wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 4: Wrangle and Analyze Data\n",
    "\n",
    "## Table of Contents\n",
    "- [Introduction](#intro)\n",
    "- [Gathering data](#gather)\n",
    "- [Assessing data](#assess)\n",
    "    - [Quality Issues](#quality_issues)\n",
    "    - [Tidiness Issues](#tidiness_issues)\n",
    "- [Cleaning data](#clean)\n",
    "- [Storing data](#store)\n",
    "    - [Storing data into a CSV file](#store_csv)\n",
    "    - [Storing data into a SQLite database](#store_sql)\n",
    "- [Analysing data](#analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "The dataset that I will be wrangling (and analyzing and visualizing) is the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog. These ratings almost always have a denominator of 10. The numerators, though? Almost always greater than 10. 11/10, 12/10, 13/10, etc. Why? Because \"they're good dogs Brent.\" WeRateDogs has over 4 million followers and has received international media coverage.\n",
    "\n",
    "Key points to keep in mind when data wrangling for this project:\n",
    "\n",
    "- You only want original ratings (no retweets) that have images. Though there are 5000+ tweets in the dataset, not all are dog ratings and some are retweets.\n",
    "- Assessing and cleaning the entire dataset completely would require a lot of time, and is not necessary to practice and demonstrate your skills in data wrangling. Therefore, the requirements of this project are only to assess and clean at least 8 quality issues and at least 2 tidiness issues in this dataset.\n",
    "- Cleaning includes merging individual pieces of data according to the rules of tidy data.\n",
    "- The fact that the rating numerators are greater than the denominators does not need to be cleaned. This unique rating system is a big part of the popularity of WeRateDogs.\n",
    "- You do not need to gather the tweets beyond August 1st, 2017. You can, but note that you won't be able to gather the image predictions for these tweets since you don't have access to the algorithm used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='gather'></a>\n",
    "## Gathering data\n",
    "\n",
    "First of all I will load all the libraries that I will use in this report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import tweepy\n",
    "import json\n",
    "import sqlite3\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read WeRateDogs Twitter archive (CSV)\n",
    "\n",
    "To read this file i will use pandas, because it's already loaded in the environment and it already in a CSV format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </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",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +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",
       "\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",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       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",
       "\n",
       "   rating_denominator     name doggo floofer pupper puppo  \n",
       "0                  10  Phineas  None    None   None  None  \n",
       "1                  10    Tilly  None    None   None  None  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data from the CSV\n",
    "twitter_data = pd.read_csv('twitter-archive-enhanced.csv')\n",
    "\n",
    "# Show the header\n",
    "twitter_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read tweet image predictions (TSV)\n",
    "\n",
    "In this case, first we need to import the data from the Udacity's servers and then read the file using the tab as a seperator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the data programmatically using 'requests'\n",
    "r = requests.get('https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv')\n",
    "\n",
    "# Save the data into a file called 'image_predictions.tsv'\n",
    "with open ('image_predictions.tsv', 'wb') as file:\n",
    "    file.write(r.content)\n",
    "\n",
    "# Read the file using tab as a seperator\n",
    "img_pred = pd.read_table('image_predictions.tsv', sep='\\t')\n",
    "\n",
    "# Show the header\n",
    "img_pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read from the Twitter API (JSON)\n",
    "\n",
    "In this case first I need to have the API keys and then import the data using the [API of Twitter](https://developer.twitter.com/en)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Configuration of the credentails for the Twitter API\n",
    "consumer_key = 'CONSUMER KEY'\n",
    "consumer_secret = 'CONSUMER SECRET'\n",
    "access_token = 'ACCESS TOKEN'\n",
    "access_secret = 'ACCESS SECRET'\n",
    "\n",
    "# Get the authorization token from the API\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "\n",
    "# Get the API access\n",
    "api = tweepy.API(auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we already know the ID of each tweet that we want to have information in the dataframe called `twitter_data` in the column `tweet_id`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create array for each tweet\n",
    "api_tweets = []\n",
    "\n",
    "# Create array with the correct and wrong ID's\n",
    "correct_id = []\n",
    "wrong_id = []\n",
    "\n",
    "# Get the data for each tweet using the API\n",
    "for tweet_id in twitter_data['tweet_id']:\n",
    "    data = {}\n",
    "    try: \n",
    "        info = api.get_status(tw_id, tweet_mode='extended', wait_on_rate_limit = True, wait_on_rate_limit_notify = True)\n",
    "        data['tweet_id'] = tweet_id\n",
    "        data['retweet_count'] = info._json['retweet_count']\n",
    "        data['favorite_count'] = info._json['favorite_count']\n",
    "        \n",
    "        # Add the tweet info in the array api_tweets\n",
    "        api_tweets.append(data)\n",
    "        \n",
    "        # Add the id to the array of correct id's        \n",
    "        correct_id.append(tweet_id)\n",
    "        \n",
    "    except Exception as e:\n",
    "        # Add the id to the array of wrong id's        \n",
    "        wrong_id.append(tweet_id)\n",
    "        \n",
    "# Transform the array to a pandas dataframe\n",
    "api_data = pd.DataFrame(api_tweets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once I have dowloaded the data from the API of Twitter, I will save all this data into a CSV using the `utf-8` encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38827</td>\n",
       "      <td>8595</td>\n",
       "      <td>892420643555336193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33260</td>\n",
       "      <td>6316</td>\n",
       "      <td>892177421306343426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25056</td>\n",
       "      <td>4192</td>\n",
       "      <td>891815181378084864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42206</td>\n",
       "      <td>8707</td>\n",
       "      <td>891689557279858688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40353</td>\n",
       "      <td>9480</td>\n",
       "      <td>891327558926688256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   favorite_count  retweet_count            tweet_id\n",
       "0           38827           8595  892420643555336193\n",
       "1           33260           6316  892177421306343426\n",
       "2           25056           4192  891815181378084864\n",
       "3           42206           8707  891689557279858688\n",
       "4           40353           9480  891327558926688256"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the API data to a CSV without the index\n",
    "api_data.to_csv('tweet_json.txt', encoding = 'utf-8', index=False)\n",
    "\n",
    "# Read the data from the txt file\n",
    "api_data = pd.read_csv('tweet_json.txt')\n",
    "\n",
    "# Show the header\n",
    "api_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='assess'></a>\n",
    "## Assessing data\n",
    "\n",
    "To assess the data, first I will do the assessment individually for each dataset and at the end I will merge them. The 3 dataframes I will be working on are:\n",
    "\n",
    "- `twitter_data`: Info for each tweet data like the timestamp and the rating\n",
    "- `img_pred`: Image predictions for each tweet\n",
    "- `api_data`: Twitter API with the nº of retweets and favorite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing `twitter_data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the header of 'twitter_data'\n",
    "twitter_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      2356 non-null int64\n",
      "in_reply_to_status_id         78 non-null float64\n",
      "in_reply_to_user_id           78 non-null float64\n",
      "timestamp                     2356 non-null object\n",
      "source                        2356 non-null object\n",
      "text                          2356 non-null object\n",
      "retweeted_status_id           181 non-null float64\n",
      "retweeted_status_user_id      181 non-null float64\n",
      "retweeted_status_timestamp    181 non-null object\n",
      "expanded_urls                 2297 non-null object\n",
      "rating_numerator              2356 non-null int64\n",
      "rating_denominator            2356 non-null int64\n",
      "name                          2356 non-null object\n",
      "doggo                         2356 non-null object\n",
      "floofer                       2356 non-null object\n",
      "pupper                        2356 non-null object\n",
      "puppo                         2356 non-null object\n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Show the info of 'twitter_data'\n",
    "twitter_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.356000e+03</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>2356.000000</td>\n",
       "      <td>2356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.427716e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>7.720400e+17</td>\n",
       "      <td>1.241698e+16</td>\n",
       "      <td>13.126486</td>\n",
       "      <td>10.455433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.856705e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>6.236928e+16</td>\n",
       "      <td>9.599254e+16</td>\n",
       "      <td>45.876648</td>\n",
       "      <td>6.745237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>6.661041e+17</td>\n",
       "      <td>7.832140e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783989e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>7.186315e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.196279e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>7.804657e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.993373e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>8.203146e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>7.874618e+17</td>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "count  2.356000e+03           7.800000e+01         7.800000e+01   \n",
       "mean   7.427716e+17           7.455079e+17         2.014171e+16   \n",
       "std    6.856705e+16           7.582492e+16         1.252797e+17   \n",
       "min    6.660209e+17           6.658147e+17         1.185634e+07   \n",
       "25%    6.783989e+17           6.757419e+17         3.086374e+08   \n",
       "50%    7.196279e+17           7.038708e+17         4.196984e+09   \n",
       "75%    7.993373e+17           8.257804e+17         4.196984e+09   \n",
       "max    8.924206e+17           8.862664e+17         8.405479e+17   \n",
       "\n",
       "       retweeted_status_id  retweeted_status_user_id  rating_numerator  \\\n",
       "count         1.810000e+02              1.810000e+02       2356.000000   \n",
       "mean          7.720400e+17              1.241698e+16         13.126486   \n",
       "std           6.236928e+16              9.599254e+16         45.876648   \n",
       "min           6.661041e+17              7.832140e+05          0.000000   \n",
       "25%           7.186315e+17              4.196984e+09         10.000000   \n",
       "50%           7.804657e+17              4.196984e+09         11.000000   \n",
       "75%           8.203146e+17              4.196984e+09         12.000000   \n",
       "max           8.874740e+17              7.874618e+17       1776.000000   \n",
       "\n",
       "       rating_denominator  \n",
       "count         2356.000000  \n",
       "mean            10.455433  \n",
       "std              6.745237  \n",
       "min              0.000000  \n",
       "25%             10.000000  \n",
       "50%             10.000000  \n",
       "75%             10.000000  \n",
       "max            170.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the info of 'twitter_data'\n",
    "twitter_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are duplicated and nulls tweets\n",
    "sum(twitter_data.tweet_id.duplicated()), sum(twitter_data.tweet_id.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       745\n",
       "a           55\n",
       "Charlie     12\n",
       "Lucy        11\n",
       "Cooper      11\n",
       "Oliver      11\n",
       "Penny       10\n",
       "Lola        10\n",
       "Tucker      10\n",
       "Bo           9\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table with the different names\n",
    "twitter_data.name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of retweeted tweets\n",
    "sum((twitter_data.retweeted_status_id.isna()) == False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None     2259\n",
       "doggo      97\n",
       "Name: doggo, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count values for 'doggo'\n",
    "twitter_data.doggo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       2346\n",
       "floofer      10\n",
       "Name: floofer, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count values for 'floofer'\n",
    "twitter_data.floofer.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None      2099\n",
       "pupper     257\n",
       "Name: pupper, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count values for 'pupper'\n",
    "twitter_data.pupper.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None     2326\n",
       "puppo      30\n",
       "Name: puppo, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count values for 'puppo'\n",
    "twitter_data.puppo.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing `img_pred`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Bernese_mountain_dog</td>\n",
       "      <td>0.651137</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.263788</td>\n",
       "      <td>True</td>\n",
       "      <td>Greater_Swiss_Mountain_dog</td>\n",
       "      <td>0.016199</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>box_turtle</td>\n",
       "      <td>0.933012</td>\n",
       "      <td>False</td>\n",
       "      <td>mud_turtle</td>\n",
       "      <td>0.045885</td>\n",
       "      <td>False</td>\n",
       "      <td>terrapin</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.692517</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.058279</td>\n",
       "      <td>True</td>\n",
       "      <td>fur_coat</td>\n",
       "      <td>0.054449</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>shopping_cart</td>\n",
       "      <td>0.962465</td>\n",
       "      <td>False</td>\n",
       "      <td>shopping_basket</td>\n",
       "      <td>0.014594</td>\n",
       "      <td>False</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.007959</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>666058600524156928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_poodle</td>\n",
       "      <td>0.201493</td>\n",
       "      <td>True</td>\n",
       "      <td>komondor</td>\n",
       "      <td>0.192305</td>\n",
       "      <td>True</td>\n",
       "      <td>soft-coated_wheaten_terrier</td>\n",
       "      <td>0.082086</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",
       "5  666050758794694657  https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg   \n",
       "6  666051853826850816  https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg   \n",
       "7  666055525042405380  https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg   \n",
       "8  666057090499244032  https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg   \n",
       "9  666058600524156928  https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.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",
       "5        1    Bernese_mountain_dog  0.651137    True    English_springer   \n",
       "6        1              box_turtle  0.933012   False          mud_turtle   \n",
       "7        1                    chow  0.692517    True     Tibetan_mastiff   \n",
       "8        1           shopping_cart  0.962465   False     shopping_basket   \n",
       "9        1        miniature_poodle  0.201493    True            komondor   \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",
       "5  0.263788    True   Greater_Swiss_Mountain_dog  0.016199    True  \n",
       "6  0.045885   False                     terrapin  0.017885   False  \n",
       "7  0.058279    True                     fur_coat  0.054449   False  \n",
       "8  0.014594   False             golden_retriever  0.007959    True  \n",
       "9  0.192305    True  soft-coated_wheaten_terrier  0.082086    True  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the header of 'img_pred'\n",
    "img_pred.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
      "tweet_id    2075 non-null int64\n",
      "jpg_url     2075 non-null object\n",
      "img_num     2075 non-null int64\n",
      "p1          2075 non-null object\n",
      "p1_conf     2075 non-null float64\n",
      "p1_dog      2075 non-null bool\n",
      "p2          2075 non-null object\n",
      "p2_conf     2075 non-null float64\n",
      "p2_dog      2075 non-null bool\n",
      "p3          2075 non-null object\n",
      "p3_conf     2075 non-null float64\n",
      "p3_dog      2075 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Show the info of 'img_pred'\n",
    "img_pred.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.384514e+17</td>\n",
       "      <td>1.203855</td>\n",
       "      <td>0.594548</td>\n",
       "      <td>1.345886e-01</td>\n",
       "      <td>6.032417e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.785203e+16</td>\n",
       "      <td>0.561875</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>1.006657e-01</td>\n",
       "      <td>5.090593e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.764835e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364412</td>\n",
       "      <td>5.388625e-02</td>\n",
       "      <td>1.622240e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.119988e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588230</td>\n",
       "      <td>1.181810e-01</td>\n",
       "      <td>4.944380e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.932034e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843855</td>\n",
       "      <td>1.955655e-01</td>\n",
       "      <td>9.180755e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id      img_num      p1_conf       p2_conf       p3_conf\n",
       "count  2.075000e+03  2075.000000  2075.000000  2.075000e+03  2.075000e+03\n",
       "mean   7.384514e+17     1.203855     0.594548  1.345886e-01  6.032417e-02\n",
       "std    6.785203e+16     0.561875     0.271174  1.006657e-01  5.090593e-02\n",
       "min    6.660209e+17     1.000000     0.044333  1.011300e-08  1.740170e-10\n",
       "25%    6.764835e+17     1.000000     0.364412  5.388625e-02  1.622240e-02\n",
       "50%    7.119988e+17     1.000000     0.588230  1.181810e-01  4.944380e-02\n",
       "75%    7.932034e+17     1.000000     0.843855  1.955655e-01  9.180755e-02\n",
       "max    8.924206e+17     4.000000     1.000000  4.880140e-01  2.734190e-01"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the description of 'img_pred'\n",
    "img_pred.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are duplicated and nulls tweets\n",
    "sum(img_pred.tweet_id.duplicated()), sum(img_pred.tweet_id.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing `api_data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38827</td>\n",
       "      <td>8595</td>\n",
       "      <td>892420643555336193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33260</td>\n",
       "      <td>6316</td>\n",
       "      <td>892177421306343426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25056</td>\n",
       "      <td>4192</td>\n",
       "      <td>891815181378084864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42206</td>\n",
       "      <td>8707</td>\n",
       "      <td>891689557279858688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40353</td>\n",
       "      <td>9480</td>\n",
       "      <td>891327558926688256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   favorite_count  retweet_count            tweet_id\n",
       "0           38827           8595  892420643555336193\n",
       "1           33260           6316  892177421306343426\n",
       "2           25056           4192  891815181378084864\n",
       "3           42206           8707  891689557279858688\n",
       "4           40353           9480  891327558926688256"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the header of 'api_data'\n",
    "api_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2345 entries, 0 to 2344\n",
      "Data columns (total 3 columns):\n",
      "favorite_count    2345 non-null int64\n",
      "retweet_count     2345 non-null int64\n",
      "tweet_id          2345 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 55.0 KB\n"
     ]
    }
   ],
   "source": [
    "# Show the info of 'api_data'\n",
    "api_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2345.000000</td>\n",
       "      <td>2345.000000</td>\n",
       "      <td>2.345000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8072.560341</td>\n",
       "      <td>3027.631983</td>\n",
       "      <td>7.422940e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12146.311778</td>\n",
       "      <td>5036.008926</td>\n",
       "      <td>6.833642e+16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.660209e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1404.000000</td>\n",
       "      <td>607.000000</td>\n",
       "      <td>6.783802e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3541.000000</td>\n",
       "      <td>1414.000000</td>\n",
       "      <td>7.189392e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9980.000000</td>\n",
       "      <td>3525.000000</td>\n",
       "      <td>7.986979e+17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>143493.000000</td>\n",
       "      <td>77409.000000</td>\n",
       "      <td>8.924206e+17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       favorite_count  retweet_count      tweet_id\n",
       "count     2345.000000    2345.000000  2.345000e+03\n",
       "mean      8072.560341    3027.631983  7.422940e+17\n",
       "std      12146.311778    5036.008926  6.833642e+16\n",
       "min          0.000000       0.000000  6.660209e+17\n",
       "25%       1404.000000     607.000000  6.783802e+17\n",
       "50%       3541.000000    1414.000000  7.189392e+17\n",
       "75%       9980.000000    3525.000000  7.986979e+17\n",
       "max     143493.000000   77409.000000  8.924206e+17"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the description of 'api_data'\n",
    "api_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are duplicated and nulls tweets\n",
    "sum(api_data.tweet_id.duplicated()), sum(api_data.tweet_id.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='quality_issues'></a>\n",
    "### Quality Issues\n",
    "\n",
    "1. [Q1 - Tweets without images](#q1)\n",
    "    - In the dataframe `twitter_data` there are some tweets that they don't have images, so we don't have predicted data for them.\n",
    "\n",
    "<br>\n",
    "\n",
    "2. [Q2 - Wrong expanded urls](#q2)\n",
    "    - In the dataframe `twitter_data` in some cases the url is wrong and also there in some cases there are multiple URL's.\n",
    "    \n",
    "<br>\n",
    "\n",
    "3. [Q3 - Wrong names of dogs](#q3)\n",
    "    - In the dataframe `twitter_data` in some cases the name of the dog is wrong (like \"a\", \"an\", \"by\", etc.) and also there are some that are missing or are defined like \"None\".\n",
    "\n",
    "<br>\n",
    "\n",
    "4. [Q4 - Remove retweets](#q4)\n",
    "    - In the dataframe `twitter_data` there are some tweets that they are retweets from other tweets. We only want the original ratings.\n",
    "\n",
    "<br>\n",
    "\n",
    "5. [Q5 - Wrong breed of dogs](#q5)\n",
    "    - In the dataframe `img_pred` some of the predictions are not a breed of a dog (like for example \"shopping_cart\").\n",
    "\n",
    "<br>\n",
    "\n",
    "6. [Q6 - Wrong data type for the timestamp](#q6)\n",
    "    - In the dataframe `twitter_data` the column `timestamp` has the data type `object` and it should be `datetime64`.\n",
    "\n",
    "<br>\n",
    "\n",
    "7. [Q7 - Wrong data type for the tweet id](#q7)\n",
    "    - In all 3 dataframes the `tweet_id` has the data type `int`, the id's should be a `string`.\n",
    "\n",
    "<br>\n",
    "\n",
    "8. [Q8 - Missing values](#q8)\n",
    "    - In the dataframe `twitter_data` the columns `in_reply_to_status_id`, `in_reply_to_user_id`, `retweeted_status_id`, `retweeted_status_user_id` and `retweeted_status_timestamp` they have a lot of missing values.\n",
    "\n",
    "<br>\n",
    "\n",
    "<a id='tidiness_issues'></a>\n",
    "### Tidiness Issues\n",
    "\n",
    "1. [T1 - Multiple columns for the dogo stage](#t1)\n",
    "    - In the dataframe `twitter_data` the dogo stage is splitted in 4 columns (doggo, floofer, pupper, puppo).\n",
    "\n",
    "<br>\n",
    "\n",
    "2. [T2 - Merge all the data](#t2)\n",
    "    - Merge all 3 dataframes using the `tweet_id` column in a unique dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='clean'></a>\n",
    "## Cleaning data\n",
    "\n",
    "In this part I will clean all the quality and tidiness issues.\n",
    "\n",
    "But before doing any change, I will do a copy of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of all the original dataframes\n",
    "twitter_data_clean = twitter_data.copy()\n",
    "img_pred_clean = img_pred.copy()\n",
    "api_data_clean = api_data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have done the copies, let's do the cleaning on the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q1'></a>\n",
    "### Q1 - Tweets without images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "**Problem:** In the dataframe `twitter_data` there are some tweets that they don't have images, so we don't have predicted data for them.\n",
    "\n",
    "**Solution:** Remove the tweets that we don't have image prediction for them. To do this I found help in this [page](https://stackoverflow.com/questions/19960077/how-to-filter-pandas-dataframe-using-in-and-not-in-like-in-sql).\n",
    "\n",
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2356"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of rows in \"twitter_data\" before removing the tweets that we don't have prediction data\n",
    "len(twitter_data_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create and array with the list of all the tweets that we have image prediciton\n",
    "list_tweet_id = img_pred_clean.tweet_id\n",
    "\n",
    "# Filter only the tweets that we have prediction data\n",
    "twitter_data_clean.query('tweet_id in @list_tweet_id', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2075"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of rows in \"twitter_data\" after removing the tweets that we don't have prediction data\n",
    "len(twitter_data_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q2'></a>\n",
    "### Q2 - Wrong expanded urls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "**Problem:** In the dataframe `twitter_data` in some cases the url is wrong and also there in some cases there are multiple URL's.\n",
    "\n",
    "**Solution:** We assume that the format of the URL is always the same and it only change the ID of each tweet. Using regex I will extract the correct url from all the tweets and copy this clean urls in a new column called `clean_urls`. To do this I found help in this [page](https://stackoverflow.com/questions/1327369/extract-part-of-a-regex-match).\n",
    "\n",
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    https://twitter.com/dog_rates/status/892420643...\n",
       "1    https://twitter.com/dog_rates/status/892177421...\n",
       "2    https://twitter.com/dog_rates/status/891815181...\n",
       "3    https://twitter.com/dog_rates/status/891689557...\n",
       "4    https://twitter.com/dog_rates/status/891327558...\n",
       "5    https://twitter.com/dog_rates/status/891087950...\n",
       "6    https://gofundme.com/ydvmve-surgery-for-jax,ht...\n",
       "7    https://twitter.com/dog_rates/status/890729181...\n",
       "8    https://twitter.com/dog_rates/status/890609185...\n",
       "9    https://twitter.com/dog_rates/status/890240255...\n",
       "Name: expanded_urls, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 10 expanded_urls\n",
    "twitter_data_clean.expanded_urls.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract url based with regex\n",
    "twitter_data_clean['clean_urls'] = twitter_data_clean['expanded_urls'].str.\\\n",
    "                                   extract('(https://twitter.com/dog_rates/status/\\d{1,18}/photo/1)', expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the column \"expanded_urls\"\n",
    "twitter_data_clean.drop('expanded_urls', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    https://twitter.com/dog_rates/status/892420643...\n",
       "1    https://twitter.com/dog_rates/status/892177421...\n",
       "2    https://twitter.com/dog_rates/status/891815181...\n",
       "3    https://twitter.com/dog_rates/status/891689557...\n",
       "4    https://twitter.com/dog_rates/status/891327558...\n",
       "5    https://twitter.com/dog_rates/status/891087950...\n",
       "6    https://twitter.com/dog_rates/status/890971913...\n",
       "7    https://twitter.com/dog_rates/status/890729181...\n",
       "8    https://twitter.com/dog_rates/status/890609185...\n",
       "9    https://twitter.com/dog_rates/status/890240255...\n",
       "Name: clean_urls, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first 10 clean_urls\n",
    "twitter_data_clean.clean_urls.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q3'></a>\n",
    "### Q3 - Wrong names of dogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "**Problem:** In the dataframe `twitter_data` in some cases the name of the dog is wrong (like \"a\", \"an\", \"by\", etc.) and also there are some that are missing or are defined like \"None\".\n",
    "\n",
    "**Solution:** In this case I will assume that all the dog names start with a upper case letter, so all the names that starts with a lower case I will put `No name`. To do this, I found help in this [page](https://stackoverflow.com/questions/50599934/removing-rows-from-dataframe-whose-first-letter-is-in-lowercase).\n",
    "\n",
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22           such\n",
       "56              a\n",
       "169         quite\n",
       "193         quite\n",
       "369           one\n",
       "542    incredibly\n",
       "649             a\n",
       "759            an\n",
       "801             a\n",
       "819          very\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the list of names that start with lower case\n",
    "twitter_data_clean[twitter_data_clean['name'].str[0].str.islower()].name.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace all the names that they start with lower case with \"No name\"\n",
    "twitter_data_clean.loc[twitter_data_clean['name'].str[0].str.islower(), 'name'] = \"No name\"\n",
    "\n",
    "# Replace all the names that they are \"None\" with \"No name\"\n",
    "twitter_data_clean.loc[twitter_data_clean['name'] == \"None\", 'name'] = \"No name\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are any name that starts with a lower case\n",
    "len(twitter_data_clean[twitter_data_clean['name'].str[0].str.islower()]) == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No name    678\n",
       "Charlie     11\n",
       "Cooper      10\n",
       "Penny       10\n",
       "Lucy        10\n",
       "Oliver      10\n",
       "Tucker      10\n",
       "Winston      8\n",
       "Bo           8\n",
       "Lola         8\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first 10 most popular names\n",
    "twitter_data_clean.name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q4'></a>\n",
    "### Q4 - Remove retweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "**Problem:** In the dataframe `twitter_data` there are some tweets that they are retweets from other tweets. We only want the original tweets.\n",
    "\n",
    "**Solution:** Remove the tweets that they have a tweet id in the column `retweeted_status_id`\n",
    "\n",
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First check how many rows they are retweets\n",
    "sum((twitter_data_clean.retweeted_status_id.notnull()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2075"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of tweets in the dataframe\n",
    "twitter_data_clean.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First check how many rows they are retweets\n",
    "twitter_data_clean = twitter_data_clean[twitter_data_clean.retweeted_status_id.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1994"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check again the number of tweets in the dataframe after removing the retweets\n",
    "twitter_data_clean.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are not retweets\n",
    "sum((twitter_data_clean.retweeted_status_id.notnull())) == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q5'></a>\n",
    "### Q5 - Wrong breed of dogs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "**Problem:** In the dataframe `img_pred` some of the predictions are not a breed of a dog (like for example \"shopping_cart\").\n",
    "\n",
    "**Solution:** In the case that the prediction is not a dog, insted of put another value that is not a dog I will put \"Not a dog\". This can be usefull later to aggragate the data and plot. To know if the prediction is a dog or not I will use the column `pX_dog` on `X` can be 1,2 and 3. \n",
    "\n",
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(543, 522, 576)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the number of rows that are not dog for each prediction\n",
    "len(img_pred_clean) - img_pred_clean.p1_dog.sum(),\\\n",
    "len(img_pred_clean) - img_pred_clean.p2_dog.sum(),\\\n",
    "len(img_pred_clean) - img_pred_clean.p3_dog.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0, 0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the number of rows that are not dog for each prediction\n",
    "len(img_pred_clean.query('p1 == \"Not a dog\"')),\\\n",
    "len(img_pred_clean.query('p2 == \"Not a dog\"')),\\\n",
    "len(img_pred_clean.query('p3 == \"Not a dog\"'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace all the predictions that are not a dog breed for \"Not a dog\"\n",
    "img_pred_clean.loc[img_pred_clean.p1_dog == False, 'p1'] = \"Not a dog\"\n",
    "img_pred_clean.loc[img_pred_clean.p2_dog == False, 'p2'] = \"Not a dog\"\n",
    "img_pred_clean.loc[img_pred_clean.p3_dog == False, 'p3'] = \"Not a dog\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(543, 522, 576)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the number of rows that are not dog for each prediction\n",
    "len(img_pred_clean.query('p1 == \"Not a dog\"')),\\\n",
    "len(img_pred_clean.query('p2 == \"Not a dog\"')),\\\n",
    "len(img_pred_clean.query('p3 == \"Not a dog\"'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q6'></a>\n",
    "### Q6 - Wrong data type for the timestamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "**Problem:** In the dataframe `twitter_data` the column `timestamp` has the data type `object` and it should be `datetime64`.\n",
    "\n",
    "**Solution:** Change the datatype of the column `timestamp` to `datetime64`\n",
    "\n",
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1994 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      1994 non-null int64\n",
      "in_reply_to_status_id         23 non-null float64\n",
      "in_reply_to_user_id           23 non-null float64\n",
      "timestamp                     1994 non-null object\n",
      "source                        1994 non-null object\n",
      "text                          1994 non-null object\n",
      "retweeted_status_id           0 non-null float64\n",
      "retweeted_status_user_id      0 non-null float64\n",
      "retweeted_status_timestamp    0 non-null object\n",
      "rating_numerator              1994 non-null int64\n",
      "rating_denominator            1994 non-null int64\n",
      "name                          1994 non-null object\n",
      "doggo                         1994 non-null object\n",
      "floofer                       1994 non-null object\n",
      "pupper                        1994 non-null object\n",
      "puppo                         1994 non-null object\n",
      "clean_urls                    1920 non-null object\n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 280.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the data type of \"timestamp\" before change the data type\n",
    "twitter_data_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the format of the column \"timestamp\" to datetime\n",
    "twitter_data_clean['timestamp'] = pd.to_datetime(twitter_data_clean['timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1994 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      1994 non-null int64\n",
      "in_reply_to_status_id         23 non-null float64\n",
      "in_reply_to_user_id           23 non-null float64\n",
      "timestamp                     1994 non-null datetime64[ns]\n",
      "source                        1994 non-null object\n",
      "text                          1994 non-null object\n",
      "retweeted_status_id           0 non-null float64\n",
      "retweeted_status_user_id      0 non-null float64\n",
      "retweeted_status_timestamp    0 non-null object\n",
      "rating_numerator              1994 non-null int64\n",
      "rating_denominator            1994 non-null int64\n",
      "name                          1994 non-null object\n",
      "doggo                         1994 non-null object\n",
      "floofer                       1994 non-null object\n",
      "pupper                        1994 non-null object\n",
      "puppo                         1994 non-null object\n",
      "clean_urls                    1920 non-null object\n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(9)\n",
      "memory usage: 280.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the data type of \"timestamp\" after change the data type\n",
    "twitter_data_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q7'></a>\n",
    "### Q7 - Wrong data type for the tweet id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "**Problem:** In all 3 dataframes the `tweet_id` has the datatype `int`. The id's should be a `string`.\n",
    "\n",
    "**Solution:** Change the data type of the column `tweet_id` from `int` to `string` in all 3 dataframes\n",
    "\n",
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1994 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      1994 non-null int64\n",
      "in_reply_to_status_id         23 non-null float64\n",
      "in_reply_to_user_id           23 non-null float64\n",
      "timestamp                     1994 non-null datetime64[ns]\n",
      "source                        1994 non-null object\n",
      "text                          1994 non-null object\n",
      "retweeted_status_id           0 non-null float64\n",
      "retweeted_status_user_id      0 non-null float64\n",
      "retweeted_status_timestamp    0 non-null object\n",
      "rating_numerator              1994 non-null int64\n",
      "rating_denominator            1994 non-null int64\n",
      "name                          1994 non-null object\n",
      "doggo                         1994 non-null object\n",
      "floofer                       1994 non-null object\n",
      "pupper                        1994 non-null object\n",
      "puppo                         1994 non-null object\n",
      "clean_urls                    1920 non-null object\n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(9)\n",
      "memory usage: 280.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the data type of \"tweet_id\" before change the data type\n",
    "twitter_data_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      "tweet_id    2075 non-null int64\n",
      "jpg_url     2075 non-null object\n",
      "img_num     2075 non-null int64\n",
      "p1          2075 non-null object\n",
      "p1_conf     2075 non-null float64\n",
      "p1_dog      2075 non-null bool\n",
      "p2          2075 non-null object\n",
      "p2_conf     2075 non-null float64\n",
      "p2_dog      2075 non-null bool\n",
      "p3          2075 non-null object\n",
      "p3_conf     2075 non-null float64\n",
      "p3_dog      2075 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the data type of \"tweet_id\" before change the data type\n",
    "img_pred_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2345 entries, 0 to 2344\n",
      "Data columns (total 3 columns):\n",
      "favorite_count    2345 non-null int64\n",
      "retweet_count     2345 non-null int64\n",
      "tweet_id          2345 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 55.0 KB\n"
     ]
    }
   ],
   "source": [
    "# Check the data type of \"tweet_id\" before change the data type\n",
    "api_data_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the datatype of the column \"tweet_id\" to string\n",
    "twitter_data_clean['tweet_id'] = twitter_data_clean['tweet_id'].astype(str)\n",
    "img_pred_clean['tweet_id'] = img_pred_clean['tweet_id'].astype(str)\n",
    "api_data_clean['tweet_id'] = api_data_clean['tweet_id'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1994 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      1994 non-null object\n",
      "in_reply_to_status_id         23 non-null float64\n",
      "in_reply_to_user_id           23 non-null float64\n",
      "timestamp                     1994 non-null datetime64[ns]\n",
      "source                        1994 non-null object\n",
      "text                          1994 non-null object\n",
      "retweeted_status_id           0 non-null float64\n",
      "retweeted_status_user_id      0 non-null float64\n",
      "retweeted_status_timestamp    0 non-null object\n",
      "rating_numerator              1994 non-null int64\n",
      "rating_denominator            1994 non-null int64\n",
      "name                          1994 non-null object\n",
      "doggo                         1994 non-null object\n",
      "floofer                       1994 non-null object\n",
      "pupper                        1994 non-null object\n",
      "puppo                         1994 non-null object\n",
      "clean_urls                    1920 non-null object\n",
      "dtypes: datetime64[ns](1), float64(4), int64(2), object(10)\n",
      "memory usage: 280.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the data type of \"tweet_id\" after change the data type\n",
    "twitter_data_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      "tweet_id    2075 non-null object\n",
      "jpg_url     2075 non-null object\n",
      "img_num     2075 non-null int64\n",
      "p1          2075 non-null object\n",
      "p1_conf     2075 non-null float64\n",
      "p1_dog      2075 non-null bool\n",
      "p2          2075 non-null object\n",
      "p2_conf     2075 non-null float64\n",
      "p2_dog      2075 non-null bool\n",
      "p3          2075 non-null object\n",
      "p3_conf     2075 non-null float64\n",
      "p3_dog      2075 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(1), object(5)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the data type of \"tweet_id\" after change the data type\n",
    "img_pred_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2345 entries, 0 to 2344\n",
      "Data columns (total 3 columns):\n",
      "favorite_count    2345 non-null int64\n",
      "retweet_count     2345 non-null int64\n",
      "tweet_id          2345 non-null object\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 55.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the data type of \"tweet_id\" after change the data type\n",
    "api_data_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='q8'></a>\n",
    "### Q8 - Missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "**Problem:** In the dataframe `twitter_data` the columns `in_reply_to_status_id`, `in_reply_to_user_id`, `retweeted_status_id`, `retweeted_status_user_id` and `retweeted_status_timestamp` they have a lot of missing values.\n",
    "\n",
    "**Solution:** In this case, apart that this columns contain a lot of missing values, I don't consider them relevant for the purpose of this analysis so I will remove them.\n",
    "\n",
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nº of columns in \"twitter_data\" before removing the 5 columns\n",
    "len(list(twitter_data_clean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the column \"in_reply_to_status_id\"\n",
    "twitter_data_clean.drop('in_reply_to_status_id', axis = 1, inplace = True)\n",
    "\n",
    "# Remove the column \"in_reply_to_user_id\"\n",
    "twitter_data_clean.drop('in_reply_to_user_id', axis = 1, inplace = True)\n",
    "\n",
    "# Remove the column \"retweeted_status_id\"\n",
    "twitter_data_clean.drop('retweeted_status_id', axis = 1, inplace = True)\n",
    "\n",
    "# Remove the column \"retweeted_status_user_id\"\n",
    "twitter_data_clean.drop('retweeted_status_user_id', axis = 1, inplace = True)\n",
    "\n",
    "# Remove the column \"retweeted_status_timestamp\"\n",
    "twitter_data_clean.drop('retweeted_status_timestamp', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nº of columns in \"twitter_data\" after removing the 5 columns\n",
    "len(list(twitter_data_clean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='t1'></a>\n",
    "### T1 - Multiple columns for the dogo stage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "**Problem:** In the dataframe `twitter_data` the dogo stage is splitted in 4 columns (doggo, floofer, pupper, puppo).\n",
    "\n",
    "**Solution:** Remove the 4 columns and put all the data in a unique column called `dogo_stage`\n",
    "\n",
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None     1920\n",
       "doggo      74\n",
       "Name: doggo, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count values for 'doggo'\n",
    "twitter_data_clean.doggo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       1986\n",
       "floofer       8\n",
       "Name: floofer, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count values for 'floofer'\n",
    "twitter_data_clean.floofer.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None      1782\n",
       "pupper     212\n",
       "Name: pupper, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count values for 'pupper'\n",
    "twitter_data_clean.pupper.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None     1971\n",
       "puppo      23\n",
       "Name: puppo, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count values for 'puppo'\n",
    "twitter_data_clean.puppo.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace all the \"None\" to \"\"\n",
    "twitter_data_clean.doggo.replace('None', '', inplace = True)\n",
    "twitter_data_clean.floofer.replace('None', '', inplace = True)\n",
    "twitter_data_clean.pupper.replace('None', '', inplace = True)\n",
    "twitter_data_clean.puppo.replace('None', '', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column by summing all the values from the 4 columns\n",
    "twitter_data_clean['dogo_stage'] = twitter_data_clean.doggo + twitter_data_clean.floofer +\\\n",
    "                                   twitter_data_clean.pupper + twitter_data_clean.puppo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                1688\n",
       "pupper           203\n",
       "doggo             63\n",
       "puppo             22\n",
       "doggopupper        9\n",
       "floofer            7\n",
       "doggofloofer       1\n",
       "doggopuppo         1\n",
       "Name: dogo_stage, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different values of the new column \"dogo_stage\"\n",
    "twitter_data_clean.dogo_stage.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there are some tweets that the it has multiple stages. Some of them like `doggo` and `pupper` they are oposit and in 12 cases the dog has this 2 stages. Now this it's not any more a tidiness issue, now it's a quality issue. To solve that quickly I will remove the cases that they have multiple stages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the dogo_stage of the dogs that they have multiple stages\n",
    "twitter_data_clean.dogo_stage.replace('doggopupper', '', inplace = True)\n",
    "twitter_data_clean.dogo_stage.replace('doggopuppo', '', inplace = True)\n",
    "twitter_data_clean.dogo_stage.replace('doggofloofer', '', inplace = True)\n",
    "\n",
    "# Replace all the tweets that they don't have doggo with \"No doggo\"\n",
    "twitter_data_clean.dogo_stage.replace('', 'No doggo', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally I will remove the 4 stages columns that I don't need more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of columns before removing the 4 stage columns\n",
    "len(list(twitter_data_clean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the column \"doggo\"\n",
    "twitter_data_clean.drop('doggo', axis = 1, inplace = True)\n",
    "\n",
    "# Remove the column \"floofer\"\n",
    "twitter_data_clean.drop('floofer', axis = 1, inplace = True)\n",
    "\n",
    "# Remove the column \"pupper\"\n",
    "twitter_data_clean.drop('pupper', axis = 1, inplace = True)\n",
    "\n",
    "# Remove the column \"puppo\"\n",
    "twitter_data_clean.drop('puppo', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No doggo    1699\n",
       "pupper       203\n",
       "doggo         63\n",
       "puppo         22\n",
       "floofer        7\n",
       "Name: dogo_stage, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different values of the new column \"dogo_stage\"\n",
    "twitter_data_clean.dogo_stage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of columns after removing the 4 stage columns\n",
    "len(list(twitter_data_clean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='t2'></a>\n",
    "### T2 - Merge all the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "\n",
    "**Problem:** Merge all 3 dataframes using the `tweet_id` column in a unique dataframe.\n",
    "\n",
    "**Solution:** I will create a new dataframe called `full_data` in where I will join all the 3 dataframes using the column `tweet_id`.\n",
    "\n",
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1994, 2075, 2345)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the number of rows of each dataframe\n",
    "len(twitter_data_clean), len(img_pred_clean), len(api_data_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the dataframe `api_data_clean` we have more data because we didn't removed any row. Now using `INNER JOIN` we will only use the data we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     tweet_id\n",
       "23    tweet_id\n",
       "dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the names of columns that are in all the dataframes\n",
    "all_columns = pd.Series(list(twitter_data_clean) + list(img_pred_clean) + list(api_data_clean))\n",
    "all_columns[all_columns.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the only column that is shared between the 3 dataframes is the `tweet_id`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the 3 dataframes using the column \"tweet_id\"\n",
    "full_data = pd.merge(twitter_data_clean, img_pred_clean, on=['tweet_id'], how='inner')\n",
    "full_data = pd.merge(full_data, api_data_clean, on=['tweet_id'], how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>clean_urls</th>\n",
       "      <th>dogo_stage</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>...</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>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56</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>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>No doggo</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>...</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>Not a dog</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>Not a dog</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "      <td>38827</td>\n",
       "      <td>8595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27</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>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>No doggo</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>...</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",
       "      <td>33260</td>\n",
       "      <td>6316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03</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>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>No doggo</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>...</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",
       "      <td>25056</td>\n",
       "      <td>4192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51</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>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>No doggo</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>...</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>Not a dog</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "      <td>42206</td>\n",
       "      <td>8707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24</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>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>No doggo</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>...</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",
       "      <td>40353</td>\n",
       "      <td>9480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id           timestamp  \\\n",
       "0  892420643555336193 2017-08-01 16:23:56   \n",
       "1  892177421306343426 2017-08-01 00:17:27   \n",
       "2  891815181378084864 2017-07-31 00:18:03   \n",
       "3  891689557279858688 2017-07-30 15:58:51   \n",
       "4  891327558926688256 2017-07-29 16:00:24   \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  rating_numerator  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                13   \n",
       "1  This is Tilly. She's just checking pup on you....                13   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                12   \n",
       "3  This is Darla. She commenced a snooze mid meal...                13   \n",
       "4  This is Franklin. He would like you to stop ca...                12   \n",
       "\n",
       "   rating_denominator      name  \\\n",
       "0                  10   Phineas   \n",
       "1                  10     Tilly   \n",
       "2                  10    Archie   \n",
       "3                  10     Darla   \n",
       "4                  10  Franklin   \n",
       "\n",
       "                                          clean_urls dogo_stage  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...   No doggo   \n",
       "1  https://twitter.com/dog_rates/status/892177421...   No doggo   \n",
       "2  https://twitter.com/dog_rates/status/891815181...   No doggo   \n",
       "3  https://twitter.com/dog_rates/status/891689557...   No doggo   \n",
       "4  https://twitter.com/dog_rates/status/891327558...   No doggo   \n",
       "\n",
       "                                           jpg_url      ...         p1_conf  \\\n",
       "0  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg      ...        0.097049   \n",
       "1  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg      ...        0.323581   \n",
       "2  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg      ...        0.716012   \n",
       "3  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg      ...        0.170278   \n",
       "4  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg      ...        0.555712   \n",
       "\n",
       "  p1_dog                  p2   p2_conf p2_dog                           p3  \\\n",
       "0  False           Not a dog  0.085851  False                    Not a dog   \n",
       "1   True            Pekinese  0.090647   True                     papillon   \n",
       "2   True            malamute  0.078253   True                       kelpie   \n",
       "3  False  Labrador_retriever  0.168086   True                    Not a dog   \n",
       "4   True    English_springer  0.225770   True  German_short-haired_pointer   \n",
       "\n",
       "    p3_conf p3_dog  favorite_count  retweet_count  \n",
       "0  0.076110  False           38827           8595  \n",
       "1  0.068957   True           33260           6316  \n",
       "2  0.031379   True           25056           4192  \n",
       "3  0.040836  False           42206           8707  \n",
       "4  0.175219   True           40353           9480  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the header of \"full_data\"\n",
    "full_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1994 entries, 0 to 1993\n",
      "Data columns (total 22 columns):\n",
      "tweet_id              1994 non-null object\n",
      "timestamp             1994 non-null datetime64[ns]\n",
      "source                1994 non-null object\n",
      "text                  1994 non-null object\n",
      "rating_numerator      1994 non-null int64\n",
      "rating_denominator    1994 non-null int64\n",
      "name                  1994 non-null object\n",
      "clean_urls            1920 non-null object\n",
      "dogo_stage            1994 non-null object\n",
      "jpg_url               1994 non-null object\n",
      "img_num               1994 non-null int64\n",
      "p1                    1994 non-null object\n",
      "p1_conf               1994 non-null float64\n",
      "p1_dog                1994 non-null bool\n",
      "p2                    1994 non-null object\n",
      "p2_conf               1994 non-null float64\n",
      "p2_dog                1994 non-null bool\n",
      "p3                    1994 non-null object\n",
      "p3_conf               1994 non-null float64\n",
      "p3_dog                1994 non-null bool\n",
      "favorite_count        1994 non-null int64\n",
      "retweet_count         1994 non-null int64\n",
      "dtypes: bool(3), datetime64[ns](1), float64(3), int64(5), object(10)\n",
      "memory usage: 317.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Show the information of \"full_data\"\n",
    "full_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='store'></a>\n",
    "## Storing data\n",
    "\n",
    "<a id='store_csv'></a>\n",
    "### Storing data into a CSV file\n",
    "\n",
    "To store the data in a CSV file I will just use the function `to_csv` from `pandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the dataframe \"full_data\" into a CSV file\n",
    "full_data.to_csv('twitter_archive_master.csv', encoding = 'utf-8', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='store_sql'></a>\n",
    "### Storing data into a SQLite database\n",
    "\n",
    "To store the data into a database I will create a small database and store the dataframe `full_data` in a table. To do this, I found some help in this [page](https://stackoverflow.com/questions/50803109/how-to-store-pandas-dataframe-in-sqlite-db)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the connexion to the database\n",
    "conn = sqlite3.connect('twitter_archive_master.sqlite')\n",
    "\n",
    "# Save the dataframe \"full_data\" into the SQLite database\n",
    "full_data.to_sql('twitter_data', conn, if_exists='replace', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='analysis'></a>\n",
    "## Analysing data\n",
    "\n",
    "In the analysis part I will show some insights and visualizations of the dataframe `full_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1.994000e+03</td>\n",
       "      <td>1.994000e+03</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.280843</td>\n",
       "      <td>10.532096</td>\n",
       "      <td>1.203109</td>\n",
       "      <td>0.593941</td>\n",
       "      <td>1.344195e-01</td>\n",
       "      <td>6.024848e-02</td>\n",
       "      <td>8855.960381</td>\n",
       "      <td>2719.236209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>41.497718</td>\n",
       "      <td>7.320710</td>\n",
       "      <td>0.560777</td>\n",
       "      <td>0.271954</td>\n",
       "      <td>1.006807e-01</td>\n",
       "      <td>5.089067e-02</td>\n",
       "      <td>12574.715334</td>\n",
       "      <td>4695.079382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.362857</td>\n",
       "      <td>5.393988e-02</td>\n",
       "      <td>1.619283e-02</td>\n",
       "      <td>1926.500000</td>\n",
       "      <td>608.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.587635</td>\n",
       "      <td>1.174550e-01</td>\n",
       "      <td>4.950530e-02</td>\n",
       "      <td>4048.000000</td>\n",
       "      <td>1312.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846285</td>\n",
       "      <td>1.951377e-01</td>\n",
       "      <td>9.159438e-02</td>\n",
       "      <td>11156.500000</td>\n",
       "      <td>3125.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "      <td>143493.000000</td>\n",
       "      <td>77409.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rating_numerator  rating_denominator      img_num      p1_conf  \\\n",
       "count       1994.000000         1994.000000  1994.000000  1994.000000   \n",
       "mean          12.280843           10.532096     1.203109     0.593941   \n",
       "std           41.497718            7.320710     0.560777     0.271954   \n",
       "min            0.000000            2.000000     1.000000     0.044333   \n",
       "25%           10.000000           10.000000     1.000000     0.362857   \n",
       "50%           11.000000           10.000000     1.000000     0.587635   \n",
       "75%           12.000000           10.000000     1.000000     0.846285   \n",
       "max         1776.000000          170.000000     4.000000     1.000000   \n",
       "\n",
       "            p2_conf       p3_conf  favorite_count  retweet_count  \n",
       "count  1.994000e+03  1.994000e+03     1994.000000    1994.000000  \n",
       "mean   1.344195e-01  6.024848e-02     8855.960381    2719.236209  \n",
       "std    1.006807e-01  5.089067e-02    12574.715334    4695.079382  \n",
       "min    1.011300e-08  1.740170e-10       79.000000      13.000000  \n",
       "25%    5.393988e-02  1.619283e-02     1926.500000     608.000000  \n",
       "50%    1.174550e-01  4.950530e-02     4048.000000    1312.500000  \n",
       "75%    1.951377e-01  9.159438e-02    11156.500000    3125.500000  \n",
       "max    4.880140e-01  2.734190e-01   143493.000000   77409.000000  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describre the dataframe \"full_data\"\n",
    "full_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1994 entries, 0 to 1993\n",
      "Data columns (total 22 columns):\n",
      "tweet_id              1994 non-null object\n",
      "timestamp             1994 non-null datetime64[ns]\n",
      "source                1994 non-null object\n",
      "text                  1994 non-null object\n",
      "rating_numerator      1994 non-null int64\n",
      "rating_denominator    1994 non-null int64\n",
      "name                  1994 non-null object\n",
      "clean_urls            1920 non-null object\n",
      "dogo_stage            1994 non-null object\n",
      "jpg_url               1994 non-null object\n",
      "img_num               1994 non-null int64\n",
      "p1                    1994 non-null object\n",
      "p1_conf               1994 non-null float64\n",
      "p1_dog                1994 non-null bool\n",
      "p2                    1994 non-null object\n",
      "p2_conf               1994 non-null float64\n",
      "p2_dog                1994 non-null bool\n",
      "p3                    1994 non-null object\n",
      "p3_conf               1994 non-null float64\n",
      "p3_dog                1994 non-null bool\n",
      "favorite_count        1994 non-null int64\n",
      "retweet_count         1994 non-null int64\n",
      "dtypes: bool(3), datetime64[ns](1), float64(3), int64(5), object(10)\n",
      "memory usage: 317.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Show the information of \"full_data\"\n",
    "full_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4K3TFEvA64NnAEcDrpgsmSZIkSRqHeYufqroGuG9G8/HABe3+BcBLBtovrM51wF5J9gdeCFxZVfdV1f3AlWxfUEmSJEnSslnsOT9rquougPbz8a39AOCOgem2tLa52iVJkiRpLHYd8fwyS1vtoH37GSSn0Q2ZY82aNUxNTc270DV7wIbDtw2fch7DLHMuW7duXdLrR8Uc2+tLlr7k6Jsk5wE/DtxTVT/Q2vYBLgHWApuBn6qq+5ME+GPgWODrwClV9fH2mpOB32yz/d2qugBJUu+s3Xj5ol634fBtnDLjtZvPOm4UkTQBFlv83J1k/6q6qw1ru6e1bwEOGpjuQODO1r5+RvvUbDOuqnOBcwHWrVtX69evn22yh3nzRZdy9s2jq+M2nzT/MucyNTXFMJmXmzm215csfcnRQ+cDbwEuHGibPr/wrCQb2+NX8/DzC59Nd37hswfOL1xHt4PlhiSXteG2kiRpwi122NtlwPQV204GLh1of0W76tuRwANtWNwHgRck2btd6OAFrU2SAM8vlCRJy2/ewyVJ3k131GbfJFvo9qqeBbwnyanAF4CXtcmvoBuGsoluKMorAarqviS/A1zfpvvtqpq5kSNJMz3s/MIknl8oSZIWbd7ip6pOnOOpo2aZtoDT55jPecB5C0onSbMb+/mFfTi3sA/ni5nBDH3LoPFZ7Dk6Up+M+oIHkjRKvTm/sA/nFvbhfDEzmKFvGSRpISx+JPXZ9PmFZ7H9+YVnJLmY7oIHD7QC6YPA7w98ifILgNeMObOkVSbJQXQXW/ke4DvAuVX1x4u54qRWxqiPSnn1uJ2XxY+kXvD8QkkraBuwoao+nuQxdFeKvBI4hQVccXJFkktLNGnDGS1+JPWC5xdKWintwirTF1f5apLP0F0s5XgeGkp7Ad0w2lczcMVJ4Loke00P0R13dkkLY/EjSZLUJFkLPAP4CAu/4uTDip/BC6vst99+q+LiEDu6iMUoL/qyFKO+AM1slvq7Wi0XA9m6dSsbDv/2SsfYoVGvS4sfSZIkIMmjgb8EfrmqvtKd2jP7pLO0bXdlycELqzzlKU8Z6ovbV9qOLmJxSk+GR204fNtIL0Azq5u/tqSXbzj825x97UPz6Os5RFNTUw/L2UfnH73nSC+sYvEjSZImXpJH0hU+F1XVX7XmhV5xUhqLUZ2n0x1Bm6xy4BErHUCSJGkltau3vQP4TFW9YeCp6StOwvZXnHxFOkfSrjg5tsCSFm2ySj1JkqTtPQ/4WeDmJDe2tl9ngVeclNR/Fj+SJGmiVdW1zH4eDyzwipOS+s1hb5IkSZImgsWPJEmSpIlg8SNJkiRpIlj8SJIkSZoIFj+SJEmSJoLFjyRJkqSJ4KWuJUmSpGW0duPlKx1BjUd+JEmSJE0Eix9JkiRJE8HiR5IkSdJEsPiRJEmSNBG84IEkSdJOaDEn2W84fBuneHK+dmIe+ZEkSZI0ESx+JEmSJE0Eix9JkiRJE8HiR5IkSdJEsPiRJEmSNBEsfiRJkiRNBIsfSZIkSRPB4keSJEnSRFhS8ZNkc5Kbk9yY5GOtbZ8kVya5rf3cu7UnyZuSbEpyU5JnjuINSJIkSdIwRnHk50eq6ulVta493ghcVVWHAFe1xwDHAIe022nAW0ewbEmSJEkaynIMezseuKDdvwB4yUD7hdW5Dtgryf7LsHxJOxGPMEuSpFFZavFTwN8luSHJaa1tTVXdBdB+Pr61HwDcMfDaLa1NkubjEWZJkrRkuy7x9c+rqjuTPB64MslndzBtZmmr7SbqiqjTANasWcPU1NS8IdbsARsO3zZc4iEMs8y5bN26dUmvHxVzbK8vWfqSY5U7Hljf7l8ATAGvZuAIM3Bdkr2S7D+9Q0aSJE22JRU/VXVn+3lPkvcBRwB3T29stGFt97TJtwAHDbz8QODOWeZ5LnAuwLp162r9+vXz5njzRZdy9s1LreMesvmk+Zc5l6mpKYbJvNzMsb2+ZOlLjlVk+ghzAX/S+oiHHWFuO2Bg7iPMFj+S5pTkPODHgXuq6gda2z7AJcBaYDPwU1V1f5IAfwwcC3wdOKWqPr4SuSUt3KIrhiR7Ao+oqq+2+y8Afhu4DDgZOKv9vLS95DLgjCQXA88GHnBvrKQhjPwIMyz8KHMfjjD34aihGczQtwwjcj7wFuDCgbbp4bVnJdnYHr+ahw+vfTbd8NpnjzWtpEVbyuGSNcD7uh0g7Aq8q6r+Nsn1wHuSnAp8AXhZm/4Kur0km+j2lLxyCcuWNCGW4whzm9+CjjL34QhzH44amsEMfcswClV1TZK1M5odXivthBb9n7yqbgd+cJb2LwFHzdJewOmLXZ6kyeMRZkkraMnDawePMO+3335jP0q2mKPVoz7KvRzMODqrIeeojzCPbjemJI2eR5gl9c3Qw2sHjzA/5SlPGeo85lE6ZePlC37NhsO3jfQo93Iw4+ishpznH73nSI8w9/vdSppoHmGWtIKWPLxWUv8sx5ecSpIkrXbTw2th++G1r2hfqnwkDq+VVhWP/EiSpImW5N10FzfYN8kW4HV05xQ6vFbayVj8SJKkiVZVJ87xlMNrpZ2Mw94kSZIkTQSLH0mSJEkTweJHkiRJ0kSw+JEkSZI0ESx+JEmSJE0Eix9JkiRJE8HiR5IkSdJEsPiRJEmSNBH8klNJkqSeWLvx8pWOIO3ULH5msZSOZ8Ph2zhlxus3n3XcUiNJkiRJWiKHvUmSJEmaCBY/kiRJkiaCxY8kSZKkiWDxI0mSJGkieMEDSdJ25rrwy2wXdZmPF32RJPWFR34kSZIkTQSLH0mSJEkTweJHkiRJ0kSw+JEkSZI0ESx+JEmSJE0Eix9JkiRJE8HiR5IkSdJEsPiRJEmSNBH8ktMxmOvLAhfDLwuUJEmSFsfiR5I00Razg2rD4ds4ZY7XuZNKkvpr7MPekhyd5NYkm5JsHPfyJe387GckLTf7GWl1GuuRnyS7AOcAPwZsAa5PcllVfXqcOSTtvOxnpLmNchg2wPlH7znS+a0W9jPS6jXuYW9HAJuq6naAJBcDxwN2FkMa9h/XjoZkDHJ4hnZC9jOSlpv9jLRKjbv4OQC4Y+DxFuDZY86gAaPeCzjTsEXYOIx6D+Vi191c68RCdGTsZyQtN/sZaZVKVY1vYcnLgBdW1c+3xz8LHFFV/21gmtOA09rDpwC3DjHrfYF7Rxx3sfqSxRzb60uWvuQAeEpVPWalQ4zSMP1Ma19oX9OH35sZzLBaMzypqvZb7jDjssh+5geAT4016OL04e9qPmYcndWQc6T9zLiP/GwBDhp4fCBw5+AEVXUucO5CZprkY1W1bunxlq4vWcyxvb5k6UsO6LKsdIZlMG8/Awvva/rwezODGczQGwvuZ1bLuloNOc04Oqsh56gzjvtqb9cDhyQ5OMluwAnAZWPOIGnnZj8jabnZz0ir1FiP/FTVtiRnAB8EdgHOq6pbxplB0s7NfkbScrOfkVavsX/JaVVdAVwx4tkuaJjcMutLFnNsry9Z+pID+pVlZHbifsYMHTN0zLCCFtHPrJZ1tRpymnF0VkPOkWYc6wUPJEmSJGmljPucH0mSJElaEau++ElydJJbk2xKsnFE8zwoydVJPpPkliS/1NrPTPLFJDe227EDr3lNy3BrkhfOl6+dJPmRJLcluaSdMDlbls1Jbm7L+1hr2yfJle21VybZu7UnyZvasm5K8syB+Zzcpr8tyckD7c9q89/UXptZMjxl4D3fmOQrSX55XOsjyXlJ7knyqYG2ZV8Hsyzjz2fJ8QdJPtuW9b4ke7X2tUn+fWDdvG0Ry5vzPc2xTpb995HkUe3xpvb82pl/L6vdbOt2xvNz/o2Nafnrkzww8Ht+7SiX35Yxax84Y5rlXg/DZFjWdZFk9yQfTfLJluG3ZplmWT8TQ2Y4Jcm/DayHnx9lhraMXZJ8Isn7Z3lup+8XFmKufnXg+RVfX0Nk/JUkn26f7auSPGncGYfJOTDdS5NUkrFftWyYjEl+qq3PW5K8q28Zkzyx9befaL/zY2ebzzJnHN//3qpatTe6kww/B3wvsBvwSeCwEcx3f+CZ7f5jgH8GDgPOBH51lukPa8t+FHBwy7TLjvIB7wFOaPffBrxqjiybgX1ntP0vYGO7vxF4fbt/LPABIMCRwEda+z7A7e3n3u3+3u25jwLPaa/5AHDMEOv8X4EnjWt9AD8MPBP41DjXwSzLuGiWHC8Adm33Xz+QY+3gdDPWz7DLm/M9zbFOlv33AfxX4G3t/gnAJSvdD4z6Ntu6nfH8rH9jY1z+euD9y7wOZu0Dx7wehsmwrOuivbdHt/uPbJ+9I2dMs6yfiSEznAK8ZZn/Jn4FeNds63sS+oUFrKd5t0tWen0NmfFHgO9q91+1Er/TYXK26R4DXANcB6zrW0bgEOATPLTN8fgeZjyXh/7PHwZsXoHf99j+9672Iz9HAJuq6vaq+hZwMXD8UmdaVXdV1cfb/a8Cn6H7Nue5HA9cXFXfrKp/ATa1bLPmSxLg+cB72+svAF6ygIjHt9fMfO3xwIXVuQ7YK8n+wAuBK6vqvqq6H7gSOLo9991V9U/V/WVdOESOo4DPVdXn58k3svVRVdcA963AOpi5jHUzc1TV31XVtvbwOrrvepjTApc353sCbptlncxllL+PwYzvBY5q0+805vh7GzTX39i4lr/shuwDl3s9LLQfHrn23ra2h49st5knyi7rZ2LIDMsqyYHAccCfzjHJTt8vLMAw2yUrvb7mzVhVV1fV19vDef+3LZNht/F+h27n4TfGGa4ZJuMvAOe0bQ+q6p4eZizgu9v9xzLLd1Ytt3H+713txc8BwB0Dj7cw4n+O7XD0M+j2tgGc0Q63nZc2LGkHOeZqfxzw5YGN5h3lLuDvktyQ7puiAdZU1V3QbSAAj19kjgPa/ZntO3IC8O6Bx+NeH9PGsQ7mWsZcfo5ur8S0g9sh5P+T5P8ZyLfQ5S3k73y5fx8PvqY9/0CbfpIse78zhOe0YVAfSPLU5VzQLH3gtLGthx1kgGVeF224143APXQ7UOZcD8v1mRgiA8BPts/+e5McNMvzS/FHwK8B35njefuFhwzzuVjp9bXQz+6pPPx/27jMmzPJM4CDqmq74ZhjMsy6PBQ4NMk/JLkuydFjS9cZJuOZwMuTbKG7guF/G0+0BRnZ/5zVXvzMtqdkZHvEkjwa+Evgl6vqK8Bbge8Dng7cBZw9T46Fts/meVX1TOAY4PQkP7yjyMuYg3TnfbwY+IvWtBLrYz4rsuwkvwFsoxsaB936eGJVPYM2XCTJdy9yecO+Zhy/j2X9zK0SK70OPg48qap+EHgz8NfLtaBZ+sCHPT3LS0a+HubJsOzroqq+XVVPp9vzfUSSH5gZcbaXjTnD3wBrq+ppwId46KjCkiX5ceCeqrphR5PN0jZp/cK0YdbFSq+voZef5OV0ox7+YFkTzW6HOZM8AngjsGFsibY3zLrclW7o23rgROBP084PHpNhMp4InF9VB9INL3tnW799MrLPTd/e2EJtAQb3cB3IiA7VJXkk3T/ci6rqrwCq6u72T+g7wNvpDiXuKMdc7ffSHa7bdUb7dqrqzvbzHuB9bZl3Tx/qaz+nD6EuNMcWHn4oe771dwzw8aq6u2Ua+/oYMI51MNcyHibdxRN+HDipDWWjDTH7Urt/A91420MXubyh/s7H9Pt48DXt+ceywkO0VsCy9TvDqKqvTA+Dqu57Rh6ZZN9RL2e2PnCGZV8P82UY17po8/8yMAXM3Gs7ts/EXBmq6ktV9c328O3As0a42OcBL06ymW64zPOT/PmMaewXHjLM52Kl19dQn90kPwr8BvDigb+vcZov52OAHwCm2t/nkcBlGe9FD4b9fV9aVf/Rhp7fSlcMjcswGU+lO9eXqvonYHdgWfrSJRjZ/5zVXvxcDxyS7spUu9ENybpsqTNtY2/fAXymqt4w0D44tvAngOkrUlwGnJDuCi4H0/1Rf3SufG0D+Wrgpe31JwOXzpJjzySPmb5Pd3L9p9ryTp7ltZcBr2hXxDgSeKANn/og8IIke7ehUC8APtie+2qSI9t7fsVsOQacyMCQt3GvjxnGsQ7mWsaD2uHrV9P9c/j6QPt+SXZp97+3rYPbF7m8ud5IeZrcAAAgAElEQVTTzCzj+H0MZnwp8OHpgm+CDPX7WC5Jvqf97ZDkCLp+/EsjXsasfeAMy7oehsmw3OuifY6nr+C4B/CjwGdnTLasn4lhMsz47L+Y7vyokaiq11TVgVW1lq6P+HBVvXzGZPYLDxlmu2Sl19e8GdMNJ/sTuv9t4z5HZdoOc1bVA1W1b1WtbX+f19Hl/VhfMjZ/TXcBCdrOmUPpLrrUp4xfoDunmyTfT1f8/NsYMw5jdP9zasxXcxj1je7w3D/T7Vn/jRHN8z/THUq7Cbix3Y4F3gnc3NovA/YfeM1vtAy3MnDFtLny0V1146N0J5//BfCoWXJ8L91VOT4J3DL9erqxwVfRnfR+FbBPaw9wTlvWzQxc9YTufJRN7fbKgfZ1dBvJnwPeAt0X386S5bvoNioeO9A2lvVBV3DdBfwHXeV/6jjWwSzL+MtZcmyiG4M6/XcyfQWfn2y/s0/SDc150SKWN+d7mmOdLPvvg65D/IvW/lHge1e6D1iGPmW2dfuLwC/O9zc2puWfMfC3dR3w3GVYB3P1geNcD8NkWNZ1ATyN7ipNN7XP7Gtb+2/TbWQt+2diyAz/c2A9XA38p2X6bKynXe1tnOtgtd1m61f7tr6GyPgh4O6Bz95lfVyXM6adGnU/NKJ1GeANwKdbX3lCDzMeBvxD60NuBF6wAhnH9r93eqNLkiRJknZqq33YmyRJkiQNxeJHkiRJ0kSw+JEkSZI0ESx+JEmSJE0Eix9JkiRJE8HiR5IkSdJEsPiRJEmSNBEsfiRJkiRNBIsfLZskpyS5dqVzSNKOJHlVkruTbE3yuJXOI6lf3J7ZuVj8aDtJdkvy3iSbk1SS9SudSdLOI8mRSa5Mcl+Sf0vyF0n2X6EsjwTeALygqh5dVV9aiRySRq9PfY36w+JHc7kWeDnwrysdRNJOZ2/gXGAt8CTgq8CfrVCWNcDuwC0rtHxJy6dPfY16wuJngrUjO69J8ukk9yf5syS7V9W3quqPqupa4NsLmN/jklyW5CtJPgp834znn5vk+iQPtJ/PHXju4CTXJPlqkg8lOSfJn4/u3Uoatx30MR+oqr+oqq9U1deBtwDPG2J+eyQ5O8nnWz9ybZI92nMvTnJLki8nmUry/TNy/GqSm9rrLkmye5JDgVvbZF9O8uHlWA+Sltcy9DVuz+zELH50EvBCug/2ocBvLmFe5wDfAPYHfq7dAEiyD3A58CbgcXTDTC4fGF//LuCj7bkzgZ9dQg5J/TFMH/PDDHfk5Q+BZwHPBfYBfg34Titi3g38MrAfcAXwN0l2G3jtTwFHAwcDTwNOqap/Bp7ant+rqp6/sLcmqUdG2de4PbMTs/jRW6rqjqq6D/g94MTFzCTJLsBPAq+tqq9V1aeACwYmOQ64rareWVXbqurdwGeBFyV5IvBD7bXfakecLlvKm5LUGzvsY5I8DXgt8D92NJMkj6DbAPmlqvpiVX27qv6xqr4J/DRweVVdWVX/QVck7UFXJE17U1Xd2XL8DfD0Ub1BSb0wqr7G7ZmdnMWP7hi4/3ngCYucz37ArrPMb9oTZjyefv6A9tx97ZD0bLkkrV5z9jFJngx8gK6g+ft55rMv3bk5n5vluYf1L1X1nbbcAwamGTx/8evAo4cJL2nVGFVf4/bMTs7iRwcN3H8icOci5/NvwLZZ5jftTrqTDZnx/BeBu4B9knzXHLkkrV6z9jFJngR8CPidqnrnEPO5l24YyvfN8tzD+pckacv94iIzS1p9RtXXuD2zk7P40elJDmxjWH8duAQgyaOS7N6m2a2dHJy5ZlJV3wb+CjgzyXclOQw4eWCSK4BDk/xMkl2T/DRwGPD+qvo88LH22t2SPAd40cjfqaSVsF0fk+QA4MPAOVX1tmFm0o7mnAe8IckTkuyS5DlJHgW8BzguyVHt0tUbgG8C/7gs70hSH42qr3F7Zidn8aN3AX8H3N5uv9vabwX+ne4w7gfb/Zl7OmY6g24oyb8C5zNwOcn23Rk/TrdR8iW6E5V/vKrubZOcBDynPfe7dEXYN5f0ziT1wWx9zM8D3wu8Lt0Xi25NsnWIef0qcDNwPXAf8HrgEVV1K92l+d9Md4ToRcCLqupbo34zknprlH2N2zM7sVTVSmfQCkmyGfj5qvrQSmeZKcklwGer6nUrnUXS4vS5j5G08+hzX+P2TP945Ee9kOSHknxfkkckORo4Hvjrlc4lSZI0LLdn+s/iRwvSvkRw6yy3k5Y46+8BpoCtdNfOf1VVfWKpeSWtLsvYx0jSg9yemVwOe5MkSZI0ETzyI0mSJGkiWPