wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "87bbd4d9",
   "metadata": {},
   "source": [
    "# Data Wrangling Project - WeRateDogs\n",
    "## by Olamide Emida\n",
    "\n",
    "\n",
    "## Introduction\n",
    "**Udacity: Wrangle and Analyze data** <br>\n",
    "The goal is to wrangle WeRateDogs Twitter data to create interesting and trustworthy analyses and visualizations. The Twitter archive is great, but it only contains very basic tweet information. Additional gathering, then assessing and cleaning is required for worthy analyses and visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc8a01ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import json\n",
    "import requests\n",
    "import tweepy\n",
    "import configparser\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb7a8ef",
   "metadata": {},
   "source": [
    "## Gather"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cd8bcc",
   "metadata": {},
   "source": [
    "### 1. The WeRateDogs Twitter archive\n",
    "\n",
    "This Enhanced Twitter Archive data has been provided. The WeRateDogs Twitter archive contains basic tweet data for all 5000+ of their tweets, but not everything. One column the archive does contain though: each tweet's text, which has been used to extract rating, dog name, and dog \"stage\" (i.e. doggo, floofer, pupper, and puppo) to make this Twitter archive \"enhanced\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "05f161a3",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the given WeRateDogs Twitter archive data\n",
    "\n",
    "archived = pd.read_csv('twitter-archive-enhanced.csv')\n",
    "archived.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba329562",
   "metadata": {},
   "source": [
    "### 2. Image Predictions File\n",
    "\n",
    "This file (`image_predictions.tsv`) is present in each tweet according to a neural network. It is hosted on Udacity's servers and should be downloaded programmatically using the Requests library and the following URL: https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv. The table is full of image predictions (the top three only) alongside each tweet ID, image URL, and the image number that corresponded to the most confident prediction (numbered 1 to 4 since tweets can have up to four images)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "edf195c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# download the images prediction file programmatically\n",
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "response = requests.get(url)\n",
    "\n",
    "with open(url.split('/')[-1], mode='wb') as file:\n",
    "    file.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1179d64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the downloaded images prediction data\n",
    "\n",
    "images = pd.read_csv('image-predictions.tsv', sep='\\t')\n",
    "images.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3829ae94",
   "metadata": {},
   "source": [
    "### 3. Additional data from the Twitter API\n",
    "\n",
    "Gather each tweet's retweet count and favorite (\"like\") count at the minimum. Using the tweet IDs in the WeRateDogs Twitter archive, 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 called tweet_json.txt file. Each tweet's JSON data should be written to its own line. Then read this .txt file line by line into a pandas DataFrame with (at minimum) tweet ID, retweet count, and favorite count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c6ad3af",
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a config parser instance\n",
    "config = configparser.ConfigParser()\n",
    "config.read('config.ini')\n",
    "\n",
    "api_key = config['twitter']['api_key']\n",
    "api_key_secret = config['twitter']['api_key_secret']\n",
    "access_token = config['twitter']['access_token']\n",
    "access_token_secret = config['twitter']['access_token_secret']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "08910263",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create authentication handler and api object\n",
    "auth = tweepy.OAuthHandler(api_key, api_key_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "\n",
    "api = tweepy.API(auth, wait_on_rate_limit = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "497a6c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2356"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_id_list = list(pd.Series(archived.tweet_id))\n",
    "len(tweets_id_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fcbc3e27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1: 892420643555336193\n",
      "Success\n",
      "2: 892177421306343426\n",
      "Success\n",
      "3: 891815181378084864\n",
      "Success\n",
      "4: 891689557279858688\n",
      "Success\n",
      "5: 891327558926688256\n",
      "Success\n",
      "6: 891087950875897856\n",
      "Success\n",
      "7: 890971913173991426\n",
      "Success\n",
      "8: 890729181411237888\n",
      "Success\n",
      "9: 890609185150312448\n",
      "Success\n",
      "10: 890240255349198849\n",
      "Success\n",
      "11: 890006608113172480\n",
      "Success\n",
      "12: 889880896479866881\n",
      "Success\n",
      "13: 889665388333682689\n",
      "Success\n",
      "14: 889638837579907072\n",
      "Success\n",
      "15: 889531135344209921\n",
      "Success\n",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Rate limit reached. Sleeping for: 272\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success\n",
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     "text": [
      "Rate limit reached. Sleeping for: 329\n"
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      "2139.4308365509996\n",
      "{888202515573088257: NotFound('404 Not Found\\n144 - No status found with that ID.'), 877611172832227328: Forbidden('403 Forbidden\\n179 - Sorry, you are not authorized to see this status.'), 873697596434513921: NotFound('404 Not Found\\n144 - No status found with that ID.'), 872668790621863937: NotFound('404 Not Found\\n144 - No status found with that ID.'), 872261713294495745: NotFound('404 Not Found\\n144 - No status found with that ID.'), 869988702071779329: NotFound('404 Not Found\\n144 - No status found with that ID.'), 866816280283807744: NotFound('404 Not Found\\n144 - No status found with that ID.'), 861769973181624320: NotFound('404 Not Found\\n144 - No status found with that ID.'), 856602993587888130: NotFound('404 Not Found\\n144 - No status found with that ID.'), 856330835276025856: NotFound('404 Not Found\\n144 - No status found with that ID.'), 851953902622658560: NotFound('404 Not Found\\n144 - No status found with that ID.'), 851861385021730816: NotFound('404 Not Found\\n144 - No status found with that ID.'), 845459076796616705: NotFound('404 Not Found\\n144 - No status found with that ID.'), 844704788403113984: NotFound('404 Not Found\\n144 - No status found with that ID.'), 842892208864923648: NotFound('404 Not Found\\n144 - No status found with that ID.'), 837366284874571778: NotFound('404 Not Found\\n144 - No status found with that ID.'), 837012587749474308: NotFound('404 Not Found\\n144 - No status found with that ID.'), 829374341691346946: NotFound('404 Not Found\\n144 - No status found with that ID.'), 827228250799742977: NotFound('404 Not Found\\n144 - No status found with that ID.'), 812747805718642688: NotFound('404 Not Found\\n144 - No status found with that ID.'), 802247111496568832: NotFound('404 Not Found\\n144 - No status found with that ID.'), 779123168116150273: NotFound('404 Not Found\\n144 - No status found with that ID.'), 775898661951791106: TweepyException(\"Failed to send request: ('Connection aborted.', ConnectionResetError(10054, 'An existing connection was forcibly closed by the remote host', None, 10054, None))\"), 775096608509886464: NotFound('404 Not Found\\n144 - No status found with that ID.'), 771004394259247104: Forbidden('403 Forbidden\\n179 - Sorry, you are not authorized to see this status.'), 770743923962707968: NotFound('404 Not Found\\n144 - No status found with that ID.'), 766864461642756096: NotFound('404 Not Found\\n144 - No status found with that ID.'), 759923798737051648: NotFound('404 Not Found\\n144 - No status found with that ID.'), 759566828574212096: NotFound('404 Not Found\\n144 - No status found with that ID.'), 754011816964026368: NotFound('404 Not Found\\n144 - No status found with that ID.'), 680055455951884288: NotFound('404 Not Found\\n144 - No status found with that ID.')}\n"
     ]
    }
   ],
   "source": [
    "# Query Twitter's API for JSON data for each tweet ID in the Twitter archive\n",
    "count = 0\n",
    "fails_dict = {}\n",
    "start = timer()\n",
    "# Save each tweet's returned JSON as a new line in a .txt file\n",
    "with open('tweet_json.txt', 'w') as outfile:\n",
    "    # This loop will likely take 20-30 minutes to run because of Twitter's rate limit\n",
    "    for tweet_id in tweets_id_list:\n",
    "        count += 1\n",
    "        print(str(count) + \": \" + str(tweet_id))\n",
    "        try:\n",
    "            tweet = api.get_status(tweet_id, tweet_mode='extended')\n",
    "            print(\"Success\")\n",
    "            json.dump(tweet._json, outfile)\n",
    "            outfile.write('\\n')\n",
    "        except tweepy.errors.TweepyException as e:\n",
    "            print(\"Fail\")\n",
    "            fails_dict[tweet_id] = e\n",
    "            pass\n",
    "end = timer()\n",
    "print(end - start)\n",
    "print(fails_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e4583f1",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>34083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>891087950875897856</td>\n",
       "      <td>2537</td>\n",
       "      <td>17171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>890971913173991426</td>\n",
       "      <td>1610</td>\n",
       "      <td>10006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>890729181411237888</td>\n",
       "      <td>15338</td>\n",
       "      <td>55013</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>890609185150312448</td>\n",
       "      <td>3534</td>\n",
       "      <td>23681</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>5937</td>\n",
       "      <td>27004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>890006608113172480</td>\n",
       "      <td>5991</td>\n",
       "      <td>26125</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
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       "      <td>4041</td>\n",
       "      <td>23701</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
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       "      <td>8137</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>3627</td>\n",
       "      <td>22884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>889531135344209921</td>\n",
       "      <td>1840</td>\n",
       "      <td>12908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>889278841981685760</td>\n",
       "      <td>4329</td>\n",
       "      <td>21314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>888917238123831296</td>\n",
       "      <td>3670</td>\n",
       "      <td>24769</td>\n",
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       "      <th>17</th>\n",
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       "      <td>3443</td>\n",
       "      <td>21696</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>888554962724278272</td>\n",
       "      <td>2827</td>\n",
       "      <td>16745</td>\n",
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       "      <th>19</th>\n",
       "      <td>888078434458587136</td>\n",
       "      <td>2828</td>\n",
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      ],
      "text/plain": [
       "              tweet_id  retweet_count  favorite_count\n",
       "0   892420643555336193           6824           32712\n",
       "1   892177421306343426           5141           28291\n",
       "2   891815181378084864           3394           21247\n",
       "3   891689557279858688           7042           35681\n",
       "4   891327558926688256           7555           34083\n",
       "5   891087950875897856           2537           17171\n",
       "6   890971913173991426           1610           10006\n",
       "7   890729181411237888          15338           55013\n",
       "8   890609185150312448           3534           23681\n",
       "9   890240255349198849           5937           27004\n",
       "10  890006608113172480           5991           26125\n",
       "11  889880896479866881           4041           23701\n",
       "12  889665388333682689           8137           40657\n",
       "13  889638837579907072           3627           22884\n",
       "14  889531135344209921           1840           12908\n",
       "15  889278841981685760           4329           21314\n",
       "16  888917238123831296           3670           24769\n",
       "17  888804989199671297           3443           21696\n",
       "18  888554962724278272           2827           16745\n",
       "19  888078434458587136           2828           18465"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the json data retrieved from the Twitter API\n",
    "\n",
    "tweets_data = []\n",
    "tweets_json = []\n",
    "\n",
    "with open('tweet_json.txt', 'r') as json_file:\n",
    "    for line in json_file:\n",
    "        tweets_json.append(json.loads(line))\n",
    "        \n",
    "for tweet in tweets_json:\n",
    "    tweets_data.append({'tweet_id': tweet['id'], \n",
    "                        'retweet_count':tweet['retweet_count'], \n",
    "                        'favorite_count':tweet['favorite_count']})\n",
    "        \n",
    "twitter = pd.DataFrame(tweets_data, columns=['tweet_id', \n",
    "                                            'retweet_count', \n",
    "                                            'favorite_count'])   \n",
    "twitter.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c5cbee5",
   "metadata": {},
   "source": [
    "## Assess\n",
    "\n",
    "## Visual Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a033acc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
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       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
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       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2356 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0     892420643555336193                    NaN                  NaN   \n",
       "1     892177421306343426                    NaN                  NaN   \n",
       "2     891815181378084864                    NaN                  NaN   \n",
       "3     891689557279858688                    NaN                  NaN   \n",
       "4     891327558926688256                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "2351  666049248165822465                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "2355  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "0     2017-08-01 16:23:56 +0000   \n",
       "1     2017-08-01 00:17:27 +0000   \n",
       "2     2017-07-31 00:18:03 +0000   \n",
       "3     2017-07-30 15:58:51 +0000   \n",
       "4     2017-07-29 16:00:24 +0000   \n",
       "...                         ...   \n",
       "2351  2015-11-16 00:24:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "2355  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "0     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "0     This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1     This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2     This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3     This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4     This is Franklin. He would like you to stop ca...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "2351  Here we have a 1949 1st generation vulpix. Enj...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                          NaN                        NaN   \n",
       "1                          NaN                        NaN   \n",
       "2                          NaN                        NaN   \n",
       "3                          NaN                        NaN   \n",
       "4                          NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2351                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "0     https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1     https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2     https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3     https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4     https://twitter.com/dog_rates/status/891327558...                12   \n",
       "...                                                 ...               ...   \n",
       "2351  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator      name doggo floofer pupper puppo  \n",
       "0                     10   Phineas  None    None   None  None  \n",
       "1                     10     Tilly  None    None   None  None  \n",
       "2                     10    Archie  None    None   None  None  \n",
       "3                     10     Darla  None    None   None  None  \n",
       "4                     10  Franklin  None    None   None  None  \n",
       "...                  ...       ...   ...     ...    ...   ...  \n",
       "2351                  10      None  None    None   None  None  \n",
       "2352                  10         a  None    None   None  None  \n",
       "2353                  10         a  None    None   None  None  \n",
       "2354                  10         a  None    None   None  None  \n",
       "2355                  10      None  None    None   None  None  \n",
       "\n",
       "[2356 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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  {
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   "execution_count": 11,
   "id": "64ad47e6",
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   "outputs": [
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       "\n",
       "[2325 rows x 3 columns]"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e79987ea",
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   "outputs": [
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       "    <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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\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",
       "<p>2075 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "0     666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1     666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2     666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3     666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4     666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "...                  ...                                              ...   \n",
       "2070  891327558926688256  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "2071  891689557279858688  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "2072  891815181378084864  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "2073  892177421306343426  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2074  892420643555336193  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0           1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1           1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2           1         German_shepherd  0.596461    True            malinois   \n",
       "3           1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4           1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "...       ...                     ...       ...     ...                 ...   \n",
       "2070        2                  basset  0.555712    True    English_springer   \n",
       "2071        1             paper_towel  0.170278   False  Labrador_retriever   \n",
       "2072        1               Chihuahua  0.716012    True            malamute   \n",
       "2073        1               Chihuahua  0.323581    True            Pekinese   \n",
       "2074        1                  orange  0.097049   False               bagel   \n",
       "\n",
       "       p2_conf  p2_dog                           p3   p3_conf  p3_dog  \n",
       "0     0.156665    True            Shetland_sheepdog  0.061428    True  \n",
       "1     0.074192    True          Rhodesian_ridgeback  0.072010    True  \n",
       "2     0.138584    True                   bloodhound  0.116197    True  \n",
       "3     0.360687    True           miniature_pinscher  0.222752    True  \n",
       "4     0.243682    True                     Doberman  0.154629    True  \n",
       "...        ...     ...                          ...       ...     ...  \n",
       "2070  0.225770    True  German_short-haired_pointer  0.175219    True  \n",
       "2071  0.168086    True                      spatula  0.040836   False  \n",
       "2072  0.078253    True                       kelpie  0.031379    True  \n",
       "2073  0.090647    True                     papillon  0.068957    True  \n",
       "2074  0.085851   False                       banana  0.076110   False  \n",
