t+/fp4/fXXjYaPffHFF3jw4AFGjhyJ4OBgNGvWDLa2trCxsUGLFi2wevVq+Pj4ICEhAYcPH37aUBJVekw4iMgquLi4IDAwEEDhuHhzY7OLioqKQkpKCjw8PNC9e3eT+3h6eqJt27YoKChAVFSUResMFK7QU6NGjVKV9fT0RPPmzZGTk4NLly5ZuGaFF6Y2NjZGn8vzLeTVh2SHDh1CdnY2XnjhBbRp08bkMf38/ODp6YnMzEyzcwbMcXR0hK+vLwDDIVTyzyUgIAD+/v4QQihDqIpuf3w4lXxxLM9jeFy9evUwZMgQAMCBAwdM1snOzg5jxowpUf1zc3MxadIk/PDDD2jUqBG2bNkCHx+fEpUtavfu3RBCoFmzZgZJRXGOHTuGhw8fwsHBAe+8847Rdjs7O2XuUExMjNm5K0/bJ8ri8OHD0Ov1qF+/Pt544w2j7XXq1MHQoUNNlr1x4wZOnTqFGjVqKD/Dx9WuXVv5uf/xxx8m9xkzZoyS6Bf16quvAjBu7/Hjx3H79m3Y2dlh5syZ5htXRG5urtIXx44da3IfjUaDnj17FltXoqqMcziIyGqMHDkSX331lXI03A+RAAAL2UlEQVRH+t133y12/1OnTgEA0tLS0LlzZ7P7ZWVlAYDZSbhlUdzcANmxY8ewY8cOnD17Funp6SYniqvx9EW+uH9cgwYNABQ+0Sjq9OnTAICzZ88WG8/MzEwAhfF82rv77du3x8mTJxEVFaVcbMvJR0BAAFJTUxEREYGoqCjlvSxFtxclz9vo0KGD2fN17NgR69atw/Xr15GdnW200lLTpk1Rt27dJ9b70aNHGD9+PKKiouDl5YXNmzejcePGJWy1IXk+Q9euXUtcRk7ufHx8zL5wr3379rC3t0dBQQEuXrxo8vhP2yfKQq6zv7+/2XkxjyeRMvl3W6vVGixI8Dj5xoS5321z7a1fvz4A4/bKPxsfHx8lJk9y/vx5ZWGK4uafyb/3anwPEVk7JhxEZDVq1qyJd999F4sWLcKmTZswbNgwg0mqj5Pv4mq1WmRkZDzx+JZeEQrAEy9W5QnJMgcHB9SpUwf29oVfv5mZmdBqtaoMqTI3Ed3R0REAjFYEk+OZm5tboliVJp7t27fH2rVrlSRCCIHo6Gg4OTmhVatW8PT0hI2NjbI9Ly8PZ8+eVcoWJQ+7Ku7CUN4mhMC9e/eMEo6SJBtA4eRloHDI08aNG0udbABQ+mqjRo1KXKYkbXV0dESdOnWQkZFhMCStqKftE2Uh10G+uDfFXHvkvqjT6cr0u+3s7Gzyc3PtLc3PpujNgor6HiKydkw4iMiqDBkyBGFhYbh16xbCwsIwZcoUs/vKq1J1795dect1eStuRaNff/0VERERynCX119/Xbmglg0bNgwnT540Wmq2IsjxHDNmDGbPnq3KOV544QXY29sjNTUVN27cQG5uLu7fv4+XX34Z9vb2qFu3Lpo3b464uDhkZmbi8uXLyM/PR7169fDss8+aPObTziUpqiQrLQGFyU5iYiLS0tIQFBSE9evXl3ooXVl+1mVpqzUyFwv58+effx6RkZEVXp+SlKldu7YqwzaJqgLO4SAiq6LRaJSx6F999ZXZO7XA/y+/WtL1/IuSLzTl5TZNkYdilZY8rvutt97CpEmT0LRpU6Px83fu3CnTOSypLPEsKflJBlA4N0N+klF0WFT79u2VeRzydlNDb+SnEzdv3jR7vtTUVACFE37d3NxKXe8mTZogPDwc9erVw4kTJzBx4sRSX/y7u7sDKL7ejytJW/Py8pQlWUv65EZNch2KGy5obq6JPGn/+vXrFn3q8iSl+dnIdc3MzDTbHqLqjgkHEVmdAQMGoGnTpnj06BE2bNhgdj95/sS1a9eeerKrvCLRnTt3zF44mlv5pqTki93nn3/e5PaUlBQkJiaW6RyWJMczOjpaeadGSRV90vOku8T+/v7KeUxNCJf/bG67TI5rdHS02XP++eefAAAvL68yvyn7ueeeQ3h4ONzc3HD06FG8//77pUo62rZtCwD47bffSlxGTtISExOVfvW46Oho5eLcXJ8rT3Kdi3uCV3TxgKLkvpidnY1jx46pU0ET5J9NXFyc2Tg/rnXr1soQSXnoHREZYsJBRFbH3t4ekyZNAgB88803Zu+QdurUSRlLX/QlXabIE51lXl5e0Gg0EELg559/Nto/MTGxzBcP8nj5+Ph4k9uXL19uFUOpZL169YKTkxPy8vLw+eefF7vv4/EsOjfgSROP5cnfUVFRiImJgZOTE1q3bq1sl5OLY8eOKZN4H58wLtcXKFxp6L///a/R9oyMDHz77bcAgN69exdbp5Jq0aKFstzuL7/8gmnTpj31Hfj+/fvDxsYGf/31l1K/J+ncuTNcXFyg1WoNXsoo0+l0ykv7/P39lTv1Fal79+6wtbVFamoqdu/ebbQ9MzPTbPufe+45JelYtmxZsavW5ebmWmyoWadOndCgQQPodLon/g7IXFxclAUO1qxZU+w8joKCAjx69MgidSWqTJhwEJFV6tevH5o3b47c3FycOHHC5D4ODg6YO3cubGxscOzYMYwbNw6xsbHKRXxBQQHOnz+PZcuWKctgyjQajbL6zeLFixETEwO9Xg+9Xo+jR49i7NixpR6jL5NXevruu++wfft25aLo5s2bmDVrFn744QezKw5VBDc3N3zwwQcAgJ07d2LKlCkGyVJeXh5iYmKwcOFCo+VMa9WqpUwO3rlzZ7HnefHFF2Fra4ubN28iIyMD7dq1M3gfg7u7O7y8vBAfH4/c3Fy4ubmhefPmRsfx9/fH3/72NwDAnDlzcPDgQSXpPH/+PMaNG4fMzEzUq1cPo0aNKkVETGvZsiXCwsLg6uqKI0eOYMaMGcUmu49r0aKFskLXxx9/jNDQUGVonU6nw/Xr1xEaGmrwPhonJydl1baIiAisWbNGuXBNTU3FBx98gJMnT8LW1hZTp061VFPLxMPDAwMHDgQALFiwALt27VJWc4qLi8M777xT7JDGuXPnQqPRID4+HsOHD8cff/yhJHd6vR5XrlzB6tWr8eqrr1pslTcHBwflHRv79u3DlClTcPXqVWV7Wloavv/+ewQHBxuUmz59OurUqYP09HQMGTIEhw8fNkiCEhMTER4ejj59+uD8+fMWqStRZcJJ40RklWxtbTF58mRMnjy52P1eeeUVLFq0CB999BH+/PNPDBo0CI6OjqhZsyaysrKKvRCcPn06jh8/jlu3bmH48OGoWbMm9Ho98vLy0LJlS4wZMwaLFi0qdRvefPNN7Ny5E2fOnEFQUBDmz58PZ2dn5QnA5MmT8eeff1rVRNORI0ciKysLISEhOHjwIA4ePIiaNWtCo9EgKytLmVju4eFhVPbtt9/GqlWr8NlnnyEkJESZMzFq1CiDd124urrCx8dHWdbW1NOLgIAAXL9+HUDhEw9T744ACl/4OG7cOFy6dAlTpkyBo6Mj7O3tlYvx2rVrY+XKlWWav2FK69atsXHjRowbNw4HDhyAg4MDlixZUuwiAkUFBQUhMzMTBw4cwMqVK7Fy5UrUqlUL2dnZykW1/JRPFhgYiKtXr2LXrl1YsWIFQkND4eLiggcPHkAIAVtbW8ydO9fsUrMVYfbs2YiPj0dsbCxmzZqFefPmwdHREVlZWXBycsInn3yC6dOnmyzr6+uLlStXYvr06bh48SLGjh0LBwcHODs749GjR0ryAsBs/yiNPn36IDU1FZ9//rnyO+Dk5AS9Xq+sMPV4n23SpAk2bNiAiRMnIikpCZMmTYK9vT1cXFyQnZ1tkHxYsq5ElQWfcBCR1erRo4cyDrw4AwcOxMGDBzF69Gi0aNECdnZ2ePjwIerUqYMOHTpg1qxZ+Omnn4zKeXp6Ytu2bejbty/q1q0LnU6Hhg0b4n//93+xdetWs0uIlpRGo8HmzZsxYcIEeHp6wtbWFnZ2dujcuTPWrl2LiRMnlun4annvvfewe/duDB48GF5eXhBCIDs7G+7u7ujSpQsWLFiAbdu2GZWbOHEiZsyYAW9vbwghkJKSgpSUFJOT703N2TC3XZ7zYUrdunXx3Xff4cMPP1TG0mu1Wnh5eWH06NHYt2/fU78rpKT8/Pywfv16ODk5Yc+ePZg7d26Jh8hpNBqsWLECq1evxj/+8Q/Uq1cPOTk5cHNzg5+fH6ZNm2b0Tgc7OzssWbIEISEhePnll+Hq6qr8XPr27Ytt27Zh+PDhajS11JydnREREYHJkyfDy8sLQOGStH369MG2bdue+B6brl274tChQ/jnP/+JVq1aKcmKs7Mz2rVrh8mTJ2P//v0mE+CyGDt2LCIjIzFgwAB4eHigoKAAjo6O8Pb2xqhRo0yu4tamTRscOHAAM2bMQLt27eDs7IysrCzUqFEDrVu3xvjx47F9+3aTCTZRVWcjrGkAMRERERERVSl8wkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKphwkFERERERKr5P32niK+A+IlSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 282,
       "width": 398
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_barh(\n",
    "    df.groupby('stage').conf.mean().sort_values(),\n",
    "    'Neural network confidence by Stage', 'Neural network confidence',\n",
    "    'img/stage_conf.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}