       "\n",
       "[2075 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43996c53",
   "metadata": {},
   "source": [
    "## Programmatic Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9100a65a",
   "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": [
    "# General information about the WeRateDogs Twitter archive data\n",
    "\n",
    "archived.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a59f2aa9",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the unique values in the source column\n",
    "\n",
    "archived['source'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae87b2eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2333\n",
       "11        3\n",
       "50        3\n",
       "20        2\n",
       "80        2\n",
       "70        1\n",
       "7         1\n",
       "15        1\n",
       "150       1\n",
       "170       1\n",
       "0         1\n",
       "90        1\n",
       "40        1\n",
       "130       1\n",
       "110       1\n",
       "16        1\n",
       "120       1\n",
       "2         1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the unique values in the rating denominator column\n",
    "\n",
    "archived['rating_denominator'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b96c5ee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      558\n",
       "11      464\n",
       "10      461\n",
       "13      351\n",
       "9       158\n",
       "8       102\n",
       "7        55\n",
       "14       54\n",
       "5        37\n",
       "6        32\n",
       "3        19\n",
       "4        17\n",
       "2         9\n",
       "1         9\n",
       "75        2\n",
       "15        2\n",
       "420       2\n",
       "0         2\n",
       "80        1\n",
       "144       1\n",
       "17        1\n",
       "26        1\n",
       "20        1\n",
       "121       1\n",
       "143       1\n",
       "44        1\n",
       "60        1\n",
       "45        1\n",
       "50        1\n",
       "99        1\n",
       "204       1\n",
       "1776      1\n",
       "165       1\n",
       "666       1\n",
       "27        1\n",
       "182       1\n",
       "24        1\n",
       "960       1\n",
       "84        1\n",
       "88        1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the unique values in the rating numerator column\n",
    "\n",
    "archived['rating_numerator'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e8356641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None        745\n",
       "a            55\n",
       "Charlie      12\n",
       "Cooper       11\n",
       "Lucy         11\n",
       "Oliver       11\n",
       "Tucker       10\n",
       "Penny        10\n",
       "Lola         10\n",
       "Winston       9\n",
       "Bo            9\n",
       "Sadie         8\n",
       "the           8\n",
       "Daisy         7\n",
       "Buddy         7\n",
       "Toby          7\n",
       "an            7\n",
       "Bailey        7\n",
       "Leo           6\n",
       "Oscar         6\n",
       "Stanley       6\n",
       "Rusty         6\n",
       "Bella         6\n",
       "Dave          6\n",
       "Jack          6\n",
       "Koda          6\n",
       "Scout         6\n",
       "Jax           6\n",
       "Milo          6\n",
       "Louis         5\n",
       "Phil          5\n",
       "Sammy         5\n",
       "Gus           5\n",
       "Chester       5\n",
       "Alfie         5\n",
       "Sunny         5\n",
       "Bentley       5\n",
       "very          5\n",
       "Oakley        5\n",
       "Finn          5\n",
       "George        5\n",
       "Larry         5\n",
       "Clarence      4\n",
       "Loki          4\n",
       "Walter        4\n",
       "Reggie        4\n",
       "Hank          4\n",
       "Scooter       4\n",
       "Duke          4\n",
       "quite         4\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the unique values in the name column\n",
    "\n",
    "archived['name'].value_counts().head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "68e703c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doggo  floofer  pupper  puppo\n",
       "None   None     None    None     1976\n",
       "                pupper  None      245\n",
       "doggo  None     None    None       83\n",
       "None   None     None    puppo      29\n",
       "doggo  None     pupper  None       12\n",
       "None   floofer  None    None        9\n",
       "doggo  None     None    puppo       1\n",
       "       floofer  None    None        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the unique values in the dog stages column\n",
    "\n",
    "archived[['doggo', 'floofer', 'pupper','puppo']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "67afb7eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicated rows in the archived data\n",
    "\n",
    "archived['tweet_id'].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dbaf5307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                         0\n",
       "in_reply_to_status_id         2278\n",
       "in_reply_to_user_id           2278\n",
       "timestamp                        0\n",
       "source                           0\n",
       "text                             0\n",
       "retweeted_status_id           2175\n",
       "retweeted_status_user_id      2175\n",
       "retweeted_status_timestamp    2175\n",
       "expanded_urls                   59\n",
       "rating_numerator                 0\n",
       "rating_denominator               0\n",
       "name                             0\n",
       "doggo                            0\n",
       "floofer                          0\n",
       "pupper                           0\n",
       "puppo                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for null values in the archived data\n",
    "\n",
    "archived.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7643be2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2325 entries, 0 to 2324\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   tweet_id        2325 non-null   int64\n",
      " 1   retweet_count   2325 non-null   int64\n",
      " 2   favorite_count  2325 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 54.6 KB\n"
     ]
    }
   ],
   "source": [
    "# general information about the Twitter API data\n",
    "\n",
    "twitter.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4b145197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicated rows in the Twitter API data\n",
    "\n",
    "twitter.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "92cd416e",
   "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": [
    "# general information about the images data\n",
    "\n",
    "images.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5de9c5c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicated rows in the images data\n",
    "\n",
    "images.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9839c5",
   "metadata": {},
   "source": [
    "#### Quality Issues\n",
    "\n",
    "#### `Archived` table\n",
    "\n",
    "- Erroneous data type for timestamp and Tweet id\n",
    "- Drop extraneous columns (Retweets and reply columns) and rows. We are only interested in the original tweets.\n",
    "- Source links should be shortened\n",
    "- Rating denominator has values greater than 10\n",
    "- Rating numerator has large values like 960 and 1776\n",
    "- Name column contains wrong names like a, an, the, just, very, quite etc\n",
    "- Remove rows without dog images\n",
    "- Drop null values\n",
    "\n",
    "#### `Twitter API` table\n",
    "- Tweet id should be a string and not integer\n",
    "- Missing data. The data has 33 rows less than the archived data. \n",
    "\n",
    "#### `Images` table\n",
    "- Tweet id should be a string and not integer\n",
    "- Missing data. The data has 281 rows less than the archived data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9274d6d",
   "metadata": {},
   "source": [
    "#### Tidiness Issues\n",
    "\n",
    "- Dog stages in the archived table should be in one column and not four columns\n",
    "- Multiple columns in the Images table for dog breeds and confidence predictions\n",
    "- Twitter API and Images tables should be part of archived table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e74cf00",
   "metadata": {},
   "source": [
    "## Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6f55de1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of the datasets before cleaning\n",
    "\n",
    "archived_clean = archived.copy()\n",
    "twitter_clean = twitter.copy()\n",
    "images_clean = images.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "177052b5",
   "metadata": {},
   "source": [
    "### Quality: Erroneous data types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fe44200",
   "metadata": {},
   "source": [
    "#### `Archived` table: Erroneous data type for timestamp and Tweet id\n",
    "#### `Twitter API` table: Tweet id should be a string and not integer\n",
    "#### `Images` table: Tweet id should be a string and not integer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dbf911a",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Convert the tweet_id columns in each table to string data type using astype() and convert the timestamp column in archived table using to_datetime()."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79298d9",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5988f84a",
   "metadata": {},
   "outputs": [],
   "source": [
    "archived_clean['timestamp'] = pd.to_datetime(archived_clean['timestamp'])\n",
    "archived_clean['tweet_id'] = archived_clean['tweet_id'].astype(str)\n",
    "twitter_clean['tweet_id'] = twitter_clean['tweet_id'].astype(str)\n",
    "images_clean['tweet_id'] = images_clean['tweet_id'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b25125f9",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dcb7d83e",
   "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   object             \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   datetime64[ns, UTC]\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: datetime64[ns, UTC](1), float64(4), int64(2), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "archived_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "582e0779",
   "metadata": {},
   "source": [
    "### Tidiness"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57bce4fb",
   "metadata": {},
   "source": [
    "#### Dog stages in the `Archived table` should be in one column and not four columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a39e34de",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "Replace the None values in the dog stage columns with a space, add all the dog stage columns together and save the result into a new column called dog_stage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "432dd0fb",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5bee981a",
   "metadata": {},
   "outputs": [],
   "source": [
    "stage = ['doggo', 'floofer', 'pupper','puppo']\n",
    "\n",
    "for stg in stage:\n",
    "    archived_clean[stg].replace('None', \"\", inplace = True)\n",
    "\n",
    "archived_clean['dog_stage'] = archived_clean['doggo'] + archived_clean['floofer'] + \\\n",
    "                              archived_clean['pupper'] + archived_clean['puppo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5684d34f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace the space in the created dog_stage column with not available\n",
    "archived_clean['dog_stage'].replace(\"\", 'not available', inplace = True)\n",
    "\n",
    "# split the dog stages having two stages with comma\n",
    "archived_clean.loc[archived_clean['dog_stage'] == 'doggopupper', 'dog_stage'] = 'doggo, pupper'\n",
    "archived_clean.loc[archived_clean['dog_stage'] == 'doggopuppo', 'dog_stage'] = 'doggo, puppo'\n",
    "archived_clean.loc[archived_clean['dog_stage'] == 'doggofloofer', 'dog_stage'] = 'doggo, floofer'\n",
    "\n",
    "# drop the dog stages columns\n",
    "archived_clean.drop(['doggo', 'floofer', 'pupper', 'puppo'], \n",
    "                    axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537c8036",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6061ddf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "not available     1976\n",
       "pupper             245\n",
       "doggo               83\n",
       "puppo               29\n",
       "doggo, pupper       12\n",
       "floofer              9\n",
       "doggo, puppo         1\n",
       "doggo, floofer       1\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archived_clean['dog_stage'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "76e519d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>&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>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>&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>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>&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>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>&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>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "      <td>&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>not available</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                  timestamp  \\\n",
       "0 2017-08-01 16:23:56+00:00   \n",
       "1 2017-08-01 00:17:27+00:00   \n",
       "2 2017-07-31 00:18:03+00:00   \n",
       "3 2017-07-30 15:58:51+00:00   \n",
       "4 2017-07-29 16:00:24+00:00   \n",
       "\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      dog_stage  \n",
       "0                  10   Phineas  not available  \n",
       "1                  10     Tilly  not available  \n",
       "2                  10    Archie  not available  \n",
       "3                  10     Darla  not available  \n",
       "4                  10  Franklin  not available  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archived_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf0d8c3",
   "metadata": {},
   "source": [
    "#### Multiple columns in the `Images table` for dog breeds and confidence predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd09365",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "Create two new columns for dog breeds and confidence, and select the dog breeds and confidence with the highest confidence percentage by creating the conditions to select the first breed prediction ( with the highest confidence percentage) for dog breeds if the p1_dog prediction is True, else select the p2_dog or p3_dog prediction if True."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd1cda14",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2bae3585",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the order of conditions from the highest confidence percentage to the lowest\n",
    "conditions = [(images_clean['p1_dog'] == True),\n",
    "              (images_clean['p2_dog'] == True),\n",
    "              (images_clean['p3_dog'] == True)]\n",
    "\n",
    "# create the order of corresponding breed choices to the conditions above\n",
    "breed_choices = [images_clean['p1'], \n",
    "                 images_clean['p2'],\n",
    "                 images_clean['p3']]\n",
    "    \n",
    "# create the order of corresponding confidence choices to the conditions above\n",
    "confidence_choices = [images_clean['p1_conf'], \n",
    "                      images_clean['p2_conf'], \n",
    "                      images_clean['p3_conf']]\n",
    "\n",
    "# create new dog_breed and confidence columns using the conditions and choice above\n",
    "images_clean['dog_breed'] = np.select(conditions, breed_choices, default='not available')\n",
    "images_clean['confidence'] = np.select(conditions, confidence_choices, default='not available')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287558f9",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a7a5c01f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>dog_breed</th>\n",
       "      <th>confidence</th>\n",
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       "      <th>1551</th>\n",
       "      <td>793135492858580992</td>\n",
       "      <td>https://pbs.twimg.com/media/CwHIg61WIAApnEV.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>bakery</td>\n",
       "      <td>0.737041</td>\n",
       "      <td>False</td>\n",
       "      <td>saltshaker</td>\n",
       "      <td>0.052396</td>\n",
       "      <td>False</td>\n",
       "      <td>teddy</td>\n",
       "      <td>0.046593</td>\n",
       "      <td>False</td>\n",
       "      <td>not available</td>\n",
       "      <td>not available</td>\n",
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       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>670803562457407488</td>\n",
       "      <td>https://pbs.twimg.com/media/CU8sSAvXIAAB1Py.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>basenji</td>\n",
       "      <td>0.344101</td>\n",
       "      <td>True</td>\n",
       "      <td>Ibizan_hound</td>\n",
       "      <td>0.210282</td>\n",
       "      <td>True</td>\n",
       "      <td>toy_terrier</td>\n",
       "      <td>0.196279</td>\n",
       "      <td>True</td>\n",
       "      <td>basenji</td>\n",
       "      <td>0.344101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1506</th>\n",
       "      <td>785533386513321988</td>\n",
       "      <td>https://pbs.twimg.com/media/CubGchjXEAA6gpw.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.436023</td>\n",
       "      <td>True</td>\n",
       "      <td>black-and-tan_coonhound</td>\n",
       "      <td>0.258049</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.145231</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.4360229999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>667188689915760640</td>\n",
       "      <td>https://pbs.twimg.com/media/CUJUk2iWUAAVtOv.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>vacuum</td>\n",
       "      <td>0.335830</td>\n",
       "      <td>False</td>\n",
       "      <td>swab</td>\n",
       "      <td>0.265278</td>\n",
       "      <td>False</td>\n",
       "      <td>toilet_tissue</td>\n",
       "      <td>0.140703</td>\n",
       "      <td>False</td>\n",
       "      <td>not available</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>703382836347330562</td>\n",
       "      <td>https://pbs.twimg.com/media/CcLq7ipW4AArSGZ.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.945664</td>\n",
       "      <td>True</td>\n",
       "      <td>standard_poodle</td>\n",
       "      <td>0.014392</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.012022</td>\n",
       "      <td>True</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.945664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>672995267319328768</td>\n",
       "      <td>https://pbs.twimg.com/media/CVb1mRiWcAADBsE.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.719559</td>\n",
       "      <td>True</td>\n",
       "      <td>boxer</td>\n",
       "      <td>0.166927</td>\n",
       "      <td>True</td>\n",
       "      <td>Boston_bull</td>\n",
       "      <td>0.101354</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.7195590000000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>880221127280381952</td>\n",
       "      <td>https://pbs.twimg.com/media/DDcscbXU0AIfDzs.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.238525</td>\n",
       "      <td>True</td>\n",
       "      <td>meerkat</td>\n",
       "      <td>0.104256</td>\n",
       "      <td>False</td>\n",
       "      <td>clumber</td>\n",
       "      <td>0.052580</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.238525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1908</th>\n",
       "      <td>852672615818899456</td>\n",
       "      <td>https://pbs.twimg.com/media/C9VNNp1XkAEWRFb.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.711235</td>\n",
       "      <td>True</td>\n",
       "      <td>otterhound</td>\n",
       "      <td>0.068235</td>\n",
       "      <td>True</td>\n",
       "      <td>Sussex_spaniel</td>\n",
       "      <td>0.046562</td>\n",
       "      <td>True</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.7112350000000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>675898130735476737</td>\n",
       "      <td>https://pbs.twimg.com/media/CWFFt3_XIAArIYK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.407430</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.077037</td>\n",
       "      <td>True</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.074597</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.40743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>700518061187723268</td>\n",
       "      <td>https://pbs.twimg.com/media/Cbi9dI_UYAAgkyC.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>American_Staffordshire_terrier</td>\n",
       "      <td>0.569501</td>\n",
       "      <td>True</td>\n",
       "      <td>Staffordshire_bullterrier</td>\n",
       "      <td>0.211308</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.121839</td>\n",
       "      <td>True</td>\n",
       "      <td>American_Staffordshire_terrier</td>\n",
       "      <td>0.569501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "1551  793135492858580992  https://pbs.twimg.com/media/CwHIg61WIAApnEV.jpg   \n",
       "265   670803562457407488  https://pbs.twimg.com/media/CU8sSAvXIAAB1Py.jpg   \n",
       "1506  785533386513321988  https://pbs.twimg.com/media/CubGchjXEAA6gpw.jpg   \n",
       "69    667188689915760640  https://pbs.twimg.com/media/CUJUk2iWUAAVtOv.jpg   \n",
       "934   703382836347330562  https://pbs.twimg.com/media/CcLq7ipW4AArSGZ.jpg   \n",
       "374   672995267319328768  https://pbs.twimg.com/media/CVb1mRiWcAADBsE.jpg   \n",
       "2018  880221127280381952  https://pbs.twimg.com/media/DDcscbXU0AIfDzs.jpg   \n",
       "1908  852672615818899456  https://pbs.twimg.com/media/C9VNNp1XkAEWRFb.jpg   \n",
       "506   675898130735476737  https://pbs.twimg.com/media/CWFFt3_XIAArIYK.jpg   \n",
       "909   700518061187723268  https://pbs.twimg.com/media/Cbi9dI_UYAAgkyC.jpg   \n",
       "\n",
       "      img_num                              p1   p1_conf  p1_dog  \\\n",
       "1551        1                          bakery  0.737041   False   \n",
       "265         1                         basenji  0.344101    True   \n",
       "1506        2              miniature_pinscher  0.436023    True   \n",
       "69          1                          vacuum  0.335830   False   \n",
       "934         2                golden_retriever  0.945664    True   \n",
       "374         1                  French_bulldog  0.719559    True   \n",
       "2018        1                       Chihuahua  0.238525    True   \n",
       "1908        1                golden_retriever  0.711235    True   \n",
       "506         1              Labrador_retriever  0.407430    True   \n",
       "909         1  American_Staffordshire_terrier  0.569501    True   \n",
       "\n",
       "                             p2   p2_conf  p2_dog               p3   p3_conf  \\\n",
       "1551                 saltshaker  0.052396   False            teddy  0.046593   \n",
       "265                Ibizan_hound  0.210282    True      toy_terrier  0.196279   \n",
       "1506    black-and-tan_coonhound  0.258049    True       Rottweiler  0.145231   \n",
       "69                         swab  0.265278   False    toilet_tissue  0.140703   \n",
       "934             standard_poodle  0.014392    True  Tibetan_mastiff  0.012022   \n",
       "374                       boxer  0.166927    True      Boston_bull  0.101354   \n",
       "2018                    meerkat  0.104256   False          clumber  0.052580   \n",
       "1908                 otterhound  0.068235    True   Sussex_spaniel  0.046562   \n",
       "506                    malinois  0.077037    True              pug  0.074597   \n",
       "909   Staffordshire_bullterrier  0.211308    True        Chihuahua  0.121839   \n",
       "\n",
       "      p3_dog                       dog_breed          confidence  \n",
       "1551   False                   not available       not available  \n",
       "265     True                         basenji            0.344101  \n",
       "1506    True              miniature_pinscher  0.4360229999999999  \n",
       "69     False                   not available       not available  \n",
       "934     True                golden_retriever            0.945664  \n",
       "374     True                  French_bulldog  0.7195590000000001  \n",
       "2018    True                       Chihuahua            0.238525  \n",
       "1908    True                golden_retriever  0.7112350000000001  \n",
       "506     True              Labrador_retriever             0.40743  \n",
       "909     True  American_Staffordshire_terrier            0.569501  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images_clean.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5902ac7",
   "metadata": {},
   "source": [
    "#### `Twitter API table` and `Images table` should be part of `Archived table`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f94fbb5",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Merge the three tables together using pd.merge and on tweet_id column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fc3facb",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a38e0dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the required columns from the images data into a new dataframe\n",
    "images_clean1 = images_clean[['tweet_id', 'dog_breed', 'confidence']]\n",
    "\n",
    "# Merge the created dataframe above with archived table and twitter api table\n",
    "archived_clean = archived_clean.merge(twitter_clean, on = 'tweet_id', \n",
    "                          how='left').merge(images_clean1, on = 'tweet_id', how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0ca9be0",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "305bec63",
   "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>dog_stage</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>dog_breed</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>&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>not available</td>\n",
       "      <td>6824.0</td>\n",
       "      <td>32712.0</td>\n",
       "      <td>not available</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>&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>not available</td>\n",
       "      <td>5141.0</td>\n",
       "      <td>28291.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>&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>not available</td>\n",
       "      <td>3394.0</td>\n",
       "      <td>21247.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>&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>not available</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>35681.0</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.1680859999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "      <td>&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>not available</td>\n",
       "      <td>7555.0</td>\n",
       "      <td>34083.0</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>891087950875897856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 00:08:17+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a majestic great white breaching ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891087950...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>not available</td>\n",
       "      <td>2537.0</td>\n",
       "      <td>17171.0</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.425595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>890971913173991426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-28 16:27:12+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Jax. He enjoys ice cream so much he gets ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://gofundme.com/ydvmve-surgery-for-jax,ht...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Jax</td>\n",
       "      <td>not available</td>\n",
       "      <td>1610.0</td>\n",
       "      <td>10006.0</td>\n",
       "      <td>Appenzeller</td>\n",
       "      <td>0.341703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>890729181411237888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-28 00:22:40+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>When you watch your owner call another dog a g...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890729181...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>not available</td>\n",
       "      <td>15338.0</td>\n",
       "      <td>55013.0</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.566142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>890609185150312448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-27 16:25:51+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Zoey. She doesn't want to be one of th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890609185...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Zoey</td>\n",
       "      <td>not available</td>\n",
       "      <td>3534.0</td>\n",
       "      <td>23681.0</td>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.4875739999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>890240255349198849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-26 15:59:51+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Cassie. She is a college pup. Studying...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890240255...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>Cassie</td>\n",
       "      <td>doggo</td>\n",
       "      <td>5937.0</td>\n",
       "      <td>27004.0</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.511319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "5  891087950875897856                    NaN                  NaN   \n",
       "6  890971913173991426                    NaN                  NaN   \n",
       "7  890729181411237888                    NaN                  NaN   \n",
       "8  890609185150312448                    NaN                  NaN   \n",
       "9  890240255349198849                    NaN                  NaN   \n",
       "\n",
       "                  timestamp  \\\n",
       "0 2017-08-01 16:23:56+00:00   \n",
       "1 2017-08-01 00:17:27+00:00   \n",
       "2 2017-07-31 00:18:03+00:00   \n",
       "3 2017-07-30 15:58:51+00:00   \n",
       "4 2017-07-29 16:00:24+00:00   \n",
       "5 2017-07-29 00:08:17+00:00   \n",
       "6 2017-07-28 16:27:12+00:00   \n",
       "7 2017-07-28 00:22:40+00:00   \n",
       "8 2017-07-27 16:25:51+00:00   \n",
       "9 2017-07-26 15:59:51+00:00   \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",
       "5  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "6  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "7  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "8  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "9  <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",
       "5  Here we have a majestic great white breaching ...                  NaN   \n",
       "6  Meet Jax. He enjoys ice cream so much he gets ...                  NaN   \n",
       "7  When you watch your owner call another dog a g...                  NaN   \n",
       "8  This is Zoey. She doesn't want to be one of th...                  NaN   \n",
       "9  This is Cassie. She is a college pup. Studying...                  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",
       "5                       NaN                        NaN   \n",
       "6                       NaN                        NaN   \n",
       "7                       NaN                        NaN   \n",
       "8                       NaN                        NaN   \n",
       "9                       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",
       "5  https://twitter.com/dog_rates/status/891087950...                13   \n",
       "6  https://gofundme.com/ydvmve-surgery-for-jax,ht...                13   \n",
       "7  https://twitter.com/dog_rates/status/890729181...                13   \n",
       "8  https://twitter.com/dog_rates/status/890609185...                13   \n",
       "9  https://twitter.com/dog_rates/status/890240255...                14   \n",
       "\n",
       "   rating_denominator      name      dog_stage  retweet_count  favorite_count  \\\n",
       "0                  10   Phineas  not available         6824.0         32712.0   \n",
       "1                  10     Tilly  not available         5141.0         28291.0   \n",
       "2                  10    Archie  not available         3394.0         21247.0   \n",
       "3                  10     Darla  not available         7042.0         35681.0   \n",
       "4                  10  Franklin  not available         7555.0         34083.0   \n",
       "5                  10      None  not available         2537.0         17171.0   \n",
       "6                  10       Jax  not available         1610.0         10006.0   \n",
       "7                  10      None  not available        15338.0         55013.0   \n",
       "8                  10      Zoey  not available         3534.0         23681.0   \n",
       "9                  10    Cassie          doggo         5937.0         27004.0   \n",
       "\n",
       "                  dog_breed          confidence  \n",
       "0             not available       not available  \n",
       "1                 Chihuahua            0.323581  \n",
       "2                 Chihuahua            0.716012  \n",
       "3        Labrador_retriever  0.1680859999999999  \n",
       "4                    basset            0.555712  \n",
       "5  Chesapeake_Bay_retriever            0.425595  \n",
       "6               Appenzeller            0.341703  \n",
       "7                Pomeranian            0.566142  \n",
       "8             Irish_terrier  0.4875739999999999  \n",
       "9                  Pembroke            0.511319  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archived_clean.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "407291bf",
   "metadata": {},
   "source": [
    "### Quality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a2411c",
   "metadata": {},
   "source": [
    "#### `Archived table` Remove rows without dog images\n",
    "#### `Twitter table` Missing data; The data has 33 rows less than the archived data\n",
    "#### `Images table` Missing data; The data has 281 rows less than the archived data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75fb182e",
   "metadata": {},
   "source": [
    "#### Define\n",
    "This has been done with the left join between archived table and twitter table and inner join with the images table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b55058e",
   "metadata": {},
   "source": [
    "#### Code and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d70f6994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for the difference between the merged master data and the images table\n",
    "len(archived_clean['tweet_id']) - len(images_clean['tweet_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f39e0558",
   "metadata": {},
   "source": [
    "#### `Archived table`: Drop extraneous columns (Retweets and reply columns) and rows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba63661b",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Filter out rows with retweets and reply columns that are not null by using loc because we are only interested in the original tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3188b56",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "38ae2508",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only rows where retweet column is null\n",
    "archived_clean = archived_clean.loc[archived_clean['retweeted_status_id'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "37d69aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only rows where reply column is null\n",
    "archived_clean = archived_clean.loc[archived_clean['in_reply_to_status_id'].isnull()]\n",
    "\n",
    "# drop all the retweets and reply columns not needed\n",
    "archived_clean.drop(['in_reply_to_status_id', 'in_reply_to_user_id', \n",
    "                     'retweeted_status_id', 'retweeted_status_user_id', \n",
    "                     'retweeted_status_timestamp'], axis = 1, \n",
    "                    inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b78834be",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7fc23650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1971 entries, 0 to 2074\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            1971 non-null   object             \n",
      " 1   timestamp           1971 non-null   datetime64[ns, UTC]\n",
      " 2   source              1971 non-null   object             \n",
      " 3   text                1971 non-null   object             \n",
      " 4   expanded_urls       1971 non-null   object             \n",
      " 5   rating_numerator    1971 non-null   int64              \n",
      " 6   rating_denominator  1971 non-null   int64              \n",
      " 7   name                1971 non-null   object             \n",
      " 8   dog_stage           1971 non-null   object             \n",
      " 9   retweet_count       1963 non-null   float64            \n",
      " 10  favorite_count      1963 non-null   float64            \n",
      " 11  dog_breed           1971 non-null   object             \n",
      " 12  confidence          1971 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(2), object(8)\n",
      "memory usage: 215.6+ KB\n"
     ]
    }
   ],
   "source": [
    "archived_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e21c6d7",
   "metadata": {},
   "source": [
    "#### `Archived table`: Source links should be shortened"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd7ebc58",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Split the soure column using str.split('>'), select the second substring with 1 and slice out the last three symbols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90e66504",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "be469a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split out the source column using str.split('>')\n",
    "archived_clean['source'] = archived_clean.source.str.split('>').str[1].str[:-3]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f058a83c",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f30fc09c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Twitter for iPhone    1932\n",
       "Twitter Web Client      28\n",
       "TweetDeck               11\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archived_clean['source'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05022324",
   "metadata": {},
   "source": [
    "#### `Archived table`: Rating denominator has values greater than 10\n",
    "#### `Archived table`: Rating numerator has large values like 960 and 1776"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79c7a0a",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Replace the wrong numerator and denominator ratings with the right ratings maually and drop the rows for the ratings that can not be gotten."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a31e1d1c",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "14ca5e44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the number of rows where rating denominator is not equal to 10\n",
    "# and where rating numerator is greater than 15\n",
    "print(archived_clean[archived_clean['rating_denominator'] != 10].count()['tweet_id'])\n",
    "archived_clean[archived_clean['rating_numerator'] > 15].count()['tweet_id']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae860eb4",
   "metadata": {},
   "source": [
    "Just 17/18 rows have incorrect ratings. We can access these rows manually because they are not much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "329b15ed",
   "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>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>820690176645140481</td>\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>416</th>\n",
       "      <td>810984652412424192</td>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>758467244762497024</td>\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>878</th>\n",
       "      <td>740373189193256964</td>\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>926</th>\n",
       "      <td>731156023742988288</td>\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>969</th>\n",
       "      <td>722974582966214656</td>\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>1003</th>\n",
       "      <td>716439118184652801</td>\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>1024</th>\n",
       "      <td>713900603437621249</td>\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>1049</th>\n",
       "      <td>710658690886586372</td>\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>1067</th>\n",
       "      <td>709198395643068416</td>\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>1133</th>\n",
       "      <td>704054845121142784</td>\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>1209</th>\n",
       "      <td>697463031882764288</td>\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>1382</th>\n",
       "      <td>684222868335505415</td>\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>1407</th>\n",
       "      <td>682962037429899265</td>\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>1514</th>\n",
       "      <td>677716515794329600</td>\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>1573</th>\n",
       "      <td>675853064436391936</td>\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>2054</th>\n",
       "      <td>666287406224695296</td>\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": [
       "                tweet_id  \\\n",
       "346   820690176645140481   \n",
       "416   810984652412424192   \n",
       "736   758467244762497024   \n",
       "878   740373189193256964   \n",
       "926   731156023742988288   \n",
       "969   722974582966214656   \n",
       "1003  716439118184652801   \n",
       "1024  713900603437621249   \n",
       "1049  710658690886586372   \n",
       "1067  709198395643068416   \n",
       "1133  704054845121142784   \n",
       "1209  697463031882764288   \n",
       "1382  684222868335505415   \n",
       "1407  682962037429899265   \n",
       "1514  677716515794329600   \n",
       "1573  675853064436391936   \n",
       "2054  666287406224695296   \n",
       "\n",
       "                                                                                                                                                                           text  \\\n",
       "346                                                                         The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd   \n",
       "416   Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx   \n",
       "736                                                                                              Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE   \n",
       "878                                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",
       "926                                                   Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv   \n",
       "969                                                                                                            Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a   \n",
       "1003                                                      This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq   \n",
       "1024                                                                              Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1   \n",
       "1049                                                               Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12   \n",
       "1067                         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",
       "1133                                                                                         Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa   \n",
       "1209                                                                               Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ   \n",
       "1382                               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",
       "1407                                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",
       "1514                                                                                                     IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq   \n",
       "1573                                                                Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw   \n",
       "2054                                   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",
       "346                 84                  70  \n",
       "416                 24                   7  \n",
       "736                165                 150  \n",
       "878                  9                  11  \n",
       "926                204                 170  \n",
       "969                  4                  20  \n",
       "1003                50                  50  \n",
       "1024                99                  90  \n",
       "1049                80                  80  \n",
       "1067                45                  50  \n",
       "1133                60                  50  \n",
       "1209                44                  40  \n",
       "1382               121                 110  \n",
       "1407                 7                  11  \n",
       "1514               144                 120  \n",
       "1573                88                  80  \n",
       "2054                 1                   2  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the max width to none to see the full text for the rating\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "\n",
    "# select the rows where rating denominator is not equal to 10\n",
    "archived_clean[['tweet_id', 'text', 'rating_numerator', \n",
    "                'rating_denominator']][archived_clean['rating_denominator'] != 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec8f681c",
   "metadata": {},
   "source": [
    "Accessed manually, here are the tweet_ids with wrong ratings and beside them are the right ratings from the text columm\n",
    "\n",
    "- 740373189193256964 : 14/10\n",
    "- 722974582966214656 : 13/10\n",
    "- 716439118184652801 : 11/10\n",
    "- 682962037429899265 : 10/10\n",
    "- 666287406224695296 : 9/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "95971a53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace these rows manually with the right ratings for numerator and denominator\n",
    "archived_clean.loc[archived_clean['tweet_id'] == '740373189193256964', 'rating_numerator'] = 14\n",
    "archived_clean.loc[archived_clean['tweet_id'] == '740373189193256964', 'rating_denominator'] = 10\n",
    "\n",
    "archived_clean.loc[archived_clean['tweet_id'] == '722974582966214656', 'rating_numerator'] = 13\n",
    "archived_clean.loc[archived_clean['tweet_id'] == '722974582966214656', 'rating_denominator'] = 10\n",
    "\n",
    "archived_clean.loc[archived_clean['tweet_id'] == '716439118184652801', 'rating_numerator'] = 11\n",
    "archived_clean.loc[archived_clean['tweet_id'] == '716439118184652801', 'rating_denominator'] = 10\n",
    "\n",
    "archived_clean.loc[archived_clean['tweet_id'] == '682962037429899265', 'rating_numerator'] = 10\n",
    "archived_clean.loc[archived_clean['tweet_id'] == '682962037429899265', 'rating_denominator'] = 10\n",
    "\n",
    "archived_clean.loc[archived_clean['tweet_id'] == '666287406224695296', 'rating_numerator'] = 9\n",
    "archived_clean.loc[archived_clean['tweet_id'] == '666287406224695296', 'rating_denominator'] = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa9d3c81",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cf074b6f",
   "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>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>740373189193256964</td>\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>14</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>722974582966214656</td>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>716439118184652801</td>\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>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407</th>\n",
       "      <td>682962037429899265</td>\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>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2054</th>\n",
       "      <td>666287406224695296</td>\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>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  \\\n",
       "878   740373189193256964   \n",
       "969   722974582966214656   \n",
       "1003  716439118184652801   \n",
       "1407  682962037429899265   \n",
       "2054  666287406224695296   \n",
       "\n",
       "                                                                                                                                              text  \\\n",
       "878   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",
       "969                                                                               Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a   \n",
       "1003                         This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq   \n",
       "1407   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",
       "2054      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",
       "878                 14                  10  \n",
       "969                 13                  10  \n",
       "1003                11                  10  \n",
       "1407                10                  10  \n",
       "2054                 9                  10  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check that the correction have been made for these tweet_ids\n",
    "archived_clean.loc[archived_clean['tweet_id'].isin(['740373189193256964', '722974582966214656', \n",
    "                                              '716439118184652801', '682962037429899265',\n",
    "                                              '666287406224695296'])][['tweet_id', 'text', \n",
    "                                                                      'rating_numerator', \n",
    "                                                                      'rating_denominator']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3bb13ca",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4ba5d187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     1959\n",
       "80        2\n",
       "50        2\n",
       "70        1\n",
       "7         1\n",
       "150       1\n",
       "170       1\n",
       "90        1\n",
       "40        1\n",
       "110       1\n",
       "120       1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the unique values in the rating denominator column\n",
    "archived_clean['rating_denominator'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d8fcf440",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove all rows where rating denominator is not equal to 10\n",
    "archived_clean = archived_clean[archived_clean['rating_denominator'] == 10 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "23b15f85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      446\n",
       "10      418\n",
       "11      393\n",
       "13      254\n",
       "9       150\n",
       "8        95\n",
       "7        51\n",
       "14       34\n",
       "5        33\n",
       "6        32\n",
       "3        19\n",
       "4        15\n",
       "2         9\n",
       "1         4\n",
       "27        1\n",
       "75        1\n",
       "0         1\n",
       "1776      1\n",
       "26        1\n",
       "420       1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the unique values in the rating numerator column\n",
    "archived_clean['rating_numerator'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b46bebae",
   "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>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>835152434251116546</td>\n",
       "      <td>When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id  \\\n",
       "250  835152434251116546   \n",
       "\n",
       "                                                                                                                  text  \\\n",
       "250  When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag   \n",
       "\n",
       "     rating_numerator  rating_denominator  \n",
       "250                 0                  10  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the row where the rating numerator column is 0\n",
    "archived_clean[['tweet_id', 'text', 'rating_numerator', \n",
    "                'rating_denominator']][archived_clean['rating_numerator'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91806a43",
   "metadata": {},
   "source": [
    "It is correct with the 0/10 rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "913164bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove all rows where rating numerator is greater than 15\n",
    "archived_clean = archived_clean[archived_clean['rating_numerator'] <= 15 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c5f6be",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9a9ea9fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10    1954\n",
      "Name: rating_denominator, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "12    446\n",
       "10    418\n",
       "11    393\n",
       "13    254\n",
       "9     150\n",
       "8      95\n",
       "7      51\n",
       "14     34\n",
       "5      33\n",
       "6      32\n",
       "3      19\n",
       "4      15\n",
       "2       9\n",
       "1       4\n",
       "0       1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the uniques values in each rating column\n",
    "print(archived_clean['rating_denominator'].value_counts())\n",
    "archived_clean['rating_numerator'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3dcd670",
   "metadata": {},
   "source": [
    "#### `Archived table`: Name column contains some wrong names like a, an, the, just, very, quite etc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1927246a",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Replace all words starting with small letters in the name column with None using loc because the wrong names were taken from the text column and they started with small letters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7990c1f",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "14cd2658",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace the values in name column starting with small letters with None\n",
    "archived_clean.loc[archived_clean['name'] == archived_clean['name'].str.lower(), 'name' ] = 'None'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e549d73",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "217ae173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       609\n",
       "Charlie     11\n",
       "Lucy        10\n",
       "Cooper      10\n",
       "Oliver      10\n",
       "Penny        9\n",
       "Tucker       9\n",
       "Sadie        8\n",
       "Winston      8\n",
       "Daisy        7\n",
       "Toby         7\n",
       "Lola         7\n",
       "Koda         6\n",
       "Jax          6\n",
       "Stanley      6\n",
       "Bo           6\n",
       "Bella        6\n",
       "Milo         5\n",
       "Louis        5\n",
       "Oscar        5\n",
       "Scout        5\n",
       "Rusty        5\n",
       "Bailey       5\n",
       "Dave         5\n",
       "Leo          5\n",
       "Chester      5\n",
       "Buddy        5\n",
       "Chip         4\n",
       "Maggie       4\n",
       "Jerry        4\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archived_clean['name'].value_counts().head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "93ef2085",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1954 entries, 0 to 2074\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            1954 non-null   object             \n",
      " 1   timestamp           1954 non-null   datetime64[ns, UTC]\n",
      " 2   source              1954 non-null   object             \n",
      " 3   text                1954 non-null   object             \n",
      " 4   expanded_urls       1954 non-null   object             \n",
      " 5   rating_numerator    1954 non-null   int64              \n",
      " 6   rating_denominator  1954 non-null   int64              \n",
      " 7   name                1954 non-null   object             \n",
      " 8   dog_stage           1954 non-null   object             \n",
      " 9   retweet_count       1946 non-null   float64            \n",
      " 10  favorite_count      1946 non-null   float64            \n",
      " 11  dog_breed           1954 non-null   object             \n",
      " 12  confidence          1954 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(2), object(8)\n",
      "memory usage: 213.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# general information about the cleaned master data\n",
    "archived_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82ef042d",
   "metadata": {},
   "source": [
    "#### `Archived` table: Drop null values "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d28b56c",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Remove the null values in the retweet and favorite columns using dropna"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93766dbb",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f0bd5f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "archived_clean.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f62cfd57",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a95b37a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1946 entries, 0 to 2074\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            1946 non-null   object             \n",
      " 1   timestamp           1946 non-null   datetime64[ns, UTC]\n",
      " 2   source              1946 non-null   object             \n",
      " 3   text                1946 non-null   object             \n",
      " 4   expanded_urls       1946 non-null   object             \n",
      " 5   rating_numerator    1946 non-null   int64              \n",
      " 6   rating_denominator  1946 non-null   int64              \n",
      " 7   name                1946 non-null   object             \n",
      " 8   dog_stage           1946 non-null   object             \n",
      " 9   retweet_count       1946 non-null   float64            \n",
      " 10  favorite_count      1946 non-null   float64            \n",
      " 11  dog_breed           1946 non-null   object             \n",
      " 12  confidence          1946 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(2), object(8)\n",
      "memory usage: 212.8+ KB\n"
     ]
    }
   ],
   "source": [
    "archived_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3cdc78a",
   "metadata": {},
   "source": [
    "## Storing\n",
    "\n",
    "Store the cleaned master DataFrame in a CSV file named `twitter_archive_master.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2d1b1a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "archived_clean.to_csv('twitter_archive_master.csv', index= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58965ab9",
   "metadata": {},
   "source": [
    "## Analyzing and visualizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f18a20bf",
   "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>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>dog_breed</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>not available</td>\n",
       "      <td>6824.0</td>\n",
       "      <td>32712.0</td>\n",
       "      <td>not available</td>\n",
       "      <td>not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421306343426/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>not available</td>\n",
       "      <td>5141.0</td>\n",
       "      <td>28291.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181378084864/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>not available</td>\n",
       "      <td>3394.0</td>\n",
       "      <td>21247.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557279858688/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>not available</td>\n",
       "      <td>7042.0</td>\n",
       "      <td>35681.0</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.1680859999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>not available</td>\n",
       "      <td>7555.0</td>\n",
       "      <td>34083.0</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                  timestamp              source  \\\n",
       "0  892420643555336193  2017-08-01 16:23:56+00:00  Twitter for iPhone   \n",
       "1  892177421306343426  2017-08-01 00:17:27+00:00  Twitter for iPhone   \n",
       "2  891815181378084864  2017-07-31 00:18:03+00:00  Twitter for iPhone   \n",
       "3  891689557279858688  2017-07-30 15:58:51+00:00  Twitter for iPhone   \n",
       "4  891327558926688256  2017-07-29 16:00:24+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                                                                                                                                 text  \\\n",
       "0                                                       This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "2                   This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB   \n",
       "3                                                             This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ   \n",
       "4  This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f   \n",
       "\n",
       "                                                                                                                     expanded_urls  \\\n",
       "0                                                                  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "1                                                                  https://twitter.com/dog_rates/status/892177421306343426/photo/1   \n",
       "2                                                                  https://twitter.com/dog_rates/status/891815181378084864/photo/1   \n",
       "3                                                                  https://twitter.com/dog_rates/status/891689557279858688/photo/1   \n",
       "4  https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator      name      dog_stage  \\\n",
       "0                13                  10   Phineas  not available   \n",
       "1                13                  10     Tilly  not available   \n",
       "2                12                  10    Archie  not available   \n",
       "3                13                  10     Darla  not available   \n",
       "4                12                  10  Franklin  not available   \n",
       "\n",
       "   retweet_count  favorite_count           dog_breed          confidence  \n",
       "0         6824.0         32712.0       not available       not available  \n",
       "1         5141.0         28291.0           Chihuahua            0.323581  \n",
       "2         3394.0         21247.0           Chihuahua            0.716012  \n",
       "3         7042.0         35681.0  Labrador_retriever  0.1680859999999999  \n",
       "4         7555.0         34083.0              basset            0.555712  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the cleaned master data\n",
    "df_archive = pd.read_csv('twitter_archive_master.csv')\n",
    "df_archive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cda2d8e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1946 entries, 0 to 1945\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   tweet_id            1946 non-null   int64  \n",
      " 1   timestamp           1946 non-null   object \n",
      " 2   source              1946 non-null   object \n",
      " 3   text                1946 non-null   object \n",
      " 4   expanded_urls       1946 non-null   object \n",
      " 5   rating_numerator    1946 non-null   int64  \n",
      " 6   rating_denominator  1946 non-null   int64  \n",
      " 7   name                1946 non-null   object \n",
      " 8   dog_stage           1946 non-null   object \n",
      " 9   retweet_count       1946 non-null   float64\n",
      " 10  favorite_count      1946 non-null   float64\n",
      " 11  dog_breed           1946 non-null   object \n",
      " 12  confidence          1946 non-null   object \n",
      "dtypes: float64(2), int64(3), object(8)\n",
      "memory usage: 197.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# general information about the cleaned master data\n",
    "df_archive.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b1f771f",
   "metadata": {},
   "source": [
    "Convert the tweet_id to string data type and timestamp to datetime data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "89e085a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the tweet_id to string data type and timestamp to datetime data type\n",
    "df_archive.tweet_id = df_archive.tweet_id.astype(str)\n",
    "df_archive.timestamp = pd.to_datetime(df_archive.timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e89a7b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1946.00000</td>\n",
       "      <td>1946.0</td>\n",
       "      <td>1946.000000</td>\n",
       "      <td>1946.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.53443</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2201.947585</td>\n",
       "      <td>7521.251799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.17537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3946.177662</td>\n",
       "      <td>11092.160330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>482.750000</td>\n",
       "      <td>1578.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1055.000000</td>\n",
       "      <td>3371.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2520.250000</td>\n",
       "      <td>9364.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14.00000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>68910.000000</td>\n",
       "      <td>140823.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rating_numerator  rating_denominator  retweet_count  favorite_count\n",
       "count        1946.00000              1946.0    1946.000000     1946.000000\n",
       "mean           10.53443                10.0    2201.947585     7521.251799\n",
       "std             2.17537                 0.0    3946.177662    11092.160330\n",
       "min             0.00000                10.0      11.000000       62.000000\n",
       "25%            10.00000                10.0     482.750000     1578.750000\n",
       "50%            11.00000                10.0    1055.000000     3371.500000\n",
       "75%            12.00000                10.0    2520.250000     9364.500000\n",
       "max            14.00000                10.0   68910.000000   140823.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical information about the cleaned master data\n",
    "df_archive.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82e2f582",
   "metadata": {},
   "source": [
    "### What is the distribution of the numerator ratings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c2eff39b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the distribution of the numerator rating using histogram\n",
    "plt.hist(data = df_archive, x = 'rating_numerator')\n",
    "plt.title('Distribution of Dog Ratings');\n",
    "plt.xlabel('Dog Ratings')\n",
    "plt.ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e9a8bc",
   "metadata": {},
   "source": [
    "The dog ratings has a left-tailed distribution. WeRateDog gives out rates between 0 and 14 for the dogs. The rating with the highest frequency is around 11."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8902cab",
   "metadata": {},
   "source": [
    "### Which dog breed is mostly tweeted about?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "256d18a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sns.color_palette()[0]\n",
    "# set the plot order and eliminate the None value by excluding index 0\n",
    "top_15_breeds = df_archive.dog_breed.value_counts().iloc[1:16].index\n",
    "\n",
    "# plot the bar chart for the top 15 dog breeds by tweet counts\n",
    "sns.countplot(data = df_archive, y = 'dog_breed', order = top_15_breeds, \n",
    "              color = base_color)\n",
    "plt.title('Top 15 Dog breeds by the number of Tweets')\n",
    "plt.ylabel('Dog Breeds')\n",
    "plt.xlabel('Number of Tweets');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "961b98af",
   "metadata": {},
   "source": [
    "The dog breed mostly tweeted about by WeRateDog is the Golden Retriever followed by the Labrador Retriever."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a74fc6a6",
   "metadata": {},
   "source": [
    "### Which dog breed has the highest favourite counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7c290f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize the favorite count by dog breed and sort descending\n",
    "breed_count = df_archive.groupby('dog_breed')['favorite_count'].sum().sort_values(ascending=False)\n",
    "\n",
    "# plot the bar chart for the top 15 dog breeds by favourite count\n",
    "plt.barh(breed_count.iloc[1:16].index.values, breed_count.iloc[1:16], color= 'r')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.ticklabel_format(style='plain', axis='x')\n",
    "plt.xticks(rotation = 15)\n",
    "plt.title('Top 15 Dog Breeds by Favourite Count')\n",
    "plt.ylabel('Dog Breeds')\n",
    "plt.xlabel('Favorite Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "425aecec",
   "metadata": {},
   "source": [
    "The top dog breed by favourite count is the Golden Retriever followed by the Labrador Retriever."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96bfa560",
   "metadata": {},
   "source": [
    "### Does WeRateDog numerator ratings influence the favourite count?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5f514d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jeT7lu4Axxl577TUGgL3zzjuNbpO0dOlSBoAtXry42Zj6OvJ5ONR7hjHG/H4/u+aaa1rV1oZoOLIDnHXWWZAkCYFAABdddBGys7Px1ltvQRQTHY9ff/01duzYgUmTJgEALMtyLxdffDFKS0vxxRdfNHv/kUgEs2fPxgknnABRFCGKIjIyMhCNRvH5558fVpv//e9/A0CjHrjLL78cfr8fq1atSjl+2mmn4bjjjnP/9ng8+N73vpcyNPXDH/4Q7777Lu6++2785z//QTweb/KxOY7D+PHjU46dcsophxzmSko+j0kjRoxA3759sXr16sO6/RVXXAFRFN3b/+tf/4JlWbj66qtTXiuPx4ORI0e2auXq8ccfj379+uG9994DkBhGGTp0KCZPnoydO3fim2++ga7rWLt2LS688EL3dv/85z9x/vnno6ioKOWxk/8yX7NmjRuXlZWF8ePHp8SddtppKCwsbNTG1rx+rXHXXXchJycHs2fPbtPtWnLxxRen9AaddNJJABKTxOtLHt+zZw+Aw3udfvaznzU6VlFRgZtvvhl9+vSBKIqQJAl9+/YFgFZ9vpLvm4afpR/+8Ic46aSTGn2WsrOz8aMf/eiQ91tfYWEhJElCdnY2rrjiCgwbNgwvvvhio7jFixfj+9//Pjwej3suq1atOuzvifqP/8Mf/jDlWMPP7Jo1azBkyJCU3kcAuOqqq1L+PuGEE5CdnY3Zs2dj8eLF2L59e6vb0dbXasKECY3arGmaO6ydfO0afidMnDix1W0CgNtuuw2bNm1KuZx55pkAgL///e84++yzkZGR4bb5j3/8Y0p7c3NzMX78eLz44ovuEG8wGMRbb72Fq6++2v0t+fe//w2/34/LLrss5fGT772G77W2aOqz0Rb//Oc/MWTIEJx22mkpn8exY8ce9or/5nTk83Co98yRouHIDvDSSy/hpJNOQm1tLV599VU8++yzuOqqq/Duu+8C+G7exqxZszBr1qwm76OlsgkTJ07EqlWrcN999+EHP/gBMjMzwXEcLr744mYTnUOpqqqCKIqNhiE4jkNhYWGjIcTc3NxG96EoSsrj//73v0fv3r3x6quv4pFHHoHH48HYsWPx2GOPpXSx+3w+eDyeRvelaVqr2l5YWNjksYZtbu3tRVFEbm6ue/vk6/WDH/ygydu3dvjoggsuwPLlywEkhh1Hjx6NoUOHoqCgAO+99x4GDhyIeDyekoSVl5fj7bffbjSskpR8n5SXl6OmpgayLLcYl9Sa1681MjMzce+992LGjBmtTnoPJScnJ+Xv5Dk1dzz5Pmnr6+Tz+RoNGziOgzFjxuDAgQO47777MHToUPj9fjiOg7POOqtVz0/yfdPUisKioqJGie7hrDx87733oKoqqqur8dxzz+H111/HrbfeisWLF7sxixYtwsyZM3HzzTfj17/+NfLy8iAIAu67774jTsJa8/6pqqpC//79G8UVFBSk/K2qKtasWYOHHnoIv/rVrxAMBtGzZ09MnToV9957b7Pv/cN5rRq2Ozl0loxNfg82jGvqO6YlvXv3brIkxRtvvIErrrgCl19+Oe68804UFhZCFEU888wzjeZkXn/99Xj99dexcuVKjB07Fq+88gp0XU9J7quqqlBYWNhoyC4/Px+iKLb6O7ApR7oitry8HF9//fUhv7uakvwHYnKY/VA68nk41HvmSFES1gFOOukk9wN4/vnnw7ZtPP/883jttddw2WWXufNB5syZkzLBsr5BgwY1eTwUCuGf//wn5s6di7vvvts9npxrcLhyc3NhWRYqKytTEjHGGMrKypr9YWuJ3+/H/PnzMX/+fJSXl7u9YuPHj8eOHTsOu60NNVUfqaysDCeccEKrb9+rVy/3b8uyUFVV5X74kq/Xa6+95v4r+3BccMEF+OMf/4iNGzdiw4YNuPfeewEAP/rRj7By5Urs3r0bGRkZKauq8vLycMopp+Chhx5q8j6LiorcuNzcXDfJaygQCBx2uw/lF7/4BZ544gnMnj0bv/jFLxpd7/F4Gk1+Blr+Ej4cbX2dmpprsm3bNnz88cdYsmQJrrnmGvd4W1ZhJd83paWljVaSHThwIGU+WHPtOJRTTz3VvZ/Ro0dj7NixeO655zBlyhT3s/qXv/wFo0aNwjPPPJNy20PNs2ovubm5jRYjAU1/XocOHYqlS5eCMYZPPvkES5YswQMPPACv15vyPVdfe7xWTbW54ee/uTYfjr/85S/o378/Xn311ZTXveEiJAAYO3YsioqK8MILL2Ds2LF44YUXcOaZZ6b0LObm5mLDhg1gjKXcX0VFBSzLct8jyX/kNnyclpKTw3lf1peXlwev19vsgp+Gn4P6zjjjDOTk5OCtt97CwoULD9mWjnweOhoNR3aCRx99FNnZ2bj//vvhOA4GDRqEgQMH4uOPP8YZZ5zR5KW5H02O48AYS5kkDwDPP/88bNtOOdaWjD25Muwvf/lLyvHXX38d0WjUvf5wFRQU4Nprr8VVV12FL774ArFY7Ijur76XX3455e8PPvgAu3fvbvUKlYa3/9vf/gbLstzbjx07FqIo4ptvvmn29Upq6Tm/4IILwHEc7rvvPvA8706Av/DCC7F69WqsXLkS5513Xsq/HIuLi7Ft2zYcf/zxTT5uMgkrLi5GVVUVbNtuMq65pL49yLKMBx98EJs2bUpZ6ZnUr18/fPnllylffFVVVfjggw/atR1teZ2ak/wCb/j5evbZZxvFNvdaJ4cWG36WNm3ahM8///yIP0sNcRyHp556CoIguIl98njD8/jkk08a1Xlq73/ZJ40cORLbtm1rNLy4dOnSZm/DcRxOPfVU/Pa3v0VWVhY+/PDDFmOB1r1WrZUs79HwO+Gvf/3rYd9nfRzHQZbllEShrKys0epIIDGRv6SkBG+++Sb++9//YvPmzY1Wzl5wwQWIRCKNalQlF8ok32sFBQXweDz45JNPUuKaety2aq4Hvbi4GN988w1yc3Ob/Cy2VCxckiTMnj0bO3bswK9//esmYyoqKvC///0PQPqfh8MZRUiinrBOkJ2djTlz5uCuu+7CX//6V0yePBnPPvssfvzjH2Ps2LG49tpr0atXL1RXV+Pzzz/Hhx9+2OSPGZAY/jnvvPPw2GOPIS8vD/369cOaNWvwxz/+sdFKwCFDhgAAnnvuOQQCAXg8HvTv37/JoYTkv6Znz56NcDiMs88+210defrpp6OkpKTN533mmWeiuLgYp5xyCrKzs/H555/jz3/+M4YPH96u9Zg2b96MG264AZdffjn27t2Le+65B7169cItt9zSqtu/8cYbEEURo0ePdldHnnrqqbjiiisAJJKIBx54APfccw++/fZbd55feXk5Nm7c6Pb4AYl/0QPAI488gh//+McQBAGnnHIKZFlGfn4+hgwZghUrVuD88893n4MLL7wQ1dXVqK6uxqJFi1La9sADD2DlypUYMWIEpk+fjkGDBkHTNOzatQvvvPMOFi9ejN69e+PKK6/Eyy+/jIsvvhi33XYbfvjDH0KSJOzbtw+rV6/GT37yE/z0pz9tr6e8kauuugqPP/64O+ReX0lJCZ599llMnjwZU6dORVVVFR599NG2rSBqhba8Ts058cQTcfzxx+Puu+8GYww5OTl4++23sXLlykaxydf6iSeewDXXXANJkjBo0CAMGjQIN954I/7whz+A53n8+Mc/dldH9unTB7fffnu7njcADBw4EDfeeCOefvpprF27Fueccw6Ki4vx61//GnPnzsXIkSPxxRdf4IEHHkD//v1TVu4FAgH07dsXb731Fi644ALk5OS43y1HYsaMGfjTn/6EH//4x3jggQdQUFCAv/71r24veHJ4+J///CeefvppXHLJJRgwYAAYY3jjjTdQU1OD0aNHN3v/bXmtWmvMmDE477zzcNdddyEajeKMM87A//73P/z5z38+7Pusr7i4GG+88QZuueUWXHbZZdi7dy9+/etfo2fPnk3uqnL99dfjkUcewcSJE+H1evHzn/885fqrr74aTz31FK655hrs2rULQ4cOxdq1a7FgwQJcfPHF7tQGjuMwefJk/OlPf8Lxxx+PU089FRs3bmyX5DLZi/nqq69iwIAB8Hg8GDp0KGbMmIHXX38d5513Hm6//XaccsopcBwHe/bswYoVKzBz5kx3nlxT7rzzTnz++eeYO3cuNm7ciIkTJ6JPnz4IhUJ4//338dxzz2H+/Pk4++yz0/48DB06FP/5z3/w9ttvo2fPnggEAq3/h+9hTecnTWpqZVhSPB5nxx13HBs4cCCzLIsxxtjHH3/MrrjiCpafn88kSWKFhYXsRz/6UcqKkKZWR+7bt4/97Gc/Y9nZ2SwQCLCLLrqIbdu2jfXt27fRCo3f/e53rH///kwQhJRVIQ1XpiXbOHv2bNa3b18mSRLr2bMn+8UvfsGCwWBKXN++fdm4ceManWPDVXB33303O+OMM1h2djZTFIUNGDCA3X777ezgwYNuzDXXXMP8fn+j+2pqFQ6aWR25YsUKVlJSwrKyspjX62UXX3wx++qrrxrdZ3OPsWXLFjZ+/HiWkZHBAoEAu+qqq1h5eXmj+DfffJOdf/75LDMzkymKwvr27csuu+wy9t5777kxuq6zG264gfXo0YNxHNdoZdntt9/OALCHHnoo5b4HDhzIALBPPvmk0eNWVlay6dOns/79+zNJklhOTg4bNmwYu+eee1gkEnHjTNNkjz/+ODv11FOZx+NhGRkZ7MQTT2Q33XRTyvPR2tevOai3OrK+FStWuCvBGn4GXnzxRXbSSScxj8fDBg8ezF599dVmV0c+9thjKbdNfgb+/ve/pxxv7vPWmtepufcdY4xt376djR49mgUCAZadnc0uv/xytmfPniZXx82ZM4cVFRUxnudTPqe2bbNHHnmEfe9732OSJLG8vDw2efJktnfv3pTbjxw5kp188slNtqMpyfdsZWVlo+vKy8tZRkYGO//88xljiffirFmzWK9evZjH42Hf//732ZtvvtnkZ/+9995jp59+OlMUhQFwv0eaWx3ZVJubut9t27axCy+8kHk8HpaTk8OmTJnCXnzxRQbAXVG7Y8cOdtVVV7Hjjz+eeb1epqoq++EPf8iWLFlyyOejta9Vc89bU+dXU1PDrr/+epaVlcV8Ph8bPXo027FjR5tWRzZ8D9f38MMPs379+jFFUdhJJ53E/t//+39Nft8ljRgxggFgkyZNavL6qqoqdvPNN7OePXsyURRZ37592Zw5c5imaSlxoVCI3XDDDaygoID5/X42fvx4tmvXrlY/V/Wvq2/Xrl1szJgxLBAIMAAp74FIJMLuvfdeNmjQICbLMlNVlQ0dOpTdfvvtrKysrNnnqL633nqLjRs3jvXo0YOJosiys7PZ+eefzxYvXsx0Xe/056Gp98zWrVvZ2WefzXw+HwPQqu/RJI4xxg47/SMkjZYsWYLrrrsOmzZtOir2ZSPkWHDjjTfilVdeQVVVVbMLSQg5VtBwJCGEkA7xwAMPoKioCAMGDEAkEsE///lPPP/887j33nspASMElIQRQgjpIJIk4bHHHsO+fftgWRYGDhyIRYsW4bbbbkt30wjpEmg4khBCCCEkDahEBSGEEEJIGlASRgghhBCSBpSEEUIIIYSkAU3M72SO4+DAgQMIBAJHvC0EIYQQQjoHYwy1tbUoKipq9Z7Bh0JJWCc7cOAA+vTpk+5mEEIIIeQw7N27t9G+sIeLkrBOltwTcu/eve2+bQshhBBCOkY4HEafPn2a3dv5cFAS1smSQ5CZmZmUhBFCCCHdTHtOJaKJ+YQQQgghaUBJGCGEEEJIGlASRgghhBCSBpSEEUIIIYSkASVhhBBCCCFpQEkYIYQQQkgaUBJGCCGEEJIGlIQRQgghhKQBJWGEEEIIIWlAFfMJIYQQckR0ywZjAMcBiiikuzndBiVhhBBCCDksmmkjFDMR0U04DOA5IEORoPokeCRKxg6FkjBCCCGEtJlm2igPadBtBz5ZgMhzsByGkGZCM20UqB5KxA4hrXPC3n//fYwfPx5FRUXgOA5vvvlms7E33XQTOI7D7373u5Tjuq7j1ltvRV5eHvx+PyZMmIB9+/alxASDQZSUlEBVVaiqipKSEtTU1KTE7NmzB+PHj4ff70deXh6mT58OwzBSYj799FOMHDkSXq8XvXr1wgMPPADG2JE8BYQQQki3FIqZ0G0HqleCJPDgOA6SwEP1StBtB6GYme4mdnlpTcKi0ShOPfVUPPnkky3Gvfnmm9iwYQOKiooaXTdjxgwsW7YMS5cuxdq1axGJRFBcXAzbtt2YiRMnYuvWrVi+fDmWL1+OrVu3oqSkxL3etm2MGzcO0WgUa9euxdKlS/H6669j5syZbkw4HMbo0aNRVFSETZs24Q9/+AMef/xxLFq0qB2eCUIIIaT70C0bEd2ET266p8snC4joJnTLbvJ6Uod1EQDYsmXLGh3ft28f69WrF9u2bRvr27cv++1vf+teV1NTwyRJYkuXLnWP7d+/n/E8z5YvX84YY2z79u0MAFu/fr0bs27dOgaA7dixgzHG2DvvvMN4nmf79+93Y1555RWmKAoLhUKMMcaefvpppqoq0zTNjVm4cCErKipijuO0+jxDoRAD4N4vIYQQ0t3EDYt9WRZmpTUxVhaKN7qU1sTYl2VhFjesdDe13XTE73eXLlHhOA5KSkpw55134uSTT250/ZYtW2CaJsaMGeMeKyoqwpAhQ/DBBx8AANatWwdVVXHmmWe6MWeddRZUVU2JGTJkSEpP29ixY6HrOrZs2eLGjBw5EoqipMQcOHAAu3btavYcdF1HOBxOuRBCCCHdGcclJuFbTtNTciyHgecScaR5XToJe+SRRyCKIqZPn97k9WVlZZBlGdnZ2SnHCwoKUFZW5sbk5+c3um1+fn5KTEFBQcr12dnZkGW5xZjk38mYpixcuNCdi6aqKvr06dPSKRNCCCFdniIKyFAkxIymhxtjho0MRaJyFYfQZZOwLVu24IknnsCSJUvAtTGVZoyl3Kap27dHDKublN9S++bMmYNQKORe9u7d2/oTIYQQQroo1SdBEXiE4iZM2wFjDKbtIBQ3oQg8VJ+U7iZ2eV02Cfvvf/+LiooKHHfccRBFEaIoYvfu3Zg5cyb69esHACgsLIRhGAgGgym3raiocHupCgsLUV5e3uj+KysrU2Ia9mYFg0GYptliTEVFBQA06iGrT1EUZGZmplwIIYSQ7s4jCShQPVA9EnTTRq1mQTdtqB6JylO0UpdNwkpKSvDJJ59g69at7qWoqAh33nkn/vWvfwEAhg0bBkmSsHLlSvd2paWl2LZtG0aMGAEAGD58OEKhEDZu3OjGbNiwAaFQKCVm27ZtKC0tdWNWrFgBRVEwbNgwN+b9999PKVuxYsUKFBUVuUkhIYQQcixJJmK9c3zok+ND7xwfJWBtkNZirZFIBF9//bX7986dO7F161bk5OTguOOOQ25ubkq8JEkoLCzEoEGDAACqqmLKlCmYOXMmcnNzkZOTg1mzZmHo0KG48MILAQAnnXQSLrroIkydOhXPPvssAODGG29EcXGxez9jxozB4MGDUVJSgsceewzV1dWYNWsWpk6d6vZcTZw4EfPnz8e1116LX/3qV/jqq6+wYMEC3H///W0eLiWEEEKOJjT36zC12zrLw7B69WoGoNHlmmuuaTK+YYkKxhiLx+Ns2rRpLCcnh3m9XlZcXMz27NmTElNVVcUmTZrEAoEACwQCbNKkSSwYDKbE7N69m40bN455vV6Wk5PDpk2bllKOgjHGPvnkE3buuecyRVFYYWEhmzdvXpvKUzBGJSoIIYSQ7qgjfr85xqjke2cKh8NQVRWhUIjmhxFCCCHdREf8fnfZOWGEEEIIIUczSsIIIYQQQtKAkjBCCCGEkDSgJIwQQgghJA0oCSOEEEIISQNKwgghhBBC0oCSMEIIIYSQNKAkjBBCCCEkDSgJI4QQQghJA0rCCCGEEELSgJIwQgghhJA0oCSMEEIIISQNKAkjhBBCCEkDSsIIIYQQQtKAkjBCCCGEkDSgJIwQQgghJA0oCSOEEEIISQNKwgghhBBC0oCSMEIIIYSQNKAkjBBCCCEkDSgJI4QQQghJA0rCCCGEEELSgJIwQgghhJA0oCSMEEIIISQNKAkjhBBCCEkDSsIIIYQQQtKAkjBCCCGEkDSgJIwQQgghJA0oCSOEEEIISQNKwgghhBBC0oCSMEIIIYSQNKAkjBBCCCEkDSgJI4QQQghJA0rCCCGEEELSgJIwQgghhJA0oCSMEEIIISQN0pqEvf/++xg/fjyKiorAcRzefPNN9zrTNDF79mwMHToUfr8fRUVFuPrqq3HgwIGU+9B1Hbfeeivy8vLg9/sxYcIE7Nu3LyUmGAyipKQEqqpCVVWUlJSgpqYmJWbPnj0YP348/H4/8vLyMH36dBiGkRLz6aefYuTIkfB6vejVqxceeOABMMba9TkhhBBCuhLdsqGZNnTLTndTjjppTcKi0ShOPfVUPPnkk42ui8Vi+PDDD3Hffffhww8/xBtvvIEvv/wSEyZMSImbMWMGli1bhqVLl2Lt2rWIRCIoLi6GbX/3Zpk4cSK2bt2K5cuXY/ny5di6dStKSkrc623bxrhx4xCNRrF27VosXboUr7/+OmbOnOnGhMNhjB49GkVFRdi0aRP+8Ic/4PHHH8eiRYs64JkhhBBC0kszbZSHNOyrjmFvdQz7qmMoD2nQTErG2g3rIgCwZcuWtRizceNGBoDt3r2bMcZYTU0NkySJLV261I3Zv38/43meLV++nDHG2Pbt2xkAtn79ejdm3bp1DADbsWMHY4yxd955h/E8z/bv3+/GvPLKK0xRFBYKhRhjjD399NNMVVWmaZobs3DhQlZUVMQcx2n1eYZCIQbAvV9CCCGkq4kbFttVGWFflIXZ3uooK62Jsb3VUfZFWZjtqoywuGGlu4mdriN+v7vVnLBQKASO45CVlQUA2LJlC0zTxJgxY9yYoqIiDBkyBB988AEAYN26dVBVFWeeeaYbc9ZZZ0FV1ZSYIUOGoKioyI0ZO3YsdF3Hli1b3JiRI0dCUZSUmAMHDmDXrl0ddcqEEEJIpwvFTOi2A9UrQRJ4cBwHSeCheiXotoNQzEx3E48K3SYJ0zQNd999NyZOnIjMzEwAQFlZGWRZRnZ2dkpsQUEBysrK3Jj8/PxG95efn58SU1BQkHJ9dnY2ZFluMSb5dzKmKbquIxwOp1wIIYSQrkq3bER0Ez5ZaPJ6nywgops0R6wddIskzDRNXHnllXAcB08//fQh4xlj4DjO/bv+/7dnDKublN/UbZMWLlzoLghQVRV9+vQ5ZPsJIYSQdGEMcBgg8k3/tok8B4cl4siR6fJJmGmauOKKK7Bz506sXLnS7QUDgMLCQhiGgWAwmHKbiooKt5eqsLAQ5eXlje63srIyJaZhb1YwGIRpmi3GVFRUAECjHrL65syZg1Ao5F727t3b2lMnhBBCOh3HATwHWE7TWZblMPBcIo4cmS6dhCUTsK+++grvvfcecnNzU64fNmwYJEnCypUr3WOlpaXYtm0bRowYAQAYPnw4QqEQNm7c6MZs2LABoVAoJWbbtm0oLS11Y1asWAFFUTBs2DA35v33308pW7FixQoUFRWhX79+zZ6DoijIzMxMuRBCCCFdlSIKyFAkxIymhxtjho0MRYIiNj1cSVovrUlYJBLB1q1bsXXrVgDAzp07sXXrVuzZsweWZeGyyy7D5s2b8fLLL8O2bZSVlaGsrMxNhFRVxZQpUzBz5kysWrUKH330ESZPnoyhQ4fiwgsvBACcdNJJuOiiizB16lSsX78e69evx9SpU1FcXIxBgwYBAMaMGYPBgwejpKQEH330EVatWoVZs2Zh6tSpbtI0ceJEKIqCa6+9Ftu2bcOyZcuwYMEC3HHHHS0ORxJCCCHdjeqToAg8QnETpu2AMQbTdhCKm1AEHqpPSncTjw7tts7yMKxevZoBaHS55ppr2M6dO5u8DgBbvXq1ex/xeJxNmzaN5eTkMK/Xy4qLi9mePXtSHqeqqopNmjSJBQIBFggE2KRJk1gwGEyJ2b17Nxs3bhzzer0sJyeHTZs2LaUcBWOMffLJJ+zcc89liqKwwsJCNm/evDaVp2CMSlQQQgjpHuKGxcpq4uzr8jD7sizMvi4Ps7Ka+DFZnoKxjvn95hijqXWdKRwOQ1VVhEIhGpokhBDS5emWDcYSc8CO5SHIjvj9FtvlXgghhBByVDqWE6+O1qUn5hNCCCGEHK0oCSOEEEIISQNKwgghhBBC0oCSMEIIIYSQNKAkjBBCCCEkDSgJI4QQQghJA0rCCCGEEELSgJIwQgghhJA0oCSMEEIIISQNKAkjhBBCCEkDSsIIIYQQQtKAkjBCCCGEkDSgJIwQQgghJA0oCSOEEEIISQNKwgghhBBC0oCSMEIIIYSQNBDT3QBCCCGEdB+6ZYMxgOMARRTS3ZxujZIwQgghhBySZtoIxUxEdBMOA3gOyFAkqD4JHomSscNBSRghhBBCWqSZNspDGnTbgU8WIPIcLIchpJnQTBsFqocSscNAc8IIIYQQ0qJQzIRuO1C9EiSBB8dxkAQeqleCbjsIxcx0N7FboiSMEEIIIc3SLRsR3YRPbrqnyycLiOgmdMvu5JZ1f5SEEUIIIaRZjAEOA0Sea/J6kefgsEQcaRtKwgghhBDSLI5LTMK3nKazLMth4LlEHGkbSsIIIYQQ0ohu2dDMxBBjhiIhZjQ93BgzbGQoEpWrOAy0OpIQQgghrqZKUUgCD8YYQnEzZXVkzLChCDxUn5TuZndLlIQRQgghBEDzpShihg0OgCLy0E0b8brkTPVQnbAjQUkYIYQQQgAkSlHU6hYCHrGuKj4HSeCgenmE4iY8ooAs1UMV89sJJWGEEEIIQThuYNfBCGzGUKuZ4HkOfllEpleEIgpuKYosP/V8tReamE8IIYQc4zTTRmlIQ1iz4JEEZCgiZIFHrWaislaHbtlUiqIDUBJGCCGEHONCMROm7SDTK4LnOLcifsAjwbAchOMWlaLoADQcSQghhBzDkhXxs3wywDjU6iYCwnd9NB5JQNSwAAA9MhSaB9aOqCeMEEIIOYbVr4gf8H43DGnZDhhLjD+G4yZknqNSFO2MesIIIYSQY1j9ivgeSUBeQEFt3ELUSNQJcxwHmR4JhVlempDfzigJI4QQQo5hiiggQ5EQ0kyoXh4eSYBHEpBpJcpU1GomemR4kOmlXrD2RsORhBBCyDFO9UlQhEQtMLNuGJLnOOiWg4Ai0TBkB6GeMEIIIeQY55EEFKged7siqojfOdLaE/b+++9j/PjxKCoqAsdxePPNN1OuZ4xh3rx5KCoqgtfrxahRo/DZZ5+lxOi6jltvvRV5eXnw+/2YMGEC9u3blxITDAZRUlICVVWhqipKSkpQU1OTErNnzx6MHz8efr8feXl5mD59OgzDSIn59NNPMXLkSHi9XvTq1QsPPPBAYtIiIYQQ0s0lE7HeOT70yfGhd44PBaqHErAOlNYkLBqN4tRTT8WTTz7Z5PWPPvooFi1ahCeffBKbNm1CYWEhRo8ejdraWjdmxowZWLZsGZYuXYq1a9ciEomguLgYtv3dbu8TJ07E1q1bsXz5cixfvhxbt25FSUmJe71t2xg3bhyi0SjWrl2LpUuX4vXXX8fMmTPdmHA4jNGjR6OoqAibNm3CH/7wBzz++ONYtGhRBzwzhBBCSHooYmJOGJWi6ASsiwDAli1b5v7tOA4rLCxkDz/8sHtM0zSmqipbvHgxY4yxmpoaJkkSW7p0qRuzf/9+xvM8W758OWOMse3btzMAbP369W7MunXrGAC2Y8cOxhhj77zzDuN5nu3fv9+NeeWVV5iiKCwUCjHGGHv66aeZqqpM0zQ3ZuHChayoqIg5jtPq8wyFQgyAe7+EEEII6fo64ve7y07M37lzJ8rKyjBmzBj3mKIoGDlyJD744AMAwJYtW2CaZkpMUVERhgwZ4sasW7cOqqrizDPPdGPOOussqKqaEjNkyBAUFRW5MWPHjoWu69iyZYsbM3LkSCiKkhJz4MAB7Nq1q9nz0HUd4XA45UIIIYQQ0mWTsLKyMgBAQUFByvGCggL3urKyMsiyjOzs7BZj8vPzG91/fn5+SkzDx8nOzoYsyy3GJP9OxjRl4cKF7lw0VVXRp0+flk+cEEIIIceELpuEJXENNqlijDU61lDDmKbi2yOG1U3Kb6k9c+bMQSgUci979+5tse2EEEIIOTZ02SSssLAQQONepoqKCrcHqrCwEIZhIBgMthhTXl7e6P4rKytTYho+TjAYhGmaLcZUVFQAaNxbV5+iKMjMzEy5EEIIIYR02SSsf//+KCwsxMqVK91jhmFgzZo1GDFiBABg2LBhkCQpJaa0tBTbtm1zY4YPH45QKISNGze6MRs2bEAoFEqJ2bZtG0pLS92YFStWQFEUDBs2zI15//33U8pWrFixAkVFRejXr1/7PwGEEEIIOaqlNQmLRCLYunUrtm7dCiAxGX/r1q3Ys2cPOI7DjBkzsGDBAixbtgzbtm3DtddeC5/Ph4kTJwIAVFXFlClTMHPmTKxatQofffQRJk+ejKFDh+LCCy8EAJx00km46KKLMHXqVKxfvx7r16/H1KlTUVxcjEGDBgEAxowZg8GDB6OkpAQfffQRVq1ahVmzZmHq1Kluz9XEiROhKAquvfZabNu2DcuWLcOCBQtwxx13HHJ4lBBCCCGkkXZbZ3kYVq9ezQA0ulxzzTWMsUSZirlz57LCwkKmKAo777zz2KeffppyH/F4nE2bNo3l5OQwr9fLiouL2Z49e1Jiqqqq2KRJk1ggEGCBQIBNmjSJBYPBlJjdu3ezcePGMa/Xy3Jycti0adNSylEwxtgnn3zCzj33XKYoCissLGTz5s1rU3kKxqhEBSGEENIddcTvN8cYlXzvTOFwGKqqIhQK0fwwQgghpJvoiN/vLjsnjBBCCCHkaEZJGCGEEEJIGlASRgghhBCSBpSEEUIIIYSkASVhhBBCCCFpQEkYIYQQQkgaUBJGCCGEEJIGlIQRQgghhKQBJWGEEEIIIWlASRghhBBCSBpQEkYIIYQQkgaUhBFCCCGEpIGY7gYQQgghpHvQLRuMARwHKKKQ7uZ0e5SEEUIIIaRFmmkjFDMR0U04DOA5IEORoPokeCRKxg4XJWGEEEIIaZZm2igPadBtBz5ZgMhzsByGkGZCM20UqB5KxA5Tm+eECYKAioqKRserqqogCPQiEEIIIUeTUMyEbjtQvRIkgQfHcZAEHqpXgm47CMXMdDex22pzEsYYa/K4ruuQZfmIG0QIIYSQrkG3bER0Ez656U4WnywgopvQLbuTW3Z0aPVw5O9//3sAAMdxeP7555GRkeFeZ9s23n//fZx44ont30JCCCGEpAVjgMMAkeeavF7kOcRZIo60XauTsN/+9rcAEj1hixcvThl6lGUZ/fr1w+LFi9u/hYQQQghJC45LTMK3HAZJaJyIWQ4DzyXiSNu1OgnbuXMnAOD888/HG2+8gezs7A5rFCGEEELSTxEFZCgSQpoJ1dt4BlPMsKF6JCpXcZjavDpy9erVHdEOQgghhHRBqk9KlKiImymrI2OGDUXgofqkdDex22pzEmbbNpYsWYJVq1ahoqICjuOkXP/vf/+73RpHCCGEkPTySAIKVI9bJyxeVydM9VCdsCPV5iTstttuw5IlSzBu3DgMGTIEHA0EE0IIIUc1jyTAowrIsiSqmN+O2pyELV26FH/7299w8cUXd0R7CCGEENJFUeLVvtpcJ0yWZZxwwgkd0RZCCCGEkGNGm5OwmTNn4oknnmi2aCshhBBCCDm0Ng9Hrl27FqtXr8a7776Lk08+GZKUuirijTfeaLfGEUIIIYQcrdqchGVlZeGnP/1pR7SFEEIIIeSY0eYk7IUXXuiIdhBCCCGEHFPaPCeMEEIIIYQcuTb3hPXv37/F2mDffvvtETWIEEIIIeRY0OYkbMaMGSl/m6aJjz76CMuXL8edd97ZXu0ihBBCCDmqHVbF/KY89dRT2Lx58xE3iBBCCCHkWNBuc8J+/OMf4/XXX2+vuyOEEEIIOaq1WxL22muvIScnp73ujhBCCCHkqNbm4cjTTz89ZWI+YwxlZWWorKzE008/3a6NI4QQQgg5WrU5CbvkkktS/uZ5Hj169MCoUaNw4okntle7CCGEEEJS6JYNxgCOOzo2E2/zcOTcuXNTLvfddx9uvvnmDknALMvCvffei/79+8Pr9WLAgAF44IEH4DiOG8MYw7x581BUVASv14tRo0bhs88+S7kfXddx6623Ii8vD36/HxMmTMC+fftSYoLBIEpKSqCqKlRVRUlJCWpqalJi9uzZg/Hjx8Pv9yMvLw/Tp0+HYRjtft6EEEII+Y5m2igPadhXHcPe6hj2VcdQHtKgmXa6m3ZE2twTBgC2bePNN9/E559/Do7jMHjwYEyYMAGC0L5Z6SOPPILFixfjxRdfxMknn4zNmzfjuuuug6qq7irNRx99FIsWLcKSJUvwve99Dw8++CBGjx6NL774AoFAAECirMbbb7+NpUuXIjc3FzNnzkRxcTG2bNnitnnixInYt28fli9fDgC48cYbUVJSgrfffts953HjxqFHjx5Yu3YtqqqqcM0114Axhj/84Q/tet6EEEIISUgmYLrtwCcLEHkOlsMQ0kxopo0C1QOP1E17xVgbffXVV2zgwIHM5/Ox008/nZ122mnM5/OxQYMGsa+//rqtd9eicePGseuvvz7l2KWXXsomT57MGGPMcRxWWFjIHn74Yfd6TdOYqqps8eLFjDHGampqmCRJbOnSpW7M/v37Gc/zbPny5YwxxrZv384AsPXr17sx69atYwDYjh07GGOMvfPOO4znebZ//3435pVXXmGKorBQKNTqcwqFQgxAm25DCCGEHKvKauLsi7IwKwvFG12+KAuzspp4p7SjI36/2zwcOX36dBx//PHYu3cvPvzwQ3z00UfYs2cP+vfvj+nTp7drgnjOOedg1apV+PLLLwEAH3/8MdauXYuLL74YALBz506UlZVhzJgx7m0URcHIkSPxwQcfAAC2bNkC0zRTYoqKijBkyBA3Zt26dVBVFWeeeaYbc9ZZZ0FV1ZSYIUOGoKioyI0ZO3YsdF3Hli1bmj0HXdcRDodTLoQQQgg5NN2yEdFN+OSme7p8soCIbkK3uuewZJuHI9esWYP169enlKPIzc3Fww8/jLPPPrtdGzd79myEQiGceOKJEAQBtm3joYcewlVXXQUAKCsrAwAUFBSk3K6goAC7d+92Y2RZRnZ2dqOY5O3LysqQn5/f6PHz8/NTYho+TnZ2NmRZdmOasnDhQsyfP78tp00IIYQ0crRNSm8NxgCHASLf9HaJIs8hzhJx3VGbe8IURUFtbW2j45FIBLIst0ujkl599VX85S9/wV//+ld8+OGHePHFF/H444/jxRdfTIlruJclY6zF/S2bimkq/nBiGpozZw5CoZB72bt3b4vtIoQQQuo7WieltwbHATwHWE7TWZblMPBcIq47anMSVlxcjBtvvBEbNmwAYwyMMaxfvx4333wzJkyY0K6Nu/POO3H33XfjyiuvxNChQ1FSUoLbb78dCxcuBAAUFhYCQKOeqIqKCrfXqrCwEIZhIBgMthhTXl7e6PErKytTYho+TjAYhGmajXrI6lMUBZmZmSkXQgghpDWSCVhIM6FIAgIeEYokIKSZx0QipogCMhQJMaPp84wZNjIUqdv2DLY5Cfv973+P448/HsOHD4fH44HH48HZZ5+NE044AU888US7Ni4Wi4HnU5soCIJboqJ///4oLCzEypUr3esNw8CaNWswYsQIAMCwYcMgSVJKTGlpKbZt2+bGDB8+HKFQCBs3bnRjNmzYgFAolBKzbds2lJaWujErVqyAoigYNmxYu543IYQQAgChmAnddqB6JUgCD47jIAk8VK8E3XYQipnpbmKHU30SFIFHKG7CtB0wxmDaDkJxE4rAQ/VJ6W7iYWvznLCsrCy89dZb+Prrr/H555+DMYbBgwfjhBNOaPfGjR8/Hg899BCOO+44nHzyyfjoo4+waNEiXH/99QASw4MzZszAggULMHDgQAwcOBALFiyAz+fDxIkTAQCqqmLKlCmYOXMmcnNzkZOTg1mzZmHo0KG48MILAQAnnXQSLrroIkydOhXPPvssgESJiuLiYgwaNAgAMGbMGAwePBglJSV47LHHUF1djVmzZmHq1KnUu0UIIaTdtXZSepbVfXuCWsMjCShQPQjFTER0E3GWGKJUPRJUn9R9y1MAbStREQqFmG3bjY7btt0hJRfC4TC77bbb2HHHHcc8Hg8bMGAAu+eee5iu626M4zhs7ty5rLCwkCmKws477zz26aefptxPPB5n06ZNYzk5Oczr9bLi4mK2Z8+elJiqqio2adIkFggEWCAQYJMmTWLBYDAlZvfu3WzcuHHM6/WynJwcNm3aNKZpWpvOiUpUEEIIaY24YbEvy8KstCbWZHmG0poY+7IszOKGle6mdhrNtFjcsJhmdv45d8TvN8dY69YULFu2DLNnz8bWrVvh8/lSrovFYjj99NPx+OOPY/z48R2QKh49wuEwVFVFKBSiHjRCCCHN0i0b+6pjUCQBktB49pBpO9BNG71zfEd1T1hX0RG/362eE/bMM8/grrvuapSAAYDP58Ps2bPx5JNPtkujCCGEkGPd0T4pnbQhCdu2bRtGjRrV7PXnnXcePv300/ZoEyGEEEJwdE9KJ22YmB8MBmFZVrPXm6bZqAwEIYQQQg7fUT0pnbQ+CevXrx82b96ME088scnrN2/ejL59+7ZbwwghhBCSSMQ8qoAsSzrmKuYf7Vo9HHnppZfinnvuabKoaVlZGe6991787Gc/a9fGEUIIISRBEQV4JIESsKNIq1dH1tbWYvjw4dizZw8mT56MQYMGgeM4fP7553j55ZfRp08frF+/HoFAoKPb3K3R6khCCCGk++mI3+9WD0cGAgH873//w5w5c/Dqq6+687+ys7MxefJkLFiwgBIwQgghhJBWanVPWH2MMRw8eBCMMfTo0eOQm2WT71BPGCGEENL9pLUnrD6O49CjR492aQAhhBBCyLHosJIwQgghhHQO3bJpVeRRipIwQgghpAvSTNutD+bU1QfLULpGfTBKDNsHJWGEEEJIF6OZNspDGnTbgU8WIPIcLIchpJnQTBsFqictiVhXTgy7o1bXCWuKpmnt1Q5CCCGE1AnFTOi2A9UrQRJ4cBwHSeCheiXotoNQzOz0NiUTw5BmQpEEBDwiFElASDNRHtKgmU3vcUma1+YkzHEc/PrXv0avXr2QkZGBb7/9FgBw33334Y9//GO7N5AQQgg5luiWjYhuwic33bPkkwVEdBO61blJT1dMDLu7NidhDz74IJYsWYJHH30Usiy7x4cOHYrnn3++XRtHCCGEJOmWDc20Oz356GyMAQ4DRL7p8k8iz8FhibjO0lUTw+6uzUnYSy+9hOeeew6TJk2CIHz3YpxyyinYsWNHuzaOEEIISQ6D7auOYW91DPuqY0f18BfHJeZaWU7TWZblMPBcIq6zdMXE8GjQ5iRs//79OOGEExoddxwHpkldkYQQQtrPsTgPSREFZCgSYkbT5xYzbGQoUqeuSjRtB6ZlN9umdCSGR4M2J2Enn3wy/vvf/zY6/ve//x2nn356uzSKEEIIAY7deUiqT4Ii8AjFTZi2A8YYTNtBKG5CEXioPqlT2pFMgivCGkJxCzsPRlFZqzcadkxHYng0aHOJirlz56KkpAT79++H4zh444038MUXX+Cll17CP//5z45oIyGEkGNQa+chZVlH34+/RxJQoHrcchDxunIQqqfzykE0LJPRM8uDspCG0po4IrqInqoHAs8jZtidmhgeTdqchI0fPx6vvvoqFixYAI7jcP/99+P73/8+3n77bYwePboj2kgIIeQY1Jp5SPGjeB6SRxLgUQVkWVJaCqNW1GoIayZUX6IXUhJ49MzyIiNuoTwcxwFHQ36m0qmJ4dHmsIq1jh07FmPHjm3vthBCCCGu+hPUJaFxInaszEPq7F4+zbRRGdbxTWUEIs8hZljwyxICXjGRGEoCvAoPw2TIz/Qg4KEesMPV5jlhAwYMQFVVVaPjNTU1GDBgQLs0ihBCCOmKE9SPdskhyBrNgChwUL2J57dWN3GwVncXQvhlEaKQmJ9HDl+bn71du3bBtht/IHRdx/79+9ulUYQQQgjQdSaoHyuSCyGyvDJkUYDNAFHgEfBIMGwHtXELwLHTC9nRWj0c+Y9//MP9/3/9619QVdX927ZtrFq1Cv369WvXxhFCCDm2dYUJ6seK+gshJIGHXxZRq5lub5dXEhA1TGRaIjTTgeqhXsgj1eok7JJLLgEAcByHa665JuU6SZLQr18//OY3v2nXxhFCCCHpnqDe2XTLTst5NlwIkekVoVs2ajUTHkmAwAGGlSgLklmXBJMj0+okzHEcAED//v2xadMm5OXldVijCCGEkIaO5sQLSMzHSvb4OXU9fhlK5/X4NVwIoYgCegQUhOMWooYFw7JhOQyqT0J+wEO9kO2gzasjd+7c2RHtIIQQQo5ZDWtyiTwHy2EIaSY000aB2vFJT3IhREgzoXp591iPgADVklATN5DlkdEnx+fu43m090p2tFYlYb///e9x4403wuPx4Pe//32LsdOnT2+XhhFCCCHHivo7AyRJAgfVm1iUEIqZ8Kgdn+yoPinRIxc3U5LBuGkjU5GQ6RVRHtLS1lt3tOEYO3SZu/79+2Pz5s3Izc1Fv379wDWzHILjOHz77bft3sijSTgchqqqCIVCyMzMTHdzCCGEpJlu2dhXHYMiCU2WfDBtB7ppo3eOr1N6nZobFvVIPGrqksX6CVqyYn5n9NalU0f8freqJ6z+EOSuXbva5YEJIYQQ0vV2BmhuIURyuDTdvXVHkzbVCTNNEwMGDMD27ds7qj2EEELIMaX+hPimpKsmlyImquMrotDqfTwbbuxNWtamJEySJOi63uxwJCGEEELapjvsDNCa3jrnKN7Hs6O0uWL+rbfeikceeQSWZXVEewghhJBjTlffGaCr9tZ1d20uUbFhwwasWrUKK1aswNChQ+H3+1Ouf+ONN9qtcYQQQsixoKvvDNBU+Yr6YoZNFfQPQ5uTsKysLPzsZz/riLYQQgghx6yuvjNAc+Urkqsj091b1x21qkQFaT9UooIQQkh31VJVf45Dl0we20vaSlQ0pbKyEl988QU4jsP3vvc99OjRo10aRAghhJCuqaneOsaQ1u2WurM2T8yPRqO4/vrr0bNnT5x33nk499xzUVRUhClTpiAWi7V7A/fv34/JkycjNzcXPp8Pp512GrZs2eJezxjDvHnzUFRUBK/Xi1GjRuGzzz5LuQ9d13HrrbciLy8Pfr8fEyZMwL59+1JigsEgSkpKoKoqVFVFSUkJampqUmL27NmD8ePHw+/3Iy8vD9OnT4dhGO1+zoQQQkhXlixfwRhQHtIQ0kwokoCAR4QiCQhpJspDGjSTSla0pM1J2B133IE1a9bg7bffRk1NDWpqavDWW29hzZo1mDlzZrs2LhgM4uyzz4YkSXj33Xexfft2/OY3v0FWVpYb8+ijj2LRokV48sknsWnTJhQWFmL06NGora11Y2bMmIFly5Zh6dKlWLt2LSKRCIqLi2Hb3705Jk6ciK1bt2L58uVYvnw5tm7dipKSEvd627Yxbtw4RKNRrF27FkuXLsXrr7/e7udMCCGEdBf1t1uSBB4cx0ESeKheCbrtIBQz093Ero21UW5uLlu9enWj4//+979ZXl5eW++uRbNnz2bnnHNOs9c7jsMKCwvZww8/7B7TNI2pqsoWL17MGGOspqaGSZLEli5d6sbs37+f8TzPli9fzhhjbPv27QwAW79+vRuzbt06BoDt2LGDMcbYO++8w3ieZ/v373djXnnlFaYoCguFQq0+p1AoxAC06TaEEEJIV6OZFvu6PMz2VkdZWSje6LK3Osq+Lg8zzbTS3dR20RG/323uCYvFYigoKGh0PD8/v92HI//xj3/gjDPOwOWXX478/Hycfvrp+H//7/+51+/cuRNlZWUYM2aMe0xRFIwcORIffPABAGDLli0wTTMlpqioCEOGDHFj1q1bB1VVceaZZ7oxZ511FlRVTYkZMmQIioqK3JixY8dC1/WU4dGGdF1HOBxOuRBCCCHdiW7Z0Ew7pSI+FXA9cm1OwoYPH465c+dC0zT3WDwex/z58zF8+PB2bdy3336LZ555BgMHDsS//vUv3HzzzZg+fTpeeuklAEBZWRkANEoKCwoK3OvKysogyzKys7NbjMnPz2/0+Pn5+SkxDR8nOzsbsiy7MU1ZuHChO89MVVX06dOnLU8BIYQQkjaaaaM8pGFfdQx7q2PYVx1z53pRAdcj1+bVkU888QQuuugi9O7dG6eeeio4jsPWrVvh8Xjwr3/9q10b5zgOzjjjDCxYsAAAcPrpp+Ozzz7DM888g6uvvtqNa7iNEmPskFsrNYxpKv5wYhqaM2cO7rjjDvfvcDhMiRghhJAuL5mA6baTUhcspJnQTBsFqocKuB6hNveEDRkyBF999RUWLlyI0047DaeccgoefvhhfPXVVzj55JPbtXE9e/bE4MGDU46ddNJJ2LNnDwCgsLAQABr1RFVUVLi9VoWFhTAMA8FgsMWY8vLyRo9fWVmZEtPwcYLBIEzTbHJ4NklRFGRmZqZcCCGEkK6uNZPuu/p2S13dYc0J83q9mDp1Kn7zm99g0aJFuOGGG+D1etu9cWeffTa++OKLlGNffvkl+vbtCwDo378/CgsLsXLlSvd6wzCwZs0ajBgxAgAwbNgwSJKUElNaWopt27a5McOHD0coFMLGjRvdmA0bNiAUCqXEbNu2DaWlpW7MihUroCgKhg0b1s5nTgghhKSPbtmI6InK+E3xyQIiugmOAwpUD1SPBN20UatZ0M1ED1iB6qE6YYfQ5uHI/Px8XHLJJSgpKcHo0aPB823O41rt9ttvx4gRI7BgwQJcccUV2LhxI5577jk899xzABLDgzNmzMCCBQswcOBADBw4EAsWLIDP58PEiRMBAKqqYsqUKZg5cyZyc3ORk5ODWbNmYejQobjwwgsBJHrXLrroIkydOhXPPvssAODGG29EcXExBg0aBAAYM2YMBg8ejJKSEjz22GOorq7GrFmzMHXqVOrdIoQQclRpzaT7eN2k+3Rst6Rb9tFRnb+tyylff/11dtlllzGv18sKCgrY9OnT2caNG9ttuWZDb7/9NhsyZAhTFIWdeOKJ7Lnnnku53nEcNnfuXFZYWMgURWHnnXce+/TTT1Ni4vE4mzZtGsvJyWFer5cVFxezPXv2pMRUVVWxSZMmsUAgwAKBAJs0aRILBoMpMbt372bjxo1jXq+X5eTksGnTpjFN09p0PlSighBCSFfXVctPxA2LldXE2dflYfZlWZh9XR5mZTVxFjc6vh0d8ft92HtH1tbW4rXXXsMrr7yC1atXo3///pg8eTLuv//+9s0SjzK0dyQhhJDuIFkJX/U2ntcVipvukGNnaW6hQHID8Y4e/uyI3+922cB7+/btmDRpEj755JOUKvSkMUrCCCGEdAdtSXo6Y3gw3Ulhl9rAW9M0/OMf/8Bf//pXLF++HPn5+Zg1a1a7NIoQQggh6eWRBBSoHndz7njd5tyq57vNuTXT7pTNu1u7UCDL6l4lMdqchK1YsQIvv/wy3nzzTQiCgMsuuwz/+te/MHLkyI5oHyGEEELSpKVJ962pI9ZeiVhbFgp0J21Owi655BKMGzcOL774IsaNGwdJohoghBBCyNGsqd6l+nXEkiSBg+pN1A0LxUx41PZJwupX55eExolYd63O3+YkrKysjOYyEUIIIcewzh4eVEThqKzO3+YkrH4CFo/HYZpms9cTQggh5OiTjuFB1Scl5qDFzSYXCnTH6vxtrrQajUYxbdo05OfnIyMjA9nZ2SkXQgghhBzd0rF5d3KhwNFUnb/NSdhdd92Ff//733j66aehKAqef/55zJ8/H0VFRXjppZc6oo2EEEII6UKSw4Mxo+myVDHDRobS/sODHklAll9CXkBBQaYHvXN83TYBAw5jOPLtt9/GSy+9hFGjRuH666/HueeeixNOOAF9+/bFyy+/jEmTJnVEOwkhhBDShXT28GDz5TDQbZOwNveEVVdXo3///gAS87+qq6sBAOeccw7ef//99m0dIYQQQrqkzhweTJbDCGkmFElAwCNCkQSENBPlIQ2a2T0Lxbc5CRswYAB27doFABg8eDD+9re/AUj0kGVlZbVn2wghhBDShSUTsd45PvTJ8XXY8GD9chiSwIPjOEgCD9UrQbcdhGLmoe+kC2pzEnbdddfh448/BgDMmTPHnRt2++23484772z3BhJCCCGka1NEAR5J6JASEa0th6Fb3a83rNV7R3777bfo378/uAZLHfbs2YPNmzfj+OOPx6mnntohjTya0N6RhBBCSOtppo291TEEPGKjHAQAGGOo1Sz0yfF1uw28W90TNnDgQFRWVrp///znP0d5eTmOO+44XHrppZSAEUIIIaTdpaMcRmdpdRLWsMPsnXfeQTQabfcGEUIIIYQkpascRmdo85wwQgghhJDOpPokKEJiT0rTdsAYg2k7CMXNblstH2hDnTCO4xqNxTY1NksIIYSQo4du2WAsMdyXrt6m5CrMZJ2weF2dMNUjQfVJ3bZOWKuTMMYYrr32WiiKAgDQNA0333wz/H5/Stwbb7zRvi0khBBCSJM6MkFqvjhqepIejyTAowrIsqS0J4XtpdVJ2DXXXJPy9+TJk9u9MYQQQgg5tI5OkJLFUXXbgU8W4DiJ4b/KiA7NtNO6VVB3T7zqa3US9sILL3RkOwghhBDSCg0TpOR2QSHNbLcEKVkc1SPxqImZiBoWHIeB5zmE4yY4Djgu13/oOyItoon5hBBCSDfS0dXjk8VRBR6orNVRq5mQBR4ZighZ4GE7DnYdjCAcN9rpjI5dlIQRQggh3URnVI9nDHAYENNtGJaDgCc12cvyydAthmA33SqoK6EkjBBCCOkmkgmSyDddnUDkOTgsEXe4OA6wbAe1mtnksKbtMPhkHnHD7pZbBXUllIQRQggh3UT96vG6ZUMzUxOhw60eX/++FFGATxYRNewmk724aSPTI0PguSNK9kgbJuYTQgghnakr1KfqahRRgCjw2FsVA8/DXRnplyUEvCJ0y4HqaX31+OZWWfoUAR6BR03MQMAjQeA52A5D3LQhCzy8igAO3XOroK6EkjBCCCFdSlerT9WVaKYN3bShWYleKr8iggMQjOk4GNHQM8vb6urxLa2yVAQeBVkeVNUa0C3bfR0CyuEle6RplIQRQgjpMjqj/EJ3FoqZAMfh+PwMhOOWWzpClgTYNoNXElr9/NRfZZkkCRxUb2J7IEUUUJTlRa2emBsmCRwEnkfMsLv1VkFdCSVhhBBCuoxDJQahmAmPemwmYfVXRkoCjx4BAaolwWEMPMeB4wC93ryu1t5XU3yyAN200SOQSHojugnDYuA5u9tvFdSVUBJGCCGkS2ht+YUs69gcBmtqZaQs8vWuZ4i3cmVka1ZZxhnAwKD6EnPEEmUqaH5ee6IkjBBCSJfQ6sTgGF2RV39lpCQ0fo7asjLyUPcV0S1URw1YtgNR4FPm5ZH2QyUqCCGEdAn1E4OmHG75haOFIgrIUCTEjKZrc8UMGxlK63oJW7ovzbSxrzoG22EIeCUEPCIUSUBIM1Ee0qCZVBusvVASRgghpEtozyTjaKX6JChCYn6caTtgLLGxdihutnmyfHP3dSAYBwPQO8fbIdsitVZTddBaOt4d0XAkIYSQLkP1SYkSFXEzZXXk0bwiry310DySgALV45bwiNeVjjicyfJN3Zdd19t4XK6vybZ0xry85kqUeCQemukcVaVLKAkjhBDSZbRnktHVHW49NI8kwKMKyLKkIy5m2/C+DMuBwHPwy02nB/Xn5XVEMd3mSpRURnSEYwYCXgnZfvmoKV1CSRghhJAupT2TjK6qPeqhtedzkryv+vPyHOY0ev4th8GyHVTUajAtp917pJorUQIAUcNGpleGJPDu8e5euoSSMEIIIV3S0ZZ41ddV66EdalukWs2CbtoQBL7J5DHbL0EWhcNKnJsrUWJYDqKGhSyfhKhhItMSU+67O5cu6VYT8xcuXAiO4zBjxgz3GGMM8+bNQ1FREbxeL0aNGoXPPvss5Xa6ruPWW29FXl4e/H4/JkyYgH379qXEBINBlJSUQFVVqKqKkpIS1NTUpMTs2bMH48ePh9/vR15eHqZPnw7DMDrqdAkhhByFWlsPTbfsTp+EXn9bJMNyoIg8ZIFHMKZjZ2UEobgBWUxM0q8/aV8ReeyuimLrniC+qYhgX3WszSspmytR4jAGx2FQRB5OEyVKRJ5r8nh30G2SsE2bNuG5557DKaecknL80UcfxaJFi/Dkk09i06ZNKCwsxOjRo1FbW+vGzJgxA8uWLcPSpUuxdu1aRCIRFBcXw7a/e3NMnDgRW7duxfLly7F8+XJs3boVJSUl7vW2bWPcuHGIRqNYu3Ytli5ditdffx0zZ87s+JMnhBBy1GhVPTTTQVmNhn3VMeytjh1WUnM46m+LlO1XYDoMmuVAlgRwHAfmMGT75ZTbhOMGviitxYFwHGVhDcGYjrBmoTKit6nNzZUo4TkOPM9Bt5yUEiXJBDVqWN22dAnHWNfPHSORCL7//e/j6aefxoMPPojTTjsNv/vd78AYQ1FREWbMmIHZs2cDSPR6FRQU4JFHHsFNN92EUCiEHj164M9//jN+/vOfAwAOHDiAPn364J133sHYsWPx+eefY/DgwVi/fj3OPPNMAMD69esxfPhw7NixA4MGDcK7776L4uJi7N27F0VFRQCApUuX4tprr0VFRQUyMzNbdS7hcBiqqiIUCrX6NoR0BR0xCZeQY5FuJepwKZLgzm+qr1YzURqMIztDQZZParRCtKMmoTfVLsNy3G2RDMvG7qoojs/PgEdKzGbSTBtfltWiJmYg2y9DNx3kBmQwlqjmr4gCemQoKFA9rWpDeUhDSDNThmkBoLJWR2lNHD1Vb2JYNG4haiQWNMR0CwWZXpxQkNGhk/M74ve7W/SE/fKXv8S4ceNw4YUXphzfuXMnysrKMGbMGPeYoigYOXIkPvjgAwDAli1bYJpmSkxRURGGDBnixqxbtw6qqroJGACcddZZUFU1JWbIkCFuAgYAY8eOha7r2LJlS/ufNCFdRHICcWf/i5yQo9Wh6qFVhnUIAo8eAaVT63Q1ty2SRxIgi3xdYsbBtL/ru6mK6AjFDeRmKOAACAIHRRQQ8EgwLAem7bhDq63RXO0yAPDLAnTbQmlNHGHNAM9xYIzB7xEBDt3ye6nLT8xfunQpPvzwQ2zatKnRdWVlZQCAgoKClOMFBQXYvXu3GyPLMrKzsxvFJG9fVlaG/Pz8Rvefn5+fEtPwcbKzsyHLshvTFF3Xoeu6+3c4HG42lpCupj1WcBFCGmuuHlpNzIBp2+iV42vydh05Cb3+cCBjzO0BS+5PyfMc/LIAzbQR8EjQLRsx3YYsJrY1ipoOMhTR7UXzSAIMy4Zm8q2er9VciZIeGQr6ZHuxtzqOmpgGnyzAAZDplZHpTUzU746rJLt0ErZ3717cdtttWLFiBTye5rsyuQYDwYyxRscaahjTVPzhxDS0cOFCzJ8/v8W2ENJVddUVXIR0d80lGwGPBA6tq9PV3hRRAGPA1+URiCIHoW4ull8WkekVoZkOemZ5wRgQipvgOcBmDsAYwpoFj5ToAavf1ojtgANr03yt5kqU6JYNr8yjf54fAs+lJIhA91wl2aWHI7ds2YKKigoMGzYMoihCFEWsWbMGv//97yGKotsz1bAnqqKiwr2usLAQhmEgGAy2GFNeXt7o8SsrK1NiGj5OMBiEaZqNesjqmzNnDkKhkHvZu3dvG58FQtKjLSu4CCFtl0zEeuf40CfHh945PhSqHngkvtP3z9TMxHyvXVVR7A/GcKA6hqhmgjkMwaiObyoiAGPIz/SgQPVA9UiwbFZXYZ+HyHPIy1BSkiLLYTAshoBHPqykSBEFeCTBvW1yuNQnC+4QaX3dcZVkl07CLrjgAnz66afYunWreznjjDMwadIkbN26FQMGDEBhYSFWrlzp3sYwDKxZswYjRowAAAwbNgySJKXElJaWYtu2bW7M8OHDEQqFsHHjRjdmw4YNCIVCKTHbtm1DaWmpG7NixQooioJhw4Y1ew6KoiAzMzPlQkh30JoVXN3tC4+Qrqh+spGO/TOT0w4OBONQRB6DegaQ61dQEzdRGo4DADyiAEVKtDOZPPbv4cfxPQIoyvaiINMD3bJh1c3jsmwHB2t1qB4JPTKVdmnn0bjBe5cejgwEAhgyZEjKMb/fj9zcXPf4jBkzsGDBAgwcOBADBw7EggUL4PP5MHHiRACAqqqYMmUKZs6cidzcXOTk5GDWrFkYOnSoO9H/pJNOwkUXXYSpU6fi2WefBQDceOONKC4uxqBBgwAAY8aMweDBg1FSUoLHHnsM1dXVmDVrFqZOnUqJFTkq1f/CS1asrq87fuER0h109v6ZoZiJWt0EzwMBWYIo8PBkicjOkFEVMSDyHApUDyzbgW7ZbgKoiIK76jGsmeA5DpppwbQZDMtBjl/GCfntt2IxmaAmVk827kOKGTZUT/cZigS6eBLWGnfddRfi8ThuueUWBINBnHnmmVixYgUCgYAb89vf/haiKOKKK65APB7HBRdcgCVLlkAQvnuhXn75ZUyfPt1dRTlhwgQ8+eST7vWCIOD//u//cMstt+Dss8+G1+vFxIkT8fjjj3feyRLSiY7GLzxCuoPO3D8zOe3AIwmo1SwIdT3fhmWjVrOgWTZCmgGbMQgcj7wMJeUzn2yrR0pMT9AsHjw4+D0C8gPtv3DnaNvgvVvUCTuaUJ0w0p00tzqyo+sVEXK4jrZ6dh19PpppY291DLLIoSykQREFOIyhNBSHYTtQBB6McVB9EnTTRp9cH47L8Tf5ue+s577hxueW7cAni8j2y8j0dlwS1hG/392+J4wQ0nE681/khByJhj/M7bmpdDp1dCKZnHYg8Dz8soSDEQ01MRNVUR0eSUSEWZA4HrLIoafqA8A1uyq6s5Le5OpJb5xHMGbCdhgM20FlrYa4YXer15ySMEJIi5pbLk5IV3Gs1bNrzx6n+tMOFIlDTczA/hoNWV4JXplHKGZBdxwETBGKxMHbRcpAaKaNYDRRQifDI3bb15ySMEJIq1DiRbqqY6WeXUf19iXnWR2oiUORBGR5JdhgqIoYUCQBvbM9kEUeuskQ8HRcnbK2OFpec0rCCCGEdFutrWeX7p6bI9Vcb19lRENN3EBP1YNMr3zoO2qCRxKQ7ZdQForDKwlQFAECgMJMEapHht8jwrIdRA0TXoNP+6roo+k1pySMEEJIt9WaenZdoefmSDXs+dFMu24TawvhuIWaqIF+eRmH3SsmiwJyMhQoIo9AREdUt5Dj/66+l1BXFzCm28gPeNKa3DR8zetvMi6LfLd6zSkJI4QQ0q3UnxN1LNSza9jzo5k2DtbqMGwHXklAbgYPzbRRGdEPez5U8nnkeQ49Aonkq1ZLlK4QeQ6aaSOmW8jNkNNeBiLZ1qhhIW44iBoWHIe5Wyx55fT31rUWJWGEEEK6habmRMmCANthqImZbvJQ39FQz65hz09t3IJhO+4+jYwx6JaDgEeEbjmHNR8qtS6ghB4BBeG6njbNTJSlKcj0ojDTCwDudmXpWKyjiAIkgcc3lRG3gr8oJYZnazUTlbU2ju+R0S1ec0rCCCGEdHkN50TZjoPqqInqaAyO44DjOITjJgpUBX5Z7NYFPBuq39vnsLq5WfV6uqy6XiCe445oPlTDQqh5GTJ8hoCauAFF4MFxwL7qKPS6ivhgDBkeCR6J7/RyIAxAsqOrqf92g5FIAJSEEUII6Qbqz4nSLRsHI4a7NU7cSFR6dxxgf3UMuRmJTbA7sp5dZxaFTSkjIfJwGNzK9qbtIBw3kemVIIs8GGOHPR+qfl3A6qiOuGkjqlswLQcHIzoMG/BIHMAAWeThV0QIAgePLHdqaYjkHpW9c3zQjERSmuwZDSiJXryGWyx1VZSEEUII6dIazokKxy0Y1nfDcZwsQrds9MrxIhw3keERUah2zOTxdBWFTfZS1WomHMdBTLdQq1uoiRsQBR4CD1TW8oc1H6p+Qplk2gwHIwYiuonamAVwDgpVHyrDOjTHRp7ogc2AqG7DIznoEVA6rTREcng24BER8HDwajzipg0eQMArQRI41GoWTcwnhBBCjlT9OVGGlZiIXT/hSa7cYwzI8snQTbtD2pHOorDuHo0xAcGoia8qo/BIPLK9MrJ8Enieb/N8qIYJZUy33OfWtJ3E/pFxA7uq4sj2iZAEHeAYFEEAwGDajjtBXrWkTisNkRyejegWwjEL5eE4oqYFAMiQJWT5JKheiSbmE0IIIUcqdU4Ug+MwiNJ3v7B2vRWQHVmeIN0FQpO7V9RqJjTDgiwJUL0SBJ5LbN1jObAsJzFf6xDqJ5QCD2iGjW8O1qIqYkDiOeimA79HgA0OuX4F4B0cjOkAA3pkKNBMG966ZE03bTiMQRH4TikNoYgCRIHHF6VhRE0LAscjyyOB4zhEdAtfV2gY0CODesIIIYSQI1V/TpRXEsDX9UAlS1LETRsBJdH7kuydae9ekK5SIFS3bHAc0D8/w50PpZsOoroFcBxkgcPeYAyKKKBHptJsz1wyofRIPPZVx1BZa6AirMG0HHwd1BDVLfTN9UIWE1sCyYIA02aJeWKGDZ8kguMA02LguMSigM4sB8Ih0RNmOQyZGSIEDrAZIAo8fIoI0z68VaKdjZIwQgghXV5yTlTctCELPOKGBU4W3b8D3sTPWXuXpEjOl0oUBE1/UdiG86E8GoeKsA6fIkD1yuA5IKSZqNEMOIw1OURaP6EsC2moCOswbQdRzQE4QBF56DaP6pgFRXLgl0Q4AEQ+MRk/FDfhEYXE82LbyFBEyGKiN7AzyoHoVmLBQLZfhlO3eXeyTliGIiIvIMOyHARjBrL8Xbs8CSVhhBBCurz6K/ds20Eo5iBu6MjxK8j2J4bkQnGz3UpSNJwvZTsMoZgBnk9MxG+oM3qBdMuGYTmwHeb2BGpGoicqN8MDILFaUhYFZHllxOvOoWFvUDKRcxyGg7U6LMdBMGKgMqJB5nlETRtwGAzLgshLiDHTfbwCVUEo7kA3LVRHgRy/gkyveMTPfVtWmzIG2IzBI/LI8EiwbAYHDDw4SHUrRGvtRJu7+pAkJWGEEEK6heScqCy/hKIsL0KaCdNyYNoMtmO3W0mK5ibgB6MG9lTFcHz+dxPfk8lDrWaiR0bHrcisDOuo1QwwcIhoJoJRAz0ylUY1wzTTRsCTKFfBcWhyiPS7ie0mKms1VNfq2FcTR8SwENVNxA0HFnMgAQDPIzcgAw6H/EwPOACSwEMWBfhkAfkBD8AArygg4BXbnIQezmpTjgMEjgNDIvltyHYYGGMQea7LT86nJIwQQki3oogCFFGA6pM7pF5XcxPwi7K92FkZwb7qOPIzFcR1G1VRDVHdgUfikOlJDJm25wrJUMzAV+URhDQTsshBFniA4xCM6qiK6pAEHj3VxFw4zbQhizwy64ZmmxsiTc6xq6zVsT8YQzCmw6xLgAzLwcGIBtthkEQeXF2C5ZU5SDwHhzH0yvLhh/2zkZ/pAWOJ4U/DslFZa7epZMfhrjZVRAE5fgUVtTqCNXHwPOcOR/rq4mWBR7ZP7tJDkQAlYYQQQrqx9v6RbWkCvkcS0DvHh9KaOL4pr0VYs8BxgFcSwfM89gXj0Ewbx+X62yUR00wbX1dEEIwZyAsoEHkOMcNGVURH3LQQ1W3EDAu65SDHLyHLlxgaTD4nLQ2Rqr66ore1BkyHIW6aCEUt1OomZFGAw5zECklZAC9wyM7wID9TQa9sP3pmeRDwyJBFAd9URBC3LGTIIrJ8cptKdrRmtSnnb3prJI/EgzkMwagJn8wj4BFhOwwHamIQwOGkXmq32CmBkjBCCCGkTsN9GhvKUEQ4TiLB6ZGpwCeJ8MoCLIdBM23sr4nDIwk4Ltd/xG2pqNVQEzPQI6DAYQwR3UZ1RAc4DiLPoyhLRjhmwHYciAKfkoABLS9S4DhA4HnIEo9Y3EKtZkGzExXmBZGDptvgOQ4ZXhleWYRpM2R6ZXhlAR6Zx57qKEL7TJSG4+6K1Vyfgh6ZCvyKmNj8OyY0uzrxUKtNeQ7YVRVFZlSEKPCNethCmokMn4iBcgaqozpCmgkAyPYpid0SvJ23hdKRoCSMEEIIqVO/JlmyBEZ9UcNCVVRHlk9GXoYHpuXAsB3w4BDwSLBsB6U1cRQcYcV+3bIR1RJFZ2tiBuKmjeqoAc20ketXIEs8TNtBlj+RdNTGLVSJBnqqnlbtm8kYoBkW8gMKVK8A3XBg2wyWA8BJbAruE0VkekT0yFAgChyihoXKWg0xw8Q35VEwDuiX64NPERHRTGw/EIJcwaFvXgBeiUNUt+GVBWR6G7ehpWRXM23URA2ENRN5GTL8iuj2sNXEDQhcogyHyHOQRb7uueYhizw8daUzdNOmbYsIIYSQ7qR+TTLVyze6PhQz4TAGn8KjKqIjZtop85GUuuRDN50jSgAYSyQSEc0Ex3NQRAE8OGR4RFTHdHAMEEUB/XK8yM9UUBu3URMzIAt8q/bNNO1EyY0cn4Rag0NuhgKAIaSbsE2AE3gYjg1R5JHpERHWTEQ0C9leCbVxC6bD4JE4VNTqkOImIroF22aIGjYCEQ0DCwI4GDVQVhOvS45S29Ew2a0/t682biFu2nX7YQrgOA6SwEERGXZWRgBwEAUOqkeCwxJ14hgDMjxHvn9mZ6MkjBBCCKknWZMsFDdTJozHDBuSyMEjCqiKGAA4eCQBgpgoFBrRLUR0BoHjwXBkGQDHAbrtAOAg8IlNu0OagbhhIW46qNUt2LYDkeOQG/CgZ5YHssSjV5YXXlk4ZAIoCTwCHglRnwzDARSZh8DzyPHJYA4QNW3opo1sr4zKSGIBgFcWIIg8grU6emRIqDVshDQDEs9DFgT4vCKicWBvMIa8gIJMrwTDYU2WyUgmuxW1Gjhw7ibctu2gVrchCRyy/Qpk8btEuDae2Kid4zlwLPGcSwKPgJDYsqk2nthyqTOLxh6pxmk+IYQQcozSrcQQYLZfguqRoJs2ajULupmYX9U3JwOiwCc2ClfEujIIHMS6QqHheGJuUrvMR2IMnMChJmpg+4Ea7Citxe6qGKKaibhmwrIc7AvFsO7rSnxVHgYPrlUJGJBIULJ8EnIzZPiURG+TadswbQbNSqxyzPVJAJi7aXhAESFyHHgAHllMzCkTBMQMGwADcxi8sgDDdlAV1uGXRWT5JER0031e60sMo5ooDcXBcxz8sgCOAypq44ltmcTvsijdshE1Es+5wHFQJAFavT1CvZKQ2D3AshEzbGQoXbtIaxL1hBFCCDnmNVevqkfA49bcUkQBumW7E88jupWYlM7BHRZLVpU/UnHDRjhuoTwUQ23cRlXEgGYkVi5WRg14BB49s7zom+NDMGrgi9IIvMdJrR6CU0QBfkVEeZiDR+Qh8ADP8QjrBkSeg18W4fdIYEhUys/0isjJUJDllVATNxDRLfhkAXHdRGVYB3MASbBhOg5ihgndYfBIfIs7CWimg0yfjExfYq5d1LABcMgPeCELHAyTAd5EbHIOGQPA8xyyfBJCcTOxAEASICTLa9Tq8CsivHL36GOiJIwQQsgxrTX1qpJxhuUgxy+D5xhqYoniphzHgTEGnuNRlOVBlk8+ovlIyeKsYc1EpkdGhuSgJmYiy+9BRNPhFQVk+iSwug3NFZFH3HAQNy1U1Go4LufQKzOTW0ABDALHwScI6JPrQUSXEDdsBDwiVK+MXL8MryKgR8CDAT0yIIs8dlfF8FUwghy/jFDcgmE7MBwHkpiYvK8qMhQpMTeruaHB5OrILJ8ESeChWhIcxsBziZ0PglEdUcNEppVY8ZmcQ5bYrkhBwJOY7F8TMxHTLeh2YjI/x3mhiAIqa3XEDaddivd2JErCCCGEHNNaqldVWaujtjwCr8y72xdFNBM5GQpUn4KamA7bYRB4Dlk+JdEDw45sPlIoZiJimBBFDswCFFGCTxaQLQow7URvUUSzofE2fCEBHkmAYScm8X9TEQHHuBY3704+BsclCtAeCMYQMWxk+hT0CPCwGIPMcSjM8iHgEcExJDIgAAdrdWR4RGQoIqpqDVi2g1x/ogfOchh6ZnpwXK4PppMoqcHANVkmo+HqyPpzvzK9IjTTQjBqIjfDgSzw4DnOLQ2iSBwqa3VEDQuOw6DbDirCOvIyZPTL87e66GtXQEkYIYSQY1ZL9aqS10U1C/16+BGoK5UQjBooC2k4Pj8Dqve7Hpz22MQ6HDexqyqKUExPbFVkGDB0oCqmw3EcxM1EgpSTKUFkAjSLwXQsBBQR2X4ZpsNa3Ly74Tnvq44ibjrIDcjQLBscBGTIAhQxUZvLshliugmvLGJXZRQ2cxKrMWMebIvUguMBWRBQqycKtvbN84PnOCgij+qogQxZbLJMRkulQBRRQLZfTpTMsBlqNQu2w5CbISOq2dhXHU8MmSoiOJ5DZViDZTvwyYmCrZLANyr62ly9snSjJIwQQsgxq6V6VeF4oqfFp4gQed4tlVB/+6Ljcn1QBD7R83IEm1jrlo24YePb8gj2VEXBGENNzETctMCBg6ZbiJh2YuUiY/B7BehgCIgS4oaDPL8CRRLB2U6Lm3fXP+eYbmFfMA7LYfCKPEzLQa1hwrB5HDQcZHpNBGMmTNuG6pVQFTXgkQTkBTwQeMDrESALHAKKiJxAYgg2HDPh9ybqi0mC0GyP3KFKgdgO0C8vA15ZQDBqIGZb4HgeEcNCTLehekXolgPbSWxYfkJBYgul5ArJJJ8sNLl/ZldBSRghhJC064g9IFujuR6ZWi0xL4nnE5tF1x9eTG5fVBUxUBs33Yruh7OBeP0FAfuq49gTjMGwEtXqbceBwzgw5iBqOYgbFuAkGl0V0eARZZi2g4AiQZYTZRpy6so6NLd5d/1zrowYqI5qOBBMVJyPmxbihg2eY3AYhwyPAFkQ0DNThlfmIWsCwIBI3IQo8pDFRIkOryKCMQ61uokMr4gT8gPwyolYbzMV8YGWS4EodfXOglEDuu0g4JXgOAwCnxiuFAQOeRkyAEDgDfjresHqzyMDmt8/s6ugJIwQQkjaNLcqsbMmVDfskdFMG7VxC8GYjrKwBjCG/IC30Y94hiKCMSA/U3Enjrc1eay/IMB2HBysjYMDg2U72BeMQfUq8Mk8YqYFy2RgtgNFEmDZDGHdgW1b8MkSvDKPcMxC31zukJt3J89ZEnnsPhjFt5UxHIwYMEwbDnNg2Ay1cROSwMEj+OAVeQi8AHACvBJDzLQSdcPExDZFzEkMR6o+CTm2VFcdn4Pt4JDDsh5JQIHqcV//eN3rn0xmK2o1hDUTat3kfc2xwXFcXdkLC7rJoPok8LwJy2EQeS6xgrLeOXf1mmGUhBFCCEmL1qxK7IxELNkjUxHWENUtOCwx0Z4DIEsCwCUmpOcFvhtaS/64e6TW1eVqSihmola3YFgOdlbW4pMDIXglEbppIRgz4ZNF5PgVRDUThmPBZDwCvIBsnwTTthP1uOIW4gZDpt/Gcble5GUo4MC5vV3NJR+qR8K+mjiqIhp48HWlZTkoIgdbERGKG9hdXYtTe+cgpFuwHA48xwEcg+MwaBZDWTgOSeQR0S0UZnrQO9sPnyyiPKzhuGxfq4ZlPVJif8ksS3J7QhkDKsM6vqmM1G1absEvS1AkHnzdeyRZFyzTJ8IvJ0qGJMuF1D/nlvbP7AooCSOEEJIWLa1K7MwJ1ckemVotUavKJyc2pO4R8ABgyM1QUiqyA0f+465bNqqjOqqjOsrCcVSENcQNG8xhCMVtGFZir0hFEuAwBpHjAIEhJ6DAchjAiZAlgOcYRIGDbQPfVERRq1nomeWDwHHone1ttn0xw0bEMBHwyNAtB6btAFyiDpfDGOK6ibjpYHd1DLLEQ+KAAQUZgAPUxA1kKCIy/R5IfGIVZEgz4Y8b8EiJ7ZsOtTqzoWQ7k4l5WDdTtiaq1U3oFg+B46CZdmIj9bper0yvCN2yUVmrIy9DgSwk9tU81P6ZXQElYYQQQjpdS6sSgc6fUM1xgFfm0T/PD4FP9PowsESJCs2EyHOI6Aa8Og/bwRH/uDOWqHFVHoqjLKzDMBObgEd0B4LAgbOAYNRArl+G6TiwGcCBQ3kojqhpIy9DQUGWF1UhHSHNQN9cER6Jg2bYCEZ0ZHolxE0bmmk3mQwZtg3bdhDwCsgVFFRGdIAlNgsPaSZ0y4HDGGK6AY6TYPEcSmt0KDIPgIfNGCK6CZnnwAs88gMeSEJi94D8TKXFuWAtSSbmWV4ZMcNutDWRIgiQRR41MaOuakaiPpsiCsj2yfBIAmo167Dn6HU2SsIIIYR0upZWJQKdP6E62Z5A3RY+ST0CCsLxRFHWiG5DNRly/PIR/7ibtoPqmI691THYYMjJ8IDngf3VcVTHTDCboVY3sHVvYsNw205MSq91AMNwYJoM1bU64pYJzXAS9bIsB5YDZHNAr2wfGNBsb6LEC/BJEqKmDYFLVMiP6CbCugHbsiGLAkSbwbAZHD1RAsNkDuIRCwGPBJ7joHolyCIHnk9sI2Q7DvZWx1CoNt8D15L6ibkk8O4woyQkVk96pcSOBTkZMnTTgcBzMCwGnrOR6RXRJ9sLnucgCXynL/A4XJSEEUII6XQt1YkCEsdth8GwnE75QW2uPYoooEdAgE8WEFAs9M7xutXaj4Qk8IhrNsKaid7ZPog8B58swiML4OMmgpoOw3YQt2wYZmLSeY5fhmUm9nUMawY4XoJlA1l+CUWqB7IkwrRseOREb5HAc832JuYGZPTK8mBHaRgev4SqmIODkThiug2vLIGzbAgyB48iQDMdROJm4vlhgCLx8IsiopqNsOMkVnJaiflbgsCl7OnYFg0T8+QwY8OtiTTDQa8sL7L9MhzGUBu3YFg2Kmr1lIUd3UH32FyJEELIUSW5KjGx+XMqzbSxtyqGUMxAeVjDvuoYykPaYf+4H2l7gMQcKr8ipVR2PxKm7UAQOciCgLhp1yWcNmSRh68uibIZ4OF45AVkZHhEWHUJj2E7KI9o+KYigqBmwrSBsG7Xze1idWUmuCZXC9Y/3+/3y0GmV0ZlrQGJBwQAjgOYtg1F4JDllZDpkeCXBVgcYFoOJJ6DzPPwyTw0y4ZmOfB5BPgVAbzIwWGJlZ3Jjczbon4inGxjj0BiiyLDdhDSEqsgVZ+EAjWxp2coZiJu2VAkAQGPCEUSENLMDn+/tBfqCTtKpKvGDiGEHK6m6kRFdAv7qmNgAI7L9cEvi522YrJ+e0SeA88lNuUORk1ohg3Dn5hnle078uFISeCR51eQ45NhMoa4YSGsJRIpUeDhk0UEIyYsGeBswCvw7tChIPDwMwbdZjANq24Rg4GamAFFEFCQmdjr8lDlGY7PD2DkoHz875sKfFMZRW3cRlw3oTMGnyhAFhm8DlCoKtBMB2CATxHgMCAcTySOisiDYxwEnoMiJFYnVkdNBKNGXbmK1muqgGuyJ1K1EhuHZ3lk9MnxAYC7sjbdCzuORJfuCVu4cCF+8IMfIBAIID8/H5dccgm++OKLlBjGGObNm4eioiJ4vV6MGjUKn332WUqMruu49dZbkZeXB7/fjwkTJmDfvn0pMcFgECUlJVBVFaqqoqSkBDU1NSkxe/bswfjx4+H3+5GXl4fp06fDMIwOOffWSq4k2Vcdw97qWKf8i5EQQtpDclWi6pGgmzZqNQtVEQOyKOD4/AxkKFJdlXoeqleCbjsIxdrew9KW9mT5JBimg91VUWw/UIstu4LYWRmB5TjQTBtVEQ3fVkawuyp6RN+zHAfkBTzomeUBzwGiwEHkAc2yUKsbMCwGWRbglxJbCNVoJgybwauIEDnAsBhMxmAwB7ZtIxyzoYgiBJ5DMG4ibliIGTYylOYXNngkAaf1zcZPTuuDAble8BKPgE9CQcCDItUD1SdCsxlq4zYyFQk8z8O0GKpiRmIjbZ+M/IAHFhi+qohg18EIwnETByNxfFtZi3C87b+Pqk+CIiSSqKhuIW5Yif+aiTb0yFQAtH5hh2517d/CLp2ErVmzBr/85S+xfv16rFy5EpZlYcyYMYhGo27Mo48+ikWLFuHJJ5/Epk2bUFhYiNGjR6O2ttaNmTFjBpYtW4alS5di7dq1iEQiKC4uhm1/9+JMnDgRW7duxfLly7F8+XJs3boVJSUl7vW2bWPcuHGIRqNYu3Ytli5ditdffx0zZ87snCejCckELKSZ3bYrlhBybEsmYr1zfMjPVJDpERNbATWROHT0D6tm2qiJmQDHUKAq8Cs8BJ5BEnnYDgPHAYLAw7BtlNbEUR7WoFuJFYhtbZMiCsjxy+id60O/XD84JzGJviqsw7EBSRCQ45MQ8EvI8kmwGYNuWghFDeg2g+EwWBaDaSbKN9RoGlSvgD45XmiGhb3VsVat4PRIAk4oCKBXdgbyMzw4uUhFzyxPIsEzgUyPgFrNxL6whkyPgP55PvRSPfApiSHTuOkk6phZFuKGg5q4AdNmOFCjYXdVrM2/Qw0T4a8rEgmvYTrIqtf72JqFHc0NxXYlHGNdvYnfqaysRH5+PtasWYPzzjsPjDEUFRVhxowZmD17NoBEr1dBQQEeeeQR3HTTTQiFQujRowf+/Oc/4+c//zkA4MCBA+jTpw/eeecdjB07Fp9//jkGDx6M9evX48wzzwQArF+/HsOHD8eOHTswaNAgvPvuuyguLsbevXtRVFQEAFi6dCmuvfZaVFRUIDMzs1XnEA6HoaoqQqFQq2/TnGQCpjbR5ZvcRLZA9RzRYxBCSGfRTBt7q2MIeMSUFYpJjCU2c+6T4+uQIcndVVEcCMbB84BuOvimshYCxyM3IKMmasJhie1zBI5DVDchCAK+lx+ATxEPq9K/WxNLMxGOG9iwswrb9taAF3hkemSAYzgY0SGCw75gDJppABASVfUdwO8RoEgieCSGN4cPzENAkcBzwKCCTJx6XBYyvfIh21EV0fHapr34qiKE0hodcdNBMBqvSzx5SAIPUQBO75uDLK+M8lAcpaE4fIoE1E2Wz1REyGKikGyh6kGmV0JOhoITCzNxXK6/1a9B/QK+Qt2WUTZjblmQ5HC0btnYVx2DIgnu6sn6TNuBbtrondN0Qn842vP3O6lL94Q1FAqFAAA5OTkAgJ07d6KsrAxjxoxxYxRFwciRI/HBBx8AALZs2QLTNFNiioqKMGTIEDdm3bp1UFXVTcAA4KyzzoKqqikxQ4YMcRMwABg7dix0XceWLVuabbOu6wiHwymX9nC0dMUSQkhSw4nZDXXkFjThuIk9B6OwHCexrY+QqMxuWBa+qYigMqqjKmLgYFjHt1VRbNsfxoe7gtgbrBuW5NDmUYhkL2B+wIMMj4wsrwxR4L/rHczyIcejoCqqI6KbiBo24oYBiwE8Xzd5XeDglRMjINv31SBmmCjM9CDglSC3Mvk4GNGxtyqSmFMsAbplwbQBw7GhGQZsJ1EmY191DN9U1iIY02HaiTph5cE4aqImKiI6oroBB4Aiieip+uCVBJTWxNv0O1S/gG+GIsEri4nktsFwdGsWUrQ0FNtVdJskjDGGO+64A+eccw6GDBkCACgrKwMAFBQUpMQWFBS415WVlUGWZWRnZ7cYk5+f3+gx8/PzU2IaPk52djZkWXZjmrJw4UJ3npmqqujTp09bTrtZR0tXLCGEJKXzhzVRpd2Cv25PSMN2wOp6YA7WmtANBxyfWJkY1y3YNoPFbER1GxHdQk3MhEfiD3veGgfAdhxkeESoXhGaYaFWt8AEBsaAmGlBtwBwPCSeAwcets1QHTOwvyYO3bSwJxjD+m+qsGFnFXYdjKKitnUJYUVIR61pAwzgOA5y3TBmrt8Dv0eGaXMwbAbTchCOGXW9lBxq4xZqNBO2k9h66UAojvJaDX5ZQIZXhF8RETVs6KbTquegrZ0L9eePmXWvl2k7CMXNLl8pP6nbrI6cNm0aPvnkE6xdu7bRdQ27rRljTXZltxTTXNd3W2MamjNnDu644w7373A43C6JWGtq7HTlTUsJIaQpTa2YtBzWYVvQaKaNiloNOw9GEIwaCEYNCDwHWeQQ0W2UhePwCgJsh0NUdyDzAjSTwWKA5QD7g3HkZCiQLQ6KKNRtLt26Sv/Jobda3ULctAAkEiDDdhCK6YgbDsK6A81y4BEFxGwbYAyyzCOuWTgYtcHAIHAcMmQBpsVg2Qx7quKQhSAKMj1gDlpcUXqwVsOe6igci+GLYAwMDCIPCBzAOA4+RYJm6xA5DrIoIm5ZCOsWbNuBR+QRF3jolgOOs8HAwbJsAAyKKMCs+3+G1vUGHKpzwXYcxM3EAglFFA65AXhXrpSf1C2SsFtvvRX/+Mc/8P7776N3797u8cLCQgCJXqqePXu6xysqKtxeq8LCQhiGgWAwmNIbVlFRgREjRrgx5eXljR63srIy5X42bNiQcn0wGIRpmo16yOpTFAWKorT1lA+pqaW89XX1TUsJIaQpbflhPdLSPPXnZIl8YnViTdyCTxHA2zwUAagM6fDIPMAlamNJvIiYbkAReRRkyGAcUBM1kOERIcR0ZHrEVo9CJDfw1i0bcdNGjl9GOODB56VhlIU1cBwg8xw4WPApEiwLkEQOcd1G1HQSc7Z4QOJ4OJwARUysJM32ywjGLOysiCDgleBpplSDZtrYVRXF/mAMGV4RCDoIx03YDuCwRA8WcxJpVE62hJhpwGFATLfgEQVIEgdJ5xA3bXhEERleCTzHY39NHMfnZyKiW/ArrU+Gmutc0EwbtXELobgO3WIQOEAzHPf90HAD8O70u9elhyMZY5g2bRreeOMN/Pvf/0b//v1Tru/fvz8KCwuxcuVK95hhGFizZo2bYA0bNgySJKXElJaWYtu2bW7M8OHDEQqFsHHjRjdmw4YNCIVCKTHbtm1DaWmpG7NixQooioJhw4a1/8m3wtHQFUsIIQ3VXzHZJ8eH3jm+lN6clkrztGW1ojv/yCe59bly/DIEjkdEt6BbDryKAM1y4DAHuumgKhJDWDNhA3DAIW5akEUOtsMQjtswbadVoxDJoTfTdhIT2z1SXYFWDrppwnYSSVBlREfESAwFcjwQN2zELAuM1fVYAQAP+BUOAVmCJPHIUARkekSU1sZQE9ObnR8cipmoiRnwyAJ8SmJ1vSzwsG0Hmm7///bePMqyrKzTfvbeZ7xjTJmRmVWZWSNVUBQIlLbKIC4QZ3FohwZLaGxsWmxABETs/tR2WQWoFAqC4lKxFbppW0Gxl40lIsOyFaQABUqoiRpyijnudKY9fH/sG1EZkRFZOURmVFLnWSuh4kbce/c599xzfucdfi9Z5Si0ozCwOMiZ75cY4zDWoo1hNdMgIFIBaSSJlKDSmnvnBtyz0MNYx75ucsaiaKt0dF4ZFvoF/aLCOj9Gqp2Gp9TfrUXGLiUBBo/ySNjLX/5y3vve9/Lnf/7ntNvt9dqrbrdLmqYIIXjVq17FLbfcwrXXXsu1117LLbfcQqPR4AUveMH63/74j/84P/MzP8P09DRTU1O85jWv4cYbb+S5z30uAI9//OP5tm/7Nl760pfyO7/zOwD8xE/8BN/1Xd/FddddB8Dznvc8nvCEJ3DzzTfzq7/6qywtLfGa17yGl770pTvWJXG2fDWEYmtqHi3UhsePPrb6HE7unjs5XTnXz3loeeQfU/IRuxVPrj+y4w/eu9X7dGJvWJJry55WTL/QBNJ3RLaTCIEgDiSVc2AsK5lmqhGSW00v1xw4g9mJzkFeWUrtB2yHSrI8qnhocUgShjQjx0pWkGmD0RbjHKGUWBxlCYEAHFh8wKKTRHTSiCgQVMaO7TRC8tKQV/aUyFyhDcujkjRUXNZtcM/cCQSCiUZMVlkSQoT0g7BL4wBBXmlWnKM0UAaGUEniQBJJL9TyUUksBau54b6FIQe6DSbO0rB1czq6N6oYldob2MYB3UY49o27dAxZT8ejWoS9853vBODZz372hsf/4A/+gBe/+MUAvO51ryPLMn7yJ3+S5eVl/s2/+Tf89V//Ne12e/3vb7vtNoIg4Id+6IfIsoznPOc5vPvd70aphz+497znPbziFa9Y76L8nu/5Ht7+9rev/14pxf/5P/+Hn/zJn+TpT386aZryghe8gF/7tV+7QFt/Zlzqodiamt0mr8z6jYwd38icrdVAzYVhK2F8cvfcGsb6aNLSsETKmP3N6BFd9k+uPyq0oxEpisq/ZxxKohAmRISJHJ0kJKs0gRC0kgAQHF3JkXnF3k7K8rCgP6qYakU0Q3VGWQghQOAojaUxHp59YrWgdOM6KuFQUhJKL7SEA8biL7A+QuUchMLPaxRW+OJ+41gcVHQaARPakWmDwJ0SmXPOp/208e9lrKXSjvneiFFpsVhK7deZhIJBaQiVQmHQxlKUjjRWoEOiVBBLhxQKKQSxEiSB/++VkfexPNPv0snBheVRyfwgJwkV7SSkkwYbrm9rhfpnUn/3aOWS8gn7auBC+IzU1NScG9tFVdaKwC/kiJya7dlOGKeRYr6fn+INNd/z6aokVJTGcqCbrs943M4z8WSfKescx1YyHNDPNcdXMx5czFAS9nYSCm2Y7xcIAVVl6ZcVVenYMxEzkYZk2pKXhutm2zzzcXuZbsVndEP84OKIL8/1mWpGHFvO+MxDSxjt+MrSkLnVHOOsN0Ad+WYBKQTaGgLhfbuGpV3fN40kYLIZo6SgHQdMNSMOTDSYacbcdOUU1862N7x3oQ3/eqzH3Sd6FMZx91yfBxeGfO7IKqNc+/0uIZDggMpAK/LpylFZkRsIJePoVMxUK2AqTRkUJfsmEm68bILr9nXppOE5e1b2sooHloZ0kpB4i+/hhfaNO2U9F+D6/aiOhNXU1NRcSLaKqlxqs+ceLexUOnc7Ybya+/mIpXU044cvXYU2DMuKNFQoKcgr59OLY7aLlmxsbgppRiELgxwcpJGiFUsmGxETzZhjqxmtJKCTBpxYzbEyJG0qZrsxcRAQKT/zMg4CHloaMSrNGUVU93RiTvRyjq6MGI6L3Qu074aMA/LCkISCVqzo577+Sjlw0hKokET5+ZHDShMryaioCJWkoRTTzZhWoohDnzLcTBwolJAYK4iUIJCSvZ2YPb2IVSWwxmKcJZCKyvpomBJ+VFKnEdPBR+OiALKipCcEioJmEqCEBBx5pZlqRuccrYpDSRoq5Dbdkl8NLgC1CKupqblkOZ8L/5l6El3KqY6LwU6nc7cTxmkIc/2crDRMNML17rm1tKIaizU5jhitEUhBtk234sn1R0o5BnnFiUFBOPZZfGgl52gvRwmw1qJDxXQrotIw04yYasVYYGXku/bAN0a14oA0Dh5x6HgSKq6dbVEaw1wv9/uz0AgcUjjvDG98KtHYcfegYrx9gkYS0JSCovI1Y5WxdJOQ6Xbsa70mmhycSr17vDYbjuNCG5SE2a4XgpW2jCqLcMKbvAYKZx2hkmjna8qk8A0Is50Y42B5WPpUsHGUxtBMUqbTcJwe9h5rUkBlz92zMpCSlVHFnvapLgMro5J2cmk3oNUirKam5pJjJy78Z2J4vN3Fu8ZzuqjV6cTHdvTziqVhSRQ+/JkU2tDLNMNSU2rDfC/HWMuVe1rEgVq3NTDWkVeG9rjLcI3TRUvW5hQ+uJRxdCXjWC9nZVBicATKp/6s9iKwn1fMDSpakeSK6RZhpFjNS4rK8dDykEobRkWFtoITvZw9nYQ97ZgyUNtaRAB0GxFPvKzLyrCil1WMNFgriKQiMxptQEpJEgjiMCCOJNp4cRSNZ1o2koDpdszlkynT7ZjJZszeVsJVe5q04oB+rk85jp2DQEmumGkSKclCv2BpVDIsqvWuUCGg0JbKGCrjvI1HKCk0KOFIA0llBUpanLAcX80ZjQd7zw0K0iBgTzsmDuRZRatO/n5nlWV5UNDLKj/PMwoYFJr5XkFlDALBQ2Z0ydZx1iKspqbmkmKnLvy14fH5s1Pp3LWL7uIg5+hqTitWZLElCgX93LuxJ6HyF/3KW0XcMzfg0HSDZhQQKcV8v2CqFdFJN17WTueZuDawOwolB6cavvZKwPJQszzIcE6QW4fEMSy89UVfCSKlOBgIVrOSuV7F6rAgEDCKvM1FWSn6eUleGaaaIUrARHPrNayOSu6ZG/LQ8oiVUYVz4IxlZA1KKUIFSkhmOimV9W79eWVIAoWxEiUlKhS04pAoCOg2ErppQGfcRbjdcbx2/CspmenEzHYSjqyMSOIIS0VWWrAWoQQGKI1BCEk3CPCNAxLtvKs/znmj1srvv6lGzPKgRDlBOw144uUTZxxN3vz9bsYBSSiZ7xUcWRrRSiKGeYVSksum/Od/PsJ/t6lFWE1NzSXFTl34a8Pj82On0rknX3STKKCdBEgh6BcV/RUvkGZavqhbG0szCZhuRcz3/BxH2/IRrclGRDzuyHPOnZHL/snH0uKgYDWv0MaSG8PcsKKqHFEoiZVgqhlS6ADrHA8uZ4wqSyOWSGBYaZIgAONAOk4MCtI4ohFJepkmlNWWEdXVUckXjqxyZDkjDAQTjZBKGwIlWRwVRKHFWodwDoGlMg7rLA5fB5ZrQSsKSeOAYVlyZMXhnGbQTGnGIVKIbY/jteN/flDQzyuWh36N10w1WcoLVrOKYW7QzqCcoxEqKuuwTtCIJIvDkqV+gVKQRApdGRx+8HkvKzEuQlBwop/zuLOYHTnX9+a5a1YUAO0kpJ2EzPdz8tIy2Yo3pCcv5TrOWoTV1NRcMux0Hde5jMip/cQ8O5XO3SyqR2VIP/edjkdWRkyKaP1vs8rQjkPaSUQSBgxyzWwnIQ4lznFWnombj6WsMljr6GWa48s5eelTeNI4nJO4kaHbCEhDSWNcKB+HIUgfTdLG0koU042QQWl5YHHIE5I2zvo0a2XsKet4cCljflBgnCMrDStZxUphyLUez64UxJEk0xW5g1YUEsqIODQY4wiURFsYZCWJFMxXFYsjxURSoCQkgeLARLKtCO02Qm92uzgiCgVKCITy2zPVjGnGhmGhcdZicWgraEWSXl6x3M/IDTSlYpRbjIUUgYsdFkcSCsJxWvj4Ss6h6eYjivH5XsE98wMCKRiVmmYU0k6D9f3mu2N9xHMrLsU6zlqE1ZwV9QWoZjfZ6TquszE8rv3ENrIT6dytRHUnDcZ1YBVSCHKtGZYV1kKkJO1xujGQYjzjUa6fi87GM/HkY6nU1rvgW1gclIwqbw5aaYsSCm0sg6IgCATT7QiTVUgBeelrpZJQIRAEQUCgJBOpZGlQ0c+Mj3AFcoOlBnhhdnRlRGUti8OClZGP7C2NfE1aGkqScTouyw1ZWTFwhjRUOCsoxpEnKQSlgV5e0klihqWmoWBYGub7GdPbCJa1zzBUkjRSOMaGsEqyr5uO08DepLWVRrSigFJbhlXFIKuIoxBTVFQ4tLEEUmCco5drWklIWVkunwxpxQFz/YKistt+Huvjo4qKQAm6SYh10B87/c+0Y5JQYa0Xq8ZsPRD8UqzjrEVYzRlRX4BqHg1ciDquMzE83ukC9K8GdiKdu5WojgPFnnaMsY5y1TAqLN00ZqIRbYiKbPdZn+nN4dqxNCw1y8OKudWcpaGPSikJK6OK0oAAokBSast8P6MZSqwTGGvJtaMVByRCUZaWotIUpbeFkNKxOMqZbadMNONT1unHHFU4fFfj8qiil5cIHI0oAOttLyKpGGlNrg0N6WcmDgpNZaAwFuF89yFW0UljJtKINAlpRSF7Ogn9XG+bonMOlILpVkRaKpJA4ZxASWglikorjLV0GxEzzYherhGZ91Iz1hIoRWUsIJBCIqSj1I4TqxnOQScNmZ1IqdbHSJ0+LTyRRoxKg3FeHLaVpJ9XLA4KIqVYHOQsD0uOrmYUxp1i3nop1nHWIqzmEakvQDWPFi5kHdfpnlP7iW3NuaRzT2YrUb3WDemAUCmy0hAqQaex8YK71Wd9NpH6OPCGr/fMD1Devt4/TwmWjGOYGywGkaY4BEI6BoU/70WBZFgY+nlJKw6x9uFuQ2MdgZQ0Y29sOtWI2Nc5dX6isY7SOJyzFJXh+GrG4rDwQ7mFoKoseaUxxteEGQuD3KwPB5eANt5EVQCZ9q/VTVJCBb28wlkHyrE0LLZsDBAClBDk2jLIK4JAkmclSkqS8UiiUaVh6KNdwjnSOKSbagTe4kIZXw9WGItxkiQcfy8d5MZydCnjsonGho7Vkzk5GhoqSTMK6OfVeuRQSnhwachEI0JKwWUTqa8ZzH2UzHdfqm2PiUc7tQireUTqC1DNo4nzvfCfLZean9jFLBk43/m1m0X1mjP9WjfkdDOiESp6ucatZOzvJigp1z/rNJLklaHUlqw0Zx2p98k2fO2Sg9Ws9EOgo7GYEIp+ZjCuIlGKSDm+MjckjhVWO5bzkjnpOwIVgnYS4IhIAkEjatCIQvZPplsek804oBkF3HlshYVhyWpW0RuVCCkIBYxKBzi0s96jzIIxfoSRFD6CuBbwCYBAWlYLQ6By9nfTcZrSkYRgnNsyRRcHiqlmzL3zQ1bz0g8Ddz49m5eG1bzCGYcdP9enXv0w8Ur7FLExDu28ZUWkBJEUgPTGsuNO00PTYtvPYXM0dC0d3c99ynd5WLKaaxrj/TXdislKQ2kso0KzqiSTTXHBvv8XmlqE1ZyWS+0CVPPVz8UeXH+p+IntVsnA+c6vPVlUrwxLhqWmGQfrnl+XTaYUleNEL+OozZlsRigBpTXM9+26j5RSktluTDs+M8uCQvsZiHs7MUeXC46sZCz2C7QFKRztNKKqDMt5ST/T2HF3onSQVop2HJIGktVcIyXjc6RgmBuiZohzjplmzDV721u+vxAw3Q45cVfBwmrhDWEdFLmhqixCQqTAGG9D4YyvAYuVj4Dp8esE4DWPk5SVYWlY0owVl02mHFvNkKLBRCPaNkWXRgop/XinXqZpp36tRxYzRoUmiRWhEFQVfg6l8NsopATnCJQXYKUW5NqglKAdSSIlMc4x1QyYPM0Q783R0DhQtJOAY8s5J/oZDy1lfiC5c+zvNlCyIhhH6ay1zPcLAimYasZ0GyFC+O/CpVK3XIuwmtNyqVyAah5bXMzB9ZeCn9ijoWTgXPd/EioaseLoiYy7TvSJQu/YPt2ImWlFdNIIUl+7tNArGOQVg0JjrGOqGWOcXU8lrox8GisO1CNG6p3zswmPLI+4f2nE8dWMQluOrYzQThAHYv285nCUlfUiIQwYlQbtKkLk+JgQJGHAnrb36GqlIYenG7TTYNvjIg58MX9VWaxz3q1eOLLCUjgQBkYVqKLCDwECJfHbqiAeCzGLrwnT1iKFZKIZoqSPEvYyTRoWXLevve3nEwWSZqTY204pK8dyXrEyKBBScFk3pTCGsnKksUTpiFJrolAyKDVCCKR1VNYX5SshCYB24mv3Su3Y04yYaiXbXiM2R0PzytDP/HtMt2KOr2RMpBHdRoTFh/9KbYmUH7NUaDgwmRIH6pKsW65FWM1puRQuQDWPXS7Gne6l4Cd2qZYM5JUf1/OVhQH93BBIwWQjIgwkTjgW+qUfoQMMc01uLKHzTvGNKBhvW8nebkIr9tYWvUyzp+2fc7pIfakt98wPeHBpSFEZisogpaCZROSVpqwMpXWkga/tcs7v01AJpBFEQiIUpEFAWVpGpWU5K2klAdo4JpKQyrhtuwLX0salNmhnSQPJivZiaw0LPtU3/jmR/nxcav+Y5OH0pAMiJejGXvgVlfdUa8chp7tHrozFOJhqhjgaNAYZ3STk2PKIlayiX2qq0qKN9Z2bwotf58bbICVYvd6l2k5iGnHgh4rHilbqzVZPd404ORo6zDWFNqSRn9MZR4oDk03SSDEoNHlpmW7F9POKQS7opAGV9m79lbFMNKJLqm65FmE1p+VSuADVXDweqxYlF7sO7WzYXDKw+TN6tJYMrEXvjqxkCCHYP5FQGsOJXoEQPpqirRdL7TQgqwyNUKGtW091xaGkML5+qRX7iMew1HS1H110ukj96qhiNat8FMlBv9BklaWdhljrWB5VOOcIpSQMBE44jBN+lqOD0liscYxKSygcVmv6GaxGimFhuHdhxL6O9/zqbNEV6JyfOSnG8xiPr+Z+8LcEZbyo8n2H/v8VviZszfbU4qODavyAcd7rbDGraAQB2sHVMy2u3NtEbzE7co1RqTm2nLEwyBmVjvlBRjNSnOgXlJVZT8/2y4pBaXwTgNIEgWBUeQPdrLI450gChZQO6QRJJLhmb4tIBRh7+vPFWonBXD/nyHJJoATDoiIOJIcnW5jxB5iEilFl6GhLGiqOr2ZYEhb6Bb1c+2kJY5f+JHzkaOijgVqE1Twij+YLUM3FYc1IsZ+XOARJKC+JUP9OcbHr0M4G5yDXFm0to8JSGrOejmlGIa1ErXfUPZpYHVX0C41SgjTyaeVKOwptiQOJs9COA473MuYHgslmRBr6lJMQ4JzAWktZGeZ7GXHoIzFFZchKDQTrkfzNUZhCGxaHOVIIKq15YCFjYeC9rEIlGOSavPDdfyISCAdaW0ZG04hCLI6yqCitFz+tSGIRxNoy0paJ0DE/yElDxcqoopNEpxwjlfE1Zsb5AnMBBAE469ONuvKv7Rh3QvLwz+DF2ZoAEw46ScR0K6CsDM1QkQSCZqIIlaDUWxfmr45K/vmBVeb7BaPK0M8qVoYVXz7eY36YkyhFGPqOT+EEkYKRtsyPcoT1EckSS6xACH9dKCqLtprKRKSBYrYdo6TYVgSukYSKve2EfqZpxorKOJQsiJR35x8UmlgJjLEUxqxfkyYaEdY5plvR+qSFk73FHq03IWvUIqzmEXk0X4BqLjyro5K7TgzGrfmCSElKo8gq+6gP9e8kF7MO7UxZE8dHljJ6WQFSMtmImGgEKCHpF/472023L8zeDdaid2ko6eeOIBTjwd2SrggotGVQaqJA4HDMDyqSYOyKn1e044BWrMgrx2rmh35XDqx15JVlkBu6aYAQgsvH9UIn48ZiJpCCflYxKjQKibWapdxQFD5CZoG8qojDiEBKinHEBwfDCqwBIWFlaFHKpz972ZqwDEhiRantlpGYUPlCemMcSgqEGHc/Wi/GNA8LrjVr0pN11FqkTAKBAmM1mQ5wVtBJIirjmOuXSOnNT7f6/O+eG3C8nzHRiin7llbisx55aai0j+6lLqSRSCrjGOaWYVlRaQgVtBKJcAKHwFqHw1JZx6AwRCMvhqaaIUKIM7oJEAKSUBIo77PmZ1sKZsbpx6WhF8rNOMBYRzcNmW5FLAxKQiURQqx7i/UzTRKqR33dci3Cas6IR+MFqObCk1eGu+cGLI9KZtrxehQ0rwx2LUXwKA71XwhOd9xfzHTtycX4a5YF042IvDIsDR3TzYh2EnJsJaMVX7gowLls81rDTxxIpBRklRmPJAqoQsVCP+f4akE/KzHji+e98wOmmjFSSk70C7LSUGhHHClM33H33CpYRRIpEJalUUAzDGknvtPy5BsFIfxIHx8R00gp0NYwqgzDwjv1C+m9uTJtaWiLUoIk8lHFQmvsOAq1JtZCA5X10qjSlqzU4zoztozEVMYShd4bK6s0WaXJq3G60TxsP7EVEVAxfl8BSTC2rHCWyTRishmyMtJ8ZXFApS2t/ad+/v284v7FAc1IYdz4HB8qkoEkDBXNKiDXhkAJCm3JS0teapQAI33ErpdblPBC0DiI8alLpSRRqOhlFQ8ujTgw0Tyjm4CN5S8hzSikP05LAhRj6xKBj6RePtlACj+kPKuMN7kF0lAxLCs62s8hfTTXLdcirOasqIXXY4u5fs7KqGRPOyYYmyf64mQ59vERj+pQ/8ViN+wh1orxk1ASh5J24ssGkkCSVYblYUkjDminAUryiOmgs+V8tnktTSiloBkFLA4KCm3pV5perlno55TG0QgjDk6m9PKKLxxdJQ4V+7oJQjoeXB75jjx8+vBEPyeNAva1YiRw2aQXI4uDgk4acni6uf7+caBoJgHOOEpjWM1KelnlrSHwRqpy7EQ/LGBUlAQCogAq5wWIUmOPMQGBBG0Z+2l5w9FSW44sZVgDVp4aiQmVXB/DI/BRvrXo11od2HaUJ/23cVBpUMIyrAxp5LDGkiaK+X5GKwzQ9tSasKw0jErLVDNkcVgRCclKpukX2nuvGYPv+hT0ci8s48iPDrLOUlV+soAUAm0cjThgohkSCoW2lkRJwjDg2GrObKdxxsfeyeUvSSTpZZa754YY6+gkoY+KFRX9vGS+L6isoZ8ZTlQVe1oJ7SQkVGI9BT+qHt11y7UIq6mp2ZJCG+95FEjUFhYlvgXdkFfyURvqvxjshj3EycX4xvpBzns7CYNC+0HUDlaLislmzHQr3LYm6Fzed62jb3lYnfM2nxzx6KQBx1czvnx8lUr7QvGVUYFSCmMsg9IQBhAKybGxjcRE6uchHl/OKK13lN/TjLl8pgHOd22PKk0SqfXOuX3dja713SREBBJnrY/0VIbKWkrjRY1hnLJkLIqct4yw48caEUjlTVoNAoSPSBbakFeaRhwyqjRHVkYcmmqdEokRwqdWhRQI59bFl4PTdjNuRgMDMy7adxXKSu6SQ6bSmGv2t4hDn3Ld/PmPhwSQl5rlrCBAMtEImEgjmomin3nPtNXMMSwsyTgapfW4a0D5WjRtHE5AM1EEUqKNIUCRV4a8rJhqhJgtROB2bC5/qaxDCkE7DWnEAQhoxxGtxNcUplHAbCdiru840csZFprpZoS1ln5e0R7fGDxaqUVYTU3NljgHFm9N4i/0G68igRQMjEXgHrWh/ovBbthDnOzfZ93DtTPTrdhbDljLqDRMtyKUFEhhzusz2hz1WhoUWOf9mcKTIqRns81rEY9e5gd0l9ox1/ezAYVzpIljfmBYnusTScnsREIziJjr5SyPSvpZRSBABQqHY6IR0Un8ZzAsNEuDkn6umWlGLI9K8mqjCJBSkAaKyUbMiYGvnQqMjzKt/ZWPBXmDVPBRJyu8iaoFhhnEgcNJRyDAGMMgdxjr2NcRzLQiVkcVZsKdIkAGufazI60jH5vAno9OzgBXQhoZlkcFVhuunm0SBT7dWRm7QRh30pCpZsw9832fGk6UL8zPSmKpkFKB0ThnUUKghMC4sWWGEITCG7VWGr9269DOCyZn/T6YaSXs6yYodWY1YWuslb80ct9ocXCqSTB+DSEY+58FRIGltA4pJLOdhEakmR8UDJdGHJpusqeVPOrrlmsRVlNTsyW+bkZSBt4eoK02WpRo64f1tpPoURvqPxfOpsbpYk6UOHlda+m8UWlQUhAptf4ZrYmiOPSpovO1kdkc6ctLXztlrWOhX6x3oZ3tNq9FPI7dv8zdJ0YsjQqOL49YyTSNSJFGAaNKM8o1PQSFtYQzklYSYA08uDyi0oYkDqi0Y3+psdahlE+P5VXFQ0tDksCbqYpNVVbWOQIpSCJJI1CQBJhYERaaxYGP9gj86B0nvEu99OVmmMqLMQtkY+v6UIFxFikssfJdeXll0I4tI0GVsaxkFbnWaOP/LsDXep0ruYOlviYpNXls+dLxPkko2ddtrB8Xa8SB4vB0g7vnelTasjIsWBlWDApDZby3mZJe0BaVH1Ukxj5loRJI6TtYjTC+gaHQOCdoRMoL3CigGft6MyXEOd0EhEoSjm1WxPgFSm0ZlprpdkRvVLGaVwxjtd4JOZmG4ODqPS1m2vF57M2LQy3CampqtiQOFJFSGFci8IW8aahQ0kfGFvoFk42IPZ2dO9Htpg9ZLytZHlVkY2GzVY3T5vVdjIkSW9VehUqyPPa5akTKezWVfoZiNw3JSp+myarzt5E5ufZsZVSxPCpZGBQ044DjqzlCwGWTjXPa5lJbTgwylgd+/uKeTgzSRzweWhlhna87SmSI1o4vH+8jgTCUDEYV/dLQ1gbhBF+pNAvDkslmTBwIgkAy3y9RcsiVMy3icKMIEQgq64ikIg4kx1YqDL5WqjwpKlU6cNrXfkkHufURstg9nD4EMNYLtCCEIPB+ZntbCXtaEaPSnLI/isrSy0pWRhXGeLF3PgJsjRwINBhluHe+z2Qa8rVXzGwpgi6bTLlqb5uFXsa/HutzdDVDa+8FNpmGZJVPAw+lTws6tzYs3HoXfweF8TVyzlms0CRRQKQCDk0nPp05LLl6T+ucvs9bmYVb57DW0Yp93d3ioOTocoaQAiVhKo3pNCNayaUhby6NVdbU7DKPNZPShy/8mmGuGVWGUEqq8ey4UlummhHX7G3tSKh/t+Yerr33mmt7oR2NSNJJItJYrdc4TTRCb32waX1ppC7oRImt6s2Gpeae+QFaW+JQepPMsYnpMNdkpW/Nn2zI87aRWYv0Kcn6YO04kLRi33VWOMODS0M6aUA7ic56m5eHJceXc+IIlAlYGWriQNHPSrS1ZIUBIUgDbwSaWW8RIbNxEbyzFBUgBP1BxXJQsZJVzLYjJlsJWVkx3xdcu1ee8r31NVGW0llfBC8EZaUp9SZxxcM/V/bhmY2ajR2MwnkBFirQ1hAISRj5uY+5NqekA7sNL5ZXRt6c1Bl/QdacP5kBSksYGgaV8R5bW5y3kkhx+UQDheB4v6CXVeDACY1zoFREK4YpF3FsXG8lxcMWH9pBJEEFY8Hk/LZDwKDUjEpfLH+umfCtzMKlEEjpI7yLQ990MdOO1m8OS20Z5CWlNo/qNOQatQirOWMea0IEdlcc7BYnX/h9Z13K0rBkZVTijGRvK2ayFbK3vTMF52dS2C7GJ/6dPvY2u7bv68YY6xiUmtJYZtoxvaxivl/QaYRbri9Q0qf8LsBEia3qzbJyfDEPfQQnUr4dPwl959pEM+KaPS2SSJ33vlqL9OXjKFt7XHO1KkuGpaEZK1aGFQv9klD5iNyoNHQbj7zNhTYsDIrxyB7Fvy72WeiXOKdZGVVoBEVZobVDtUPKzFI6RyMRFNX4si78wOeRtiglqYxmJXM4HNoKklBxebp1d2CoJBLJ4qAgVoLZTsrx1RHGGspNIam1wvyTg1mbxZIGimpczF8ZlHTkpeH46ojLJpqnpAOz0gtMbSyl9pG0nRBgMBaMBpwLMMbSyytfpJ5sjIg659OiGsvlkylY77t298KQvNTEoaKTKKwTDEov8IX0/mjG+OhgqxHgnLetGWnDhIhII8Eg08SB784dFvqcu3M3m4WHynsV3n28TxhI9k1sbLiwzn8IWWnppOe7Jy88tQireUROJ0Qu1MXxTLmQwvDRMBR5N9icfhqWGgfrM/EaieTQVPMRX+ds32+rwva5Xk4/16SRvCAieLNruxCCQG00fNTGf+az3WTLIvQkkMRK7vhEia3qzdbqYdZmBWalYWY6ptMIvMWBteDYEQEG/nulje8yS6PArynzHZgro4qjyyMfgSv9IOxRZWjFAVfNtIiVOu3n5MbtgM4J5vsFK6OChWFOVRlK41NOo7EXQ5AbEuWNTPPCIZyksoY0DFBKISpNrCSlc5SVoe9gphmPrR4cw8KcUphfGUulDVGgMNoyKisq47sjN2M5vWUEPDzL0TkfrZkflLTSiMsmHFKeWhMlJWCdf3/nKItHeoczR4xfPw4hVpLReOD5ZlZHFUmkmG7GLA1LMmNQAXRCxfIwQ0lBVgqyyiKl99/KSkMIGOWHf1vnyLRv/OimIdOdhMk0xlhBYQxFZVkZndqdeaZsaRYOxJGkESqUEDjnGwGyyhCNv3OXinVOLcJqTst2QmR+UPDg0ohmHBCoretnzpazEVQXI0L1SF1v872CPZ34qyoyuFX6KQkVQeg/90FecWw5Y7ad0BnbBOzE+21V2J5XhmGhGZaGK2eatGO1oyJ4K9f2k/EjZwoAokCsm9OCF0Nrhd2Vsexpx2Sl3dGJElvVm61mJcdWMiRuPc2VhGq9ON45P+IlGyuJcz0uT/4uNqKAh1YyokCyPKwojaUVhwRS8qVRTj/T9EYl+ycb4+ioJNOG+UFx2s/J38A5FgY5c30/fDnLKob6JIE2/ttBZjGxJQkU2oAxFSr0ZqKpFWgLWalR0nvYNWPpfcgQzK0WpGG4ZWE+gaQdBSwNc0alReK7/gwPz2g8HYqHLSvC4OFB2pW2lMZirCNSgig4NUqaFZZASSabIQvDCilBmPPrkDx5XVJAJ4lJk4CsNKeIsLXjf6IR0kkDjiyNyEvv7j83LCi0w5mKvNRU1lIZy6gylGa8rfgu0UobjMH7qClFIwwJAokDX+uHYLbDKenYs2GzWXihDUpJ3NiKZO0a0I5D2mlAHEj6ub4krHNqEVZzWrYSItb50P7yqERJwf5mcl4Xx7MVVBcjQnU6cVBoLw6OLI/oFT4S8mhKUZ5PdHC79BN4ATrRiDi+mrM8qnZEhJ2usL2faaxzNCLfDFAZh3XO342Pj5nzsX7Y7Nq+ua7LOn9nbaxjsunn0hV6zVLBd+JJ4aMzM62Y2W6yoxMlTi5Kziov+h9YHLI4LGnFgT/2lWRQ6PFnFTAqDcNS+2jYOcz3XBuDdLyXYZyjEQYoKdDGcP9CSRhI0lBRaMtCv2CqmTCRWI4uZyhASYlxsDQoiZSiCLYe2QN+/1TG/34lL1nul2g3TnWd/DkBpQWbgQ4Ma1oiMBpnoTSaqvI+W5GSCCWpDCwNKwI5pBGG7O2kp/p0IWjFAYV1VAbSSKKtQI5TbWfCycO0M+3FjxJQKkOuKwZlhdY+YrRZECgFzThgshGzMKh2RHw9vG3eWLYZC6rKMt2OacYbL/cnf/cqA+3U31AXxnqfLedd/1dzizEObX0xfhJCIBXaGrLKH+yhcpQGcm3ItUEWEAeCMPA3rN1GdEo69lxY+04JAWkoiUNF1536navGUyQuBeucWoTVbMt2QqSX+ZP+nnZMoQ3luJ35XHyRzkVQXQxfpu3EQaEN8/2CojIEUtAcXwh3QgCei3g6+TnOcd7Rwc3pp80Y6wvXV0cVvUa1PjT5XNmq+wl8J+byqPACwDoWByWVtV74SF8TYoxlonnu6YbNru393NeS5NowzH0NyyDX5NpHLHKt14/9tehgXhkGZcWJfk48HvuyU8SBP7buOTFgJS+YW/XF6pGSFNp6R/kooJXYdRNTIeBAN6WVKJQ8u+NydVTyyfsWuW9uSFZpnPBCtBWFKCVYHZSkcYDDm3OWlaXTCJjrFySxwkqBwBFKyUBbHlwect1sZ9u0UKENqyPvEd8bVQxKjWQ8AoeNQkzgOwfdSUXzxXi0T696WL5ILM3AopRDacGRVUsjrLjBdjZEMgHi0M/ZlFgQ/tge5obhORRmra3V4Y+rUkM/M95CoSi29NJLQsVUK+L+pYxA+s7KkxsCzgcDRFKykhv2T0gOT586Nujk7551jsWB737e244YFoZhYSi0Rcm1FQmscEilCJUiRjLIKj89QPrGhEo7RkUFzqJkxPHljL2d2Hd+nkckbDObxxtt5nxrMS8mtQir2ZathMhaTUoytipYGw2xxtn6Ip2toLpYvkybfZik8CmFtYtwY1wfI8fpj/MRgOeSWt38nLWC6CiQTDajc44OxoFaTz9tLuIFWBmV4GBhkK/XiJxPFHBz91M/r8b7sWRxWPqibCmY7SZ00nA9LZqVmtWR5cDEqcOZz+W941BwdEXzleGQYe6jXE44ptKIve2YQlvumx8RB5Lp1sOWHNo6ZjspIHZE/PfzCmPdelqtqHwH2EpWUuoKg2B15DvqGnFAK/ZF0UI4eqOSQzPeD2ph4EdNddPwjI7L1VHJh//1OHd8ZZlASVqRwjiBsYZRaRDOkWlHVFkCKWgnkmGhOd7L6GUVV800WRoUhFLQTkKaUeANVfOKiUa0ZVqoqCyro5IokCSBIC8dmd1ahKw99kgWDhboa6j6hqmWIFY+LTbK9SnpyDhQzLQjGnFEUfRZGhSM9PmJILO2iPGiV3PNfQtDnn6NOOU47aQh+7spf18tIJwjDr3IqfT5F+hroBjXvLXjkMktGiVOPv6NcSyNcqJA0k1iIqXBCaJA8NByjsNSaosU0vumOQf46GMxnrUUBCCcQ2sLkZ92sDgq6TZDWkm4I5Gwk9lcsL9TtZgXm1qE1WzL6TxagtC3A28O+Z6NR9C5CKqL4cu09j5ZaTnRz4nG2x4pRWktrXhsXjoeinxyfdDZCsDNkUBrHZWxp62n2eo5J3o5C/2C6VZEHEjSKPAn1HMQh5PNiERJVkYl7SRcb/1eGZUMcz8KJo0UnSRESrEu9CabEVEgzzoV122ErGYl/3qsx8IwxxgfNVgd+dmUcegjPz7iJwiVQEQBWVl48dY497To2ns/tJyhBOhKszDMKbXzflTCFwVbBw8sj9jbSk4pAm6nAcY6HlwaUBrDRCPaUsCejtVRyYNLGfODfF2ESXwrfhgIisqwkvl4yyAr6eWGpazCOcex1YzpZshUM2UiiUhDxfKgpNSWyycbj3hjkleGzz64zB33raCdo5MELAwLRqUe134Jjq2O6OeGy6caWOs42rP0RyWLwwqJH3S9rxvRCP2A61FhSEIv1FpxsGVaaK2kYXHgU5GjnatLp7QwzDRxM0Yby3zmi+43s7+T0okkhdZk5SMX358JBt/p2M81QgpOrGasZuUp3YFxoOg0QoSTJHFIbC3klRc158nabYKxjlFVbbtda0JmPs8RMBYwilYS0U4iDkwmSFZZzkuWcRjnSKSkGBs1W8u6ajXjMGAzCZhs+HPBykjTG1U0o62Pge04k6zAlgX7O1CLebGpRVjNtpzOo0Vb35K8JkTWOBuPoLMVVIX2dUrGugvmywQPi5xBWdIfVWRaEwdeCIxKy8HJlOlWTBT6rq6t6oPOVIRs1Ym4lnLrZRVCwKHpjZ2I872CXuFTByd6OUeXM+5bHFJpy5dOGPa1G1w122KyEdFJg7OODnbSkEMzTY4uZxTarEfnBIJWGhIFfli0EA8L8SMrGcd7OVPN6JzSoA4/xiUrjK9dcb5rrJdXHJhsUBnL0rBYr83KK8NUM6bSPnUaqrMXf+BP5HHovb6WspIHl3O087MJp5oxSSR9ui30UamFfg7CMdmIaMchUSg4tpJz33yf472cbhox0Qw50Em5am+TbuORpwmsjkq+cGSVYen9yOJAsjTM+cz9q+TGUhpDVRmODwpWBgWBDAiVYFiWOOffP1Cwr5MwN8iIlMKNoxjXzLY50E3HRftbv/+DSyO+dKLPsChpJIojS0MWRgWj0s9STALv/D7IK/pFiXWWvLDklabQljgQLAxLFnoxx9sFs92UTqK4bKLBKAxoRMGW+yAKJIuDnPvmByxlO6jAxhi8q70wjmwcYdxqDZV1lMbuiAA7mYEGN6yYSwrunRvw9VfPbPh9oQ2xCrhsqkG1oFkaaIpymxc7S0ogcb4xYb5X+gHp06daRKwJGW0coRh3UYaOZhjgBOSVJgwVqQ4YhZpBVpE579kWqQApHIWwyHHnqhI+ErU0rLA49rRjlBJU1p7Rd/NsswKbC/YvxSapWoTVnJbtPFp8UW5EO914CJ1NLn67eqA11gRVqe2GL+bqqGR5WHJgMj3li7kTtQBzvZz7Fgb08jV7Bn/Rl1KwMMwJhWNvO6Gfn1990CN2IlYVX1kYjLuXIm8q2s+5Z36AdY7lUckg176Oy5p136OjqyOiEIoqpdARM63olLTxdutZO5HNdhJwsDQsiEKJRKBNMS6K9oaIR1czBpmv3RJIppshUTdGWz8DcDUvOTTVPINapIphUSEFNOIA43wkrJOGrI5KTqyMSMOAOW3o5ZUvzg/8xePIckap3XqHbhh4g9IztWgotGFU+IuTsY5OGrC3HSOV74x8aNnv46zS5JUXiAezlMsnm1w+IVgcGr58okelHRONkKlmwOKg4oH5IXfPD7nxsg77uulpBemDSxnD0rB/IqXUvrB+vl+RaUM/K1nJSrSFe0/0GZWGRqQojWVUaBqRXO8i7OcVc/2CqYYf2h2pkCNhRj/T7G1HHJw69ftSaMM9c32OLo14aCWjl5XeAd05pPAO6T1b+nmUzju8D3IfMXKs1UI5oGKhVxGHA0IFk82Y6/d3eeKBSQ5PN8irU40zpRB8ZXHIXK8g3+EuNgsMKuhUmkYzQlvBcIsQ0wNLI1ayiizXO+bRdTJDAyeWcu6ZGzAs9IZ9UFS+hu/QZMKDCwP6uaGwOxONc/i6tFwbernm6PKIpxya2vJvk1Bx9WyLE4MJFkYllbYsZSVZaYkCsT4tQyJIY++5Vhm3ntwVwndGqijASX9+jqRgopGQBpJAiTMa4H0+DVeXmvA6mVqE1ZyWrUK+SSCZGqeelPQeLeeSi98q0nYyo9IQB5LlYbnhiyklPLA44r75AZdPNWjFwY7VAiz0c+46MVh3sb5sMiWvDEujgqJyTDUi5oYl/3JkhalmzFQrwjqHNmdfH3Q2nYhRoDixmtPLK39yMo5RoamMQ2uHtYIkUeTAMKt4cCVHICm1RRvHTCvaNjp48t1nXlm0teN0mBfAc2OBOCo0B6cbgGBpmFNpx3w/Z1RZQiVYGhVo5wcVr7fpV45rZrd31S+0YWnohc7SqCJWUBp/PBVlyai0jIqSbsMgpKSsNFjB3KjiwZUhSRAgpT9Os9KsX+hmOwlTzfgRo3HO+Tq3Qq+5zCfejbvQPLCYcXx1RFYaJtKIsrL0RiNGueHocs5n1BJBIJlMY7rNEOEEK7kmCATTnZhRWTE/KEjjYNuLSD+vmB/kpJFksV8wqjRHlzPuXxzQzyxxJLhvcURWaBzSp3iycj0V1M8NSnmrgOWBIcsNON/NiXBU1rCSOxqh2tK8cq6X8fkjqywMck70CgptCANBb1SNj0OoKhiNFcrphEoJlBVEGjJdoM0K+zsJR1YyQiU4NL1RkN83N+D+xeH6a18IRoWh24RAiVMG0BfacGQp49hyRn6G3ZDnwlDDVxYH3sPtJNz4ZqZfagqtUdI3JewUhYWFfkkzCci1e8TC+EOTDTqx4t5egXE+AtrLSx5cHLA0qmilvlM2ViGjwqCdIQwVcRDgrKWRBt5t3zk/eSKRBIHv/jT2kW8CL0bD1aORWoTVPCJbhXxP7sQ7n1z8IxVXCiA3ljT0kQrnfHj66r0tHlrKWByUY3PE86sFWBMinz+yzJfnBigBrTgiKwwjrVnJK8rSMJlExKHg+MqIvNSc6I1I4wCBYKoRM9OKSM8w/fdInYhZZQiE87VR+NbxtRqm1bxCSkEnVhwrc4R1LIzn7zUCRaJ8ZC4MJPfMD5huTm1bD3RiNadfaAZ5xfKwZHFYcGylQCnLZd0GnTQE4Wug7p7rE0iBcYLVrCArLO1EUVaOe+YHnFjNuGI8Jy6UfjZfHEgOTje2/FweFkEWa+26EaOPiimOrmZklebQzARZZVkalQghyUrN8rCgm0asjApWh5pWGrKvE4+96yTaOXpZte17g+/YyrWPIiopiZSvU7p/cUhvpAmE71drJIoT/Xz9+IyUZJBXLIxK2lFGJ418TQyO6WbCRCNESh9JvHKmRWG2tmow1pGVllI4Mm0Z5Jr7F4csDSuM9VHZlVGJQBIIixM+ClGZsW0DEBswAnIDeqAp9YBCG554+STGOSIEBp/O3dxNeu/8iMVBSa8wZKVvhBjlkGtfBH8u3XqB8DYN/bzkyGrGyrDgWCDpptGG7f/i0VXmBvkZ+XGdK6sVNLKSSEnScON3rKgsx1ZGrOYV2Q6lAbfCAouDkrnVYnyT5klCxUIv5+hShgUq7YXsTr7vyMDSaoYYR8pPR6gUh6ZbHFnK6ZWGwbBkkBcIJek0AkKpkMLfICWRRAUhifTDzUelpRUFBOPIl3SCSvv64dlOjHWnF4EXq+Hq0UgtwmrOmFPqCbbJxZ+N1cLpiivTSHJkOSPXlrm+L9hWSjCR+lqnQ9MN+lnF3o43qjzXL2deGR5YHHJsNWeuV1JVhiODgqzqEQWKVhhicTTjgOW8IlSwkmuMc1igEYbMtBOGhR/fsn9CnlH6b6tOxMr4WpthYVjJKhIlKVYyFgYFk80QgTfJHBUW4QRC+dq53DqssxgLrTggkL4rTALO2lPa89dYc4zvZSVzvcKPdykNpdEcW8q4e27I4ckmh2YaOGv5/NEBYQjdRkwjkPSzirmBbwqQQL8w9HNfr4X0acM0VHTScMu72DUR5GuSLfODnEIbCm0IpPR1gJXjzmN9GqG3RzDO8uBiTm9UoB10khAVwKxNcdaiLaRLQ67b1yFU3tNqu2hcqCRJoOgXFb2RF7d3nehz38KQduJtKxqB4shShnXQSkJQcGJQwNho9Lg2xGFIM1GEQjHfL2jFAbMTMWEgMdbSiIMtLyJKCnKtKcf74OjKkGOrBcL57bx/sc9i39KIfbF3qdkwXBqgwD8O/vHVHMqljGYUMJFEXHmgQeW8qD/5MOjnFXOrOcOq5NjSiMp6IVeZjZYLZ8vIQjiOfDywMOAr0y0acbBBBPbzigeX+uTFObzBWeCAlaFBCHvKAO9CG5azgkGhd2Rw9nZY8PYqbmMkrNSW5ZHvfM0qTXWB1KhxjmFhT1sn6yPSBQ5oNAOcdAg0hVGk2jclhQrSMGA19+737cg3cDQiSTMKfBrVCd/I5BQCb13RiEKSQJ1WBF6shqtHI7UIqzkvTr6gnKuL/VaRNoCVYcl9C4P1YbHehcix2C/oNkIum0ixblxbMa5QOBchNtfzswO1sYwqH/W6d26AcY5AQTMM2NdNGeLnqoFPczTjBt00JA0lM60Q4+B4P0MKwUx7+/Tfyax1Is71cgCGZcV8v/Tdho2IbhKwPCg51stIQkk3jQmVZFCU5NrQjiOMteTad7LJ8Zv2xrViaTdlsuFrwjbXZKzdfVbGsjysQECpHSt5SVFaIqUYlZoHV4a+u8v4yIFxsK9t2NMO6eeaXlmxOvTp2zgMiENv+52GiqVBxd1zPSbScEtPr1B5o9QHlwccWcm4f3FEUVmUsAgp6eUlCklpUva0BFKB1oZS+6Lx3DpSJ+kGMat5ycIwx1pHEgU045DLJxo8tDwkCsWW9WneEV7x0OqQB5cz5ns5872cvNJkRUVuDEMlEEj2d5t009AbjA5KFkcZx5Yy4iggKC0rAxBSoJRESMEDi4rysOPrrpjZ9iISBZJACO5dyujnFXfP9VkaVeSVZpRXnBj6C3f5CGJl7WVLvPAujW+WmG6PuG5fG+scalPDSlYaFgY5w0zTrwxWO5w5M6f4R0LjPb1O9HMeWBoy04k5MJGub/+o1KyWXnheaHLjm1mKytJOHn48DnwDw2gn2hEfAbXFDWlWGlYyjXPWR40u0HsHoWIlL8fD17exvCkNDy1njMqKyThgMCoBQTlW/EkoySpLoBzdNKCX+SaNUaEpjWAqjQmVtwSJlCANA6aaEUr6kV4TjdOfD8+0PvhSMF89W2oRVrMj7ISLfTzuwloZeiF338KAu04MmGyG7G2nKClYzUp6ecX9iyPuOj4gULCnndCIApqRYv9Eyt7OmRumFtpwbCXDOsewNCyNvChRCDKtWRpqCp2zMKyIlSAMFZ1YecfyOEAIwaDwnVWtOGA1czy0NGT/ZHJGgrCThsxOJHzhyKo3iywMzjlm2wkWuOtEH4EgjUMC4VDj8P8gNywNCnqxpqwsK1nJTCMiSXwxuxKCVhxinVu3mdgsAJzz0adB7jvetLFklWZUWnq5prKGYeFTYA8tjZD4Or1cW7COpZFiUBiM1YxKQ6lhpulTFct5jiHi4HSDrKj4ysKQ6/Z3Ttl+IaARBNw9N+DIcoazUBnDQFuKqqC0fkRNpCR7uxGdOCKTvtA3ChSh8NtVGMtiP0fjZ/TFpeH++RE4RzcN6Wd6y3SgL8iHYe63f3FY0C81SkistAz7FVkgUUIRqBHDsqTSjpWsYnVUUGiQQlOOU/RSSkIpUFJS5JovHe/zhaMr3HBZ17u5b7qI5KV3HF0Z5dx5vMegMIBlqVeweo7hGQdgoKgMx5Yzvnh8lWv2dJhqbjwmhYC5QcEwr3DGf347FZhyjCNA2nF0ecTebsLTDk2ub78SEmEFFyOxZIFeXtHLSmbaD3u8xaGvbc23Gha5w0ShPMVKZVho+rmmGYYEsrggaVkJRMJHoLLSbGudsppXaGMIA0VZQVZZGokiySVGgHOWOFRo45hIQkCwOPA1oCGCOJa044goAIminSi6jYBmFNBIFVPN03cJn0l98KVivnq27Kx72mOEd7zjHVx55ZUkScLTnvY0Pv7xj+/2knadk4sqvV3AmolpuF4P80isCbnV3EdlKm3pJAGFdiwOfbqs0H6USm40Xz7RZ1QYJKx3jN0zP+CBxSH5Gcb2i8oyLA3OunFRt0RbQEJlBUoIhIOsLBnkhqK09AvtrQekH0VSGcMwq9DW4ax3Mk/P4mQRB4rJRuQtJZKAA5Mpk60YbRz9sqIRS/a2Y8JAkVWWorKkcUC3Ga2bpaaB4vig4ERvhBSC2U5MGnkPs0FZoc2p6QghQOAojSPXhsVBST+rmB+UzPcyFocl1lpGuQYhEMKb1lrr6Bd+sHUYCEaFN4oVOCprKUrLytAbrgbCRzpXsmrL7rR4XPw7Nyh9/ZH0j0WhpJ1EpKFEKcGgMhxbKrwVwtAXpvcLgwKqyrIwKFjOtR+VogRxIDHOsDyqONHLGVVe2Bd643Gx5lU1yEvm+gWBEkynsa/Fy0pK5ygqQ6k1i72co8sjHlgaMsrKsZ8XVBaGhU+nOOsoKoe2hkbibSG+eHyFO4/1CNWp0wVW84pKG5ZGJauZpZ9rHlo6dwEGD3ctFqVjOct5YCEjCRR7OvGGv5PCz70cFIbKuh3vDrT48T+59t/rSD1sVdFOA/Z3Y9RFCgOsjgpKvckxP1De2PhieHoKTinMD5VEIKicJZCSCxXkcdKPk9ouilRoQzVuDDqxWpBbQyMKkfj0o7OO/rjhoxFLSmtIlGK6FTPbiTk43WRfO6WdBAgkE82I6/d3mWlFvlN33CDzSHQbIbHyRfiVsbhxHdlqVl1S5qtnSy3CzpL3ve99vOpVr+Lnf/7n+cxnPsMzn/lMvv3bv50HHnhgt5e2a5xpUeXmC+BmThZy1sJwHEGbSENO9DKWs5xGJBmWmmFmCCRcNpXihKDQlsmmrw1bGJRnJPrgpA6lvMI5wUQaMMy9N9hUGvgoW6ywCDpj5+dSWxyObiNCCUEjDOiNZ/Y1k9CbloZn9tUqtEEby+VTDSaakW/9tz5dY51juhGjpB9IGweShUFBVmn2NEMmmyFp6F2/p1oxg9zbfU+3YiaaEXtaCQenG5Tam2lu5ZjdTiJGpWZ5UDJYa6F3jsL6rspBqQFBJwmQ0qfZ4lB6nzhjEc6RxgFKSKQEhyAIBK0kxOIvwACREqgt6j0K7R3ZAwQgaIY+wtiMQyYasd/HeD+yUalZGZSUpSFQfjaddpZBqVnOKoJxcX2gJGHgTSOd8GsaZIa8sqdEA3tZxVw/R/iCFmbbMa0kJLcOh6Qdjc1qjSOrHIPCYJ2lcpYwkMjAR+KiwIuw0ufNUdIbXqaBoD8yrA58h+nmbV8elDy0nDPfq3wUwfnPfyeQgY8gSIU3Bd20+5UUxKHyPlmbBMqOvP/4f3qFQWtLGDy8gDhQPPngNM344uSXjINyCwEuhWS2nW7zrJ0hAPJCM9rUghmHkj3tCK29x1Z0AXZFOLYviaTwDTZbsFaPlUSKSAoEgulGRKQUaew99MzY0X+qEYOTtNKAq/a0eOrBaa6YbDLdjrlyT4MnHuiwtxuN/SQDrtzTPiObGni4PribhBSVoZ9rispHwHZiHvCjlTodeZa85S1v4cd//Mf5D//hPwDw1re+lQ996EO8853v5NZbb93l1e0OO1FUeaqQ838cBwqEIAkUhVmLGFREgaAZxURKEQeSrDLr3TdZqbfsBNuKJFQ0I8XcIEMhcdYhhaQ5LsYHmGjEWGdphgHdRkhWVPRGmspapsfdkNo59rZiKuO7Bc/YpHS879qJd7jPCk04HvZrrL8bFDgCKUkiRSCEL8TX3qfnwESDK6abHO/nHB6maCs4PONd0pXw+6Wd+NbyrXx69nRiIqkYFgYBWOtnYkoBhfaF/kJYAhnisAi5Vsfki+aN9OtJQ+VnMCaKZhjQHA/ZPt7LOdRNmW4lW4uwylI5w3Qz4uhqhnYWXTmCscBpETCIA4yzGGuRSlCUlkYU0ggt/dKijW8+UIEkkGLsW6SQQtKNQ4JAsDwsODB56hDnlVFJXmmiSDLVikjDgMrlJEoi0gitDZUFjcNojRSKSDpvxRFKTGFAQicOsAjfEOF8jaJwfiZlJBWdNKIybsNn4BwcWfb2DWEoKQaGYFy8fC5diWtIII18LWOaBHSTAOtOFaBZaYikYk8zZK6Xk+3oCGlvmxEpH5kMAnFKYfzhmSaHp9ucGPZ29H23XEsQnhIdL8Z2LFdOtzm+mrGU7/z7hkAkfY1ev9h4Y9hJQ67a0+ILR5exznlvv3JnZkeCP4bi0NflNrcRYOAj4sb6iO/Vs23KY6sMck0USjoEHOh6kRoKH61rxJJ9nYTpVsxMOx6Pn6qYbIU0QsVqpplpx1zWbXD1aexptuKrwXz1bKlF2FlQliWf/vSnef3rX7/h8ec973n8/d///ZbPKYqConi40qLXu/AnnIvNThRVbhZySRjQikIGhaYRB3TTCO00nSSC8diYzli4eIHhh9BGY1sL49wZddLEga8j+8rCgJHRBFIQKH9X6JyPEEy1g3GjgR8fk4QKpQTOQjMJiKQYD1T2A6b3dc+sHmzzvosDxUQjpl9UJIEiEHJsXOpTCcbATCthTyeiso68Usy0IpqpoqtDppsh8z0/hLrUDin8RINWoqjM1vtDCJjtJuSV5q4TQ+aGJUoKOknki3kLjRQSbZwfPYKPCO2diOiPNFlVkcaxF4aVZboRebf/okJYQScKaSQhBybSLetRHI5GEDLTDlnOyvVB2kpJAimRkY/WBARMtSL2tUNyDYNCM8i9qetEK0RrSSsMxwOHYW/Hm662k4C8NOTaneLcXmiDsdBNI46tjkiDgNL4CQFxoGgkgpWhIw0ESRIi8ds5yCtWC41C+GO01Fi8ePcjngRSSCabAfunUgKpaCYKy8bPYFhoVrKKdhKSBj41ZZ3FiTMTYQGn+naF+KickpJmGjDRiGhGoRe7mywC0kjRTCTtZsTeTkK+lJHtUGFSLCANoTt+/yjwHbsn004DnnLFFJ9/qLejI4s2kwKHplOE2ngj4sbDxvd2Ew5PNxkdG5Lv4DoCQAU+GtWMFZtPCXGgeMLlHT79YItjvZxIC9qhI6seeUbmmRALmGhGXD3dpJP6Y2Cr81Ic+DFko9Kyr5tgXIejKyNfK1lqkjCgnQSMKksrUkRhzGVTDfZ1U6aaEf2sYn83RUn/nYoCxeP3ddkz7lo/p7V/lQuvk6lF2FmwsLCAMYbZ2dkNj8/OznL8+PEtn3PrrbfyS7/0SxdjebvGThRVbhZyUSCZacc8tDRiVGq0MyghCaXEWLDW1zCESqKNRUovkrT1Q2WVEGfcSXP5VIOrZ9t84UjPCwDhY/hKKaYif/IclpZQCmY6KeE07GnFTDZDrLEsZd7FPJSSqU7k3ebPcd+1Uz8YPNeGNA4oVzNUyHg8jPSGjsIb5MbKi7Y48CkDEEx3Iqab0YYxPpWxGGu23B/OQTMOePKhSSZbMf96ZJUH9JBGFGCspRMGFM5bg8RBQDNRfpwSgkbs29InGiGNKGV5mPsInnN0opBWHNBKAvZ1Eg5ObZ3ySUJfl7OnnXBkpaQcb3c0HrzczyomYm//MNmIiSIFwhJFvuuqKyDPNYujHKSkGcCeVsI1s00mGhGF8U0Ll3VTZjfVRDnnU3L7ugnzvYIgdAwHlrz09XPCCZJA4hLfYr9mUDzfz9FO0EokaRhiVkeYyqKFwY6NbidbIYemmygZkIaCqWZCEmysy/FDun392lQz5vhqRoSviTHa12g5HhZjioed6gFasR+evCacBBAoCKSv5enEIZNpSDv1g743WwQoKdjbTnggiphqanqZxg2r83Kvl3iD10YIjSQiUoJWJDk82SDdVK6QhIrr9rY5MBVzz0Kxw3G4h5mZCHjy5V0aUbBBBHtz3ojpRsSeTsriUNPLCsrKG52ejx4N8fYMkVyrf2vQTeNT/u7wVIunHZpgYTXjy6XBWk0zgspBPlZiJwtyMf4n2b6JQgHN0HdeXzXT4obLurTicDxue2v8gG8xnhcbsL+T0sv9fx+cTGiEiodWCqYaIa3UC+qJNBpH2kNm2vH6vNduI+TgVONcd91jjlqEnQNi09XMOXfKY2v83M/9HK9+9avXf+71ehw8ePCCrm83ON+J9lsJub2dmFIblocVZemTg93UrQ+DbcT+8M21Xb/I9HNvMTnVPPP5jUmouOFAF5zgngVf7K+tjxooqTDWcqgV0owjSm2ZaUY84fIuSkjm+jkToRcas53knMxiN++76VbE8riwvRGFGKvR2rK36+u+5sZjkfZ3Etqpj+40o5Bjqxn7t4g4nU4Er4nfQEmu29fhwETKl471OLqcc2yQMbeS0cAxmSZoDNrATDOhspZR4V20p5sJCsG+TsKgNAgsjSiiGSkeN9vhaw5NbDtkOw4U+7oJvVGDlVxz3/yIUVWRFRqpQCjFoemEThiyrxujnU+dhEpSGT9pYHlY0YgDWrFitpPSSSMQfoByL9e0k5AnHZr0j2+x7Xs7CZdNlTywNCSNFGKkMQ6K0j83sZY09FGl1VGJtbCvE9NOQrLKsLcZ+caDcZfdbDfh4FQbNU5NHp7qkkb++N7sEdZNIwZFwZ5WRCMOySv/nkVVIsdmrGsX3jVBFq7/LEgCkMJhDKD8NIs0kkymAc00ZE87Zf9EykQzPkWEt5OQa/d2eGBhiHGwmpUYZ7Ej42vbzhIBhBISBeF4mGArSbl6b5sr9jZPSUfHgfKjjS6bZLF3nOULYJg6EcPXHp7hSQcnTxHBcaC4fLrBQysjDgxSTvRyP//VwagoGVXO12vx8L6XnH5qQIAXwUpBJPys1T2diCdfPsGByVNvRDppyNdfvYe8NARKcP/iiEHuU9zGOoZ55RuFxnWHAi8OlYCmEJTarXvEAaQxzDRiOs2YVhxw7b42e7oJE43otOelThpxxUyLh5YzhPCp45aLcM7RiAO0sTzp8gn2tmOGheZEL2dxUDDV9DejSgpGpaGThOxtn/lNaE0tws6KmZkZlFKnRL3m5uZOiY6tEccxcXzqHdBXGzsx0X6zGImUZLabAAKlfDpOKbh6b4us1CwMCo6ujMYCQ7I8LNDWcdlEetadNN1GxFOvmGRvJ+buZo/PH+uzMixJA8X+Ce8H1i80RWnZ001IwoA0lEy3O2c1q/BM9p110EkDpttdkkDSyyoWh36+onW+0WGiEbGnkxAH0ncS4WiOIw2VsWcsgk+JxCUhT7x8gqmWvzDdHfUZlhVJqKh0wKjSRIEkcpL97YhOGtPLK5TCp75ihUKCFBycSLn+QPcRP/vZTuLrdQQkUvLg8pBebhFYX/M20yQKJZFSTDZDZloxUgjvim+8wesg0xTGorVbT4sYC3vbEU/Y3+XgVPOU9z1526/b1yYOFMd7uY8sSlgaFCShYqqZEo67y3q5oNMI2N9Nxm7/FWkoaacRxkIyru9KA0EzlFw+1eTgdIPO+HtwMu1xmvYri4bH7euyOCz5yuIIbRx5VTEo3Pr0gMr4qIoMBNOtiG4aMMiNj9BWFSKSpJEiCnzkdqodc9V0k8ft87Mr93W2TpFftafJvXvbWByF8fU7ocpZyiqK6mEReLImk/hUlxC+4F1JH1VMY0EahiAglILLpho8+fIJZtsx1+zpbJmOvmpvi2+8eoalQc5nH1yht4NCbCqGr792L895wn4mGvEpIhj8sfeEA11KY1nJS750ok9RWlpJgBKaKnBo58cxrW1n6MbDqiV00wAnha8dNBbGwl4JSScJmGmnXDPb5pnX7dn2/DDbSXjaFdOUxtFt9FlYzRhVmmFlaAS+9jJQiij0c3srbXEI4lAQK0Uc+lm+ZWWZaEZ0mzEK2D+RcmiqRTeJzqhEYm/HH9P9QjMzjqb7my1DHMr1AvtCGw5MpOPOXj/BwVhzXhNLHsvUIuwsiKKIpz3tadx+++183/d93/rjt99+O89//vN3cWWPDs63qHI7IXf1nhZppLDO0c80pTFkVUigJIPcIKUXG81IcXj67HzCNr//NbNtDk43eOLlE9wz3+fEakFpHKPSsqeVcvVsk32dlCiQO1o0erp9t7eTclAb8sqMTWn9qJtBUdHPtY/mtBMOTTW879dZiuDN4jcOJHvaCTjBEy/3F6h+pnHOEYa+KD8OhT8hV46JQlMYg5KCJPDdofu6PjJ4pl1Rh6ebTKQRs52Uq3ttlrNinCoNmWxENCLlxwUFkjQK1kVmoWF/p8HEvpCVrPKzNoclQSDZ2wk4PN087fGwtu2FsVy5p8lUM2SxHZNGAcvDEiUdSRiwPPL2HTOtiOlWTCQFq4VmohmPi5QjWlHAcuanDzTjgD2dhL2t+LQzLA9OpfSykqVRyZMum6AVB8z1vBnxUr+gNHY9EjLZDDk8lXJwsoWQkvvm+6zmFWmkMICuLGGgmG2HXDvbYW8npp1E7O9uf1PSbUQ889oZYiV9LeG4djBSUFaO0hocjkAqnHWUY6uTOPAXf2+KG5AqhZECxj6Bl083ODjZIAkDDkw2TpuOfvb1ewFoJhGfe2CB+b5dT8Wejq1q4tZScYdnGjz9mhmefu1eJhrxliJ47f2vnW3TSUP2tmP2tBe5d35EUWkqM7avwTefWKPJtaWsIAgEe1sxl003uHwixWC568SQY8sZRWVoJQH7uymP29vlpqsneMKBiW23Y+28s+Zb9qXjfT+ztLLEM5K9nYSphrdracYhAsmorFgZlgy1ISsMV+3pkBUVpTEooZjpxFw722K2m7K/m55RicTa+TcZrdm52PVzy8nHbxz4G85uIzqr6Sg1WyOc+2ocBHDheN/73sfNN9/Mb//2b/MN3/ANvOtd7+J3f/d3+cIXvsDhw4cf8fm9Xo9ut8vq6iqdzqnmlTWe0325T/4dsC5O4vBUH6bzpZ9XZKWvp+qkjy6zwO320bmcGLebdpBGkmhcV2adW9/PwNgjzY5Tg3Y82cCd1wiptbWf/Lpr9W0nzyvdbiJDcZJYPdPjYfO2G+vd5UtrGWQ+4ppp3z06mUZMNCMC6TsxnXCocUG+FBAGkm4SIqXYUJd3OlZHJQ8uZRxd8QPDT/RyisogpPDF1KGgnQS0o5BmEtJKIhhHPhtRgJCCUVGxMvKRCal8s0EzDs9YDOeV4cjyiPsXBxxdLjjRz1js5wwrX6meRoqD3ZR9kwmRCBhVFY0o8MfEuDFlZZCzPPIDwLtpxEQz5NBUk2v3trZNR5+8D+6ZG3LnsRU+ff8CR1cLrHOkSpFXmjCUJMrn4wIl2duNiZA0E7+GNJIY4+1Z4kCxf7Lhj4tAntHUDvDHzmpWMt8vWejlGOswzpGVBusco8Knm9fEkh+JZZDSd287xr5WI38z00pCrpxpnVV0qNDGGwuPh1kmoaKdeHuRzd+HUltWRiW9XGOMIy8NS1mJBDqNgIk09lM3ziE6VYurrbkQ1+9ahJ0D73jHO3jzm9/MsWPHeOITn8htt93Gs571rDN6bi3Cah7NXAon3wu1xq1et9CGorLr4hI4r1mpp6OfV/7CPx5Ub8YmnoES66Ky1HZsEivW03ubb0pOXu/ZrmfttYaF9hMqRhVx6C1TJpvR+jZudyMkhH9/4yyNKNjWof2R9kFWGrJSY8fvGwVy/WZICjHuQvXzXNfWcPJncL6fyebnr61rq/1+sjDaifc+FzZ/Ho/27/ClSi3CvgqoRVhNTU1NTc2lx4W4fteO+TU1NTU1NTU1u0AtwmpqampqampqdoFahNXU1NTU1NTU7AK1CKupqampqamp2QVqEVZTU1NTU1NTswvUIqympqampqamZheoRVhNTU1NTU1NzS5Qi7Campqampqaml2gFmE1NTU1NTU1NbtAPcD7IrM2oKDX6+3ySmpqampqamrOlLXr9k4OGqpF2EWm3+8DcPDgwV1eSU1NTU1NTc3Z0u/36Xa7O/Ja9ezIi4y1lqNHj9JutxFr01bPkl6vx8GDB3nwwQcfk/Mn6+1/7G7/Y3nbod7+evvr7d/N7XfO0e/3OXDgAFLuTDVXHQm7yEgpufzyy3fktTqdzmPyi7hGvf2P3e1/LG871Ntfb3+9/bu1/TsVAVujLsyvqampqampqdkFahFWU1NTU1NTU7ML1CLsEiSOY37hF36BOI53eym7Qr39j93tfyxvO9TbX29/vf1fbdtfF+bX1NTU1NTU1OwCdSSspqampqampmYXqEVYTU1NTU1NTc0uUIuwmpqampqamppdoBZhNTU1NTU1NTW7QC3CLjHe8Y53cOWVV5IkCU972tP4+Mc/vttLuijceuutfO3Xfi3tdpu9e/fyvd/7vXzpS1/a7WXtGrfeeitCCF71qlft9lIuGkeOHOFHf/RHmZ6eptFo8DVf8zV8+tOf3u1lXRS01vyX//JfuPLKK0nTlKuuuor/9t/+G9ba3V7aBeFjH/sY3/3d382BAwcQQvCBD3xgw++dc/ziL/4iBw4cIE1Tnv3sZ/OFL3xhdxZ7ATjd9ldVxc/+7M9y44030mw2OXDgAD/2Yz/G0aNHd2/BO8gjffYn8x//439ECMFb3/rWi7a+naYWYZcQ73vf+3jVq17Fz//8z/OZz3yGZz7zmXz7t387DzzwwG4v7YLz0Y9+lJe//OX8wz/8A7fffjtaa573vOcxHA53e2kXnU996lO8613v4klPetJuL+Wisby8zNOf/nTCMOSv/uqv+OIXv8iv//qvMzExsdtLuyi86U1v4rd/+7d5+9vfzp133smb3/xmfvVXf5W3ve1tu720C8JwOOTJT34yb3/727f8/Zvf/Gbe8pa38Pa3v51PfepT7Nu3j2/5lm9Zn817qXO67R+NRtxxxx381//6X7njjjv4sz/7M7785S/zPd/zPbuw0p3nkT77NT7wgQ/wj//4jxw4cOAirewC4WouGb7u677OvexlL9vw2PXXX+9e//rX79KKdo+5uTkHuI9+9KO7vZSLSr/fd9dee627/fbb3Td90ze5V77ylbu9pIvCz/7sz7pnPOMZu72MXeM7v/M73Ute8pINj33/93+/+9Ef/dFdWtHFA3Dvf//713+21rp9+/a5N77xjeuP5Xnuut2u++3f/u1dWOGFZfP2b8UnP/lJB7j777//4izqIrHdtj/00EPusssuc5///Ofd4cOH3W233XbR17ZT1JGwS4SyLPn0pz/N8573vA2PP+95z+Pv//7vd2lVu8fq6ioAU1NTu7ySi8vLX/5yvvM7v5PnPve5u72Ui8pf/MVfcNNNN/GDP/iD7N27l6c85Sn87u/+7m4v66LxjGc8gw9/+MN8+ctfBuBzn/scn/jEJ/iO7/iOXV7Zxee+++7j+PHjG86FcRzzTd/0TY/JcyH486EQ4jERGbbWcvPNN/Pa176WG264YbeXc97UA7wvERYWFjDGMDs7u+Hx2dlZjh8/vkur2h2cc7z61a/mGc94Bk984hN3ezkXjf/5P/8nd9xxB5/61Kd2eykXnXvvvZd3vvOdvPrVr+YNb3gDn/zkJ3nFK15BHMf82I/92G4v74Lzsz/7s6yurnL99dejlMIYw6/8yq/w7/7dv9vtpV101s53W50L77///t1Y0q6S5zmvf/3recELXvCYGOr9pje9iSAIeMUrXrHbS9kRahF2iSGE2PCzc+6Ux77a+amf+in++Z//mU984hO7vZSLxoMPPsgrX/lK/vqv/5okSXZ7ORcday033XQTt9xyCwBPecpT+MIXvsA73/nOx4QIe9/73scf//Ef8973vpcbbriBz372s7zqVa/iwIEDvOhFL9rt5e0K9bnQF+n/yI/8CNZa3vGOd+z2ci44n/70p/mN3/gN7rjjjq+az7pOR14izMzMoJQ6Jeo1Nzd3yh3hVzP/+T//Z/7iL/6Cj3zkI1x++eW7vZyLxqc//Wnm5uZ42tOeRhAEBEHARz/6UX7zN3+TIAgwxuz2Ei8o+/fv5wlPeMKGxx7/+Mc/JppSAF772tfy+te/nh/5kR/hxhtv5Oabb+anf/qnufXWW3d7aRedffv2ATzmz4VVVfFDP/RD3Hfffdx+++2PiSjYxz/+cebm5jh06ND6efD+++/nZ37mZ7jiiit2e3nnRC3CLhGiKOJpT3sat99++4bHb7/9dr7xG79xl1Z18XDO8VM/9VP82Z/9GX/7t3/LlVdeudtLuqg85znP4V/+5V/47Gc/u/7vpptu4oUvfCGf/exnUUrt9hIvKE9/+tNPsST58pe/zOHDh3dpRReX0WiElBtP10qpr1qLitNx5ZVXsm/fvg3nwrIs+ehHP/qYOBfCwwLsrrvu4m/+5m+Ynp7e7SVdFG6++Wb++Z//ecN58MCBA7z2ta/lQx/60G4v75yo05GXEK9+9au5+eabuemmm/iGb/gG3vWud/HAAw/wspe9bLeXdsF5+ctfznvf+17+/M//nHa7vX4X3O12SdN0l1d34Wm326fUvzWbTaanpx8TdXE//dM/zTd+4zdyyy238EM/9EN88pOf5F3vehfvete7dntpF4Xv/u7v5ld+5Vc4dOgQN9xwA5/5zGd4y1vewkte8pLdXtoFYTAYcPfdd6//fN999/HZz36WqakpDh06xKte9SpuueUWrr32Wq699lpuueUWGo0GL3jBC3Zx1TvH6bb/wIED/Nt/+2+54447+Mu//EuMMevnw6mpKaIo2q1l7wiP9NlvFpxhGLJv3z6uu+66i73UnWF3mzNrzpbf+q3fcocPH3ZRFLmnPvWpjxmLBmDLf3/wB3+w20vbNR5LFhXOOffBD37QPfGJT3RxHLvrr7/evetd79rtJV00er2ee+UrX+kOHTrkkiRxV111lfv5n/95VxTFbi/tgvCRj3xky+/7i170Iuect6n4hV/4Bbdv3z4Xx7F71rOe5f7lX/5ldxe9g5xu+++7775tz4cf+chHdnvp580jffabudQtKoRzzl0kvVdTU1NTU1NTUzOmrgmrqampqampqdkFahFWU1NTU1NTU7ML1CKspqampqampmYXqEVYTU1NTU1NTc0uUIuwmpqampqamppdoBZhNTU1NTU1NTW7QC3Campqampqamp2gVqE1dTU1DwG+MVf/EW+5mu+ZreXUVNTcxK1CKupqdkxXvziFyOE4I1vfOOGxz/wgQ8ghNilVe0Mf/d3f4cQgpWVlQv2HkKI9X+tVosnP/nJvPvd7z6n1/nABz6w4bHXvOY1fPjDH96ZhdbU1OwItQirqanZUZIk4U1vehPLy8u7vZQzoizLi/p+zjm01tv+/g/+4A84duwYn/vc5/jhH/5h/v2///c7Mpy41Wo9ZgY919RcKtQirKamZkd57nOfy759+7j11lu3/ZutUmNvfetbueKKK9Z/fvGLX8z3fu/3cssttzA7O8vExAS/9Eu/hNaa1772tUxNTXH55Zfz+7//+xte58iRI/zwD/8wk5OTTE9P8/znP5+vfOUrp7zurbfeyoEDB3jc4x4HwB//8R9z00030W632bdvHy94wQuYm5sD4Ctf+Qrf/M3fDMDk5CRCCF784hcDUBQFr3jFK9i7dy9JkvCMZzyDT33qU+vvtxZB+9CHPsRNN91EHMd8/OMf33bfTExMsG/fPq6++mre8IY3MDU1xV//9V+v//5Tn/oU3/It38LMzAzdbpdv+qZv4o477lj//do+/L7v+z6EEOs/b97na/vh137t19i/fz/T09O8/OUvp6qq9b85duwY3/md30maplx55ZW8973v5YorruCtb33rhs/y0KFDxHHMgQMHeMUrXrHtttXU1GykFmE1NTU7ilKKW265hbe97W089NBD5/Vaf/u3f8vRo0f52Mc+xlve8hZ+8Rd/ke/6ru9icnKSf/zHf+RlL3sZL3vZy3jwwQcBGI1GfPM3fzOtVouPfexjfOITn6DVavFt3/ZtGyJeH/7wh7nzzju5/fbb+cu//EvAR8R++Zd/mc997nN84AMf4L777lsXWgcPHuRP//RPAfjSl77EsWPH+I3f+A0AXve61/Gnf/qn/OEf/iF33HEH11xzDd/6rd/K0tLShm153etex6233sqdd97Jk570pEfcdmMM/+t//S+WlpYIw3D98X6/z4te9CI+/vGP8w//8A9ce+21fMd3fAf9fh9gXQCuRdROFoSb+chHPsI999zDRz7yEf7wD/+Qd7/73RvSnz/2Yz/G0aNH+bu/+zv+9E//lHe9613rwhTgf//v/81tt93G7/zO73DXXXfxgQ98gBtvvPERt62mpmbMLg8Qr6mp+SriRS96kXv+85/vnHPu67/+691LXvIS55xz73//+93Jp5tf+IVfcE9+8pM3PPe2225zhw8f3vBahw8fdsaY9ceuu+4698xnPnP9Z621azab7n/8j//hnHPu937v99x1113nrLXrf1MUhUvT1H3oQx9af93Z2VlXFMVpt+WTn/ykA1y/33fOOfeRj3zEAW55eXn9bwaDgQvD0L3nPe9Zf6wsS3fgwAH35je/ecPzPvCBD5z2/ZxzDnBJkrhms+mUUg5wU1NT7q677tr2OVpr12633Qc/+MENr/P+979/w99t3udr+1drvf7YD/7gD7of/uEfds45d+eddzrAfepTn1r//V133eUAd9tttznnnPv1X/9197jHPc6VZfmI21ZTU3MqdSSspqbmgvCmN72JP/zDP+SLX/ziOb/GDTfcgJQPn6ZmZ2c3RFqUUkxPT69HZz796U9z99130263abVatFotpqamyPOce+65Z/15N954I1EUbXivz3zmMzz/+c/n8OHDtNttnv3sZwPwwAMPbLu+e+65h6qqePrTn77+WBiGfN3XfR133nnnhr+96aabzmibb7vtNj772c9y++238zVf8zXcdtttXHPNNeu/n5ub42UvexmPe9zj6Ha7dLtdBoPBade5HTfccANKqfWf9+/fv74vv/SlLxEEAU996lPXf3/NNdcwOTm5/vMP/uAPkmUZV111FS996Ut5//vff9p6t5qamo0Eu72Ampqar06e9axn8a3f+q284Q1vWE/rrSGlxDm34bGTa5HWODkNB77rb6vHrLUAWGt52tOexnve855TXmvPnj3r/91sNjf8bjgc8rznPY/nPe95/PEf/zF79uzhgQce4Fu/9VtPW7i/tg2bOz+dc6c8tvk9t2Pfvn1cc801XHPNNfzJn/wJT3nKU7jpppt4whOeAPharvn5ed761rdy+PBh4jjmG77hG86pweB0+3Lz57PGyY8fPHiQL33pS9x+++38zd/8DT/5kz/Jr/7qr/LRj370lNeuqak5lToSVlNTc8F44xvfyAc/+EH+/u//fsPje/bs4fjx4xsu6J/97GfP+/2e+tSnctddd7F37951IbP2r9vtbvu8f/3Xf2VhYYE3vvGNPPOZz+T666/fUPsErEfOjDHrj11zzTVEUcQnPvGJ9ceqquKf/umfePzjH3/e23PNNdfwAz/wA/zcz/3c+mMf//jHecUrXsF3fMd3cMMNNxDHMQsLCxueF4bhhnWeC9dffz1aaz7zmc+sP3b33XefYtGRpinf8z3fw2/+5m/yd3/3d/y///f/+Jd/+Zfzeu+amscKtQirqam5YNx444288IUv5G1ve9uGx5/97GczPz/Pm9/8Zu655x5+67d+i7/6q7867/d74QtfyMzMDM9//vP5+Mc/zn333cdHP/pRXvnKV562SeDQoUNEUcTb3vY27r33Xv7iL/6CX/7lX97wN4cPH0YIwV/+5V8yPz/PYDCg2Wzyn/7Tf+K1r30t//f//l+++MUv8tKXvpTRaMSP//iPn/f2APzMz/wMH/zgB/mnf/onwAuzP/qjP+LOO+/kH//xH3nhC19ImqYbnnPFFVfw4Q9/mOPHj5+zVcj111/Pc5/7XH7iJ36CT37yk3zmM5/hJ37iJ0jTdD3K9+53v5vf+73f4/Of/zz33nsvf/RHf0Saphw+fPj8Nrqm5jFCLcJqamouKL/8y798Smrr8Y9/PO94xzv4rd/6LZ785CfzyU9+kte85jXn/V6NRoOPfexjHDp0iO///u/n8Y9/PC95yUvIsoxOp7Pt8/bs2cO73/1u/uRP/oQnPOEJvPGNb+TXfu3XNvzNZZddxi/90i/x+te/ntnZWX7qp34K8NG+H/iBH+Dmm2/mqU99KnfffTcf+tCHNtROnQ833ngjz33uc/n//r//D4Df//3fZ3l5mac85SncfPPN6/YYJ/Prv/7r3H777Rw8eJCnPOUp5/ze//2//3dmZ2d51rOexfd93/fx0pe+lHa7TZIkgLfT+N3f/V2e/vSn86QnPYkPf/jDfPCDH6z9yGpqzhDhtkv819TU1NTUnMRDDz3EwYMH+Zu/+Rue85zn7PZyamoueWoRVlNTU1OzJX/7t3/LYDDgxhtv5NixY7zuda/jyJEjfPnLX64L72tqdoC6O7KmpqamZkuqquINb3gD9957L+12m2/8xm/kPe95Ty3Aamp2iDoSVlNTU1NTU1OzC9SF+TU1NTU1NTU1u0AtwmpqampqampqdoFahNXU1NTU1NTU7AK1CKupqampqamp2QVqEVZTU1NTU1NTswvUIqympqampqamZheoRVhNTU1NTU1NzS5Qi7Campqampqaml2gFmE1NTU1NTU1NbvA/w99fACSYAHo6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the relationship between numerator ratings and favourite count\n",
    "sns.regplot(data = df_archive, x = 'rating_numerator', y = 'favorite_count', \n",
    "            fit_reg = False, x_jitter=0.3, scatter_kws={'alpha':1/10})\n",
    "plt.title('Relationship between Numerator Ratings and Favourite Count')\n",
    "plt.xlabel('Numerator Ratings')\n",
    "plt.ylabel('Favorite Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "991e337e",
   "metadata": {},
   "source": [
    "Some high ratings have more favorite counts. So there seems to be a positive relationship between numerator ratings and favourite count. As the numerator rating increases, the favorite count also increases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aabfe1f",
   "metadata": {},
   "source": [
    "### Is there any relationship between favourite count and retweet count?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e9ff9ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the relationship between numerator ratings and favourite count\n",
    "plt.scatter(data = df_archive, x = 'favorite_count', y = 'retweet_count')\n",
    "plt.title('Relationship between the Favorite Count and Retweet Count')\n",
    "plt.xlabel('Favorite Count')\n",
    "plt.ylabel('Retweet Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7aad674",
   "metadata": {},
   "source": [
    "There is a strong positive correlation between favorite count and retweet count as expected. As the favorite count increases, the retweet count also increases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed5642be",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "I gathered data from three different sources: \n",
    "- The WeRateDogs Twitter archive - A file provided by Udacity\n",
    "- Images prediction files - A file downloaded programmatically from Udacity server\n",
    "- Additional data from the Twitter API - gathering through Twitter API\n",
    "\n",
    "I assessed, identified and cleaned the following quality and tidiness issues:\n",
    "#### Quality Issues\n",
    "\n",
    "#### `Archived` table\n",
    "\n",
    "- Erroneous data type for timestamp and Tweet id\n",
    "- Drop extraneous columns (Retweets and reply columns) and rows. We are only interested in the original tweets.\n",
    "- Source links should be shortened\n",
    "- Rating denominator has values greater than 10\n",
    "- Rating numerator has large values like 960 and 1776\n",
    "- Name column contains some wrong names like a, an, the, just, very, quite etc\n",
    "- Remove rows without dog images\n",
    "- Drop null values\n",
    "\n",
    "#### `Twitter API` table\n",
    "- Tweet id should be a string and not integer\n",
    "- Missing data. The data has 33 rows less than the twitter archived\n",
    "\n",
    "#### `Images` table\n",
    "- Tweet id should be a string and not integer\n",
    "- Missing data. The data has 281 rows less than the twitter archived\n",
    "\n",
    "#### Tidiness Issues\n",
    "\n",
    "- Dog stages in the archived table should be in one column and not four columns\n",
    "- Multiple columns in the Images table for dog breeds and confidence predictions\n",
    "- Twitter API and Images tables should be part of archived table.\n",
    "\n",
    "And I gathered the following insights:\n",
    "### Insights\n",
    "- The numerical rating distribution is left-tailed and the highest rating is around 11.\n",
    "- Golden Retriever is the dog breed mostly tweeted about by WeRateDog and it has the highest favorite count.\n",
    "- WeRateDog rating seems to influence the favorite count for dog breeds and\n",
    "- Favorite count has a strong positive correlation with the retweet count."
   ]
  }
 ],
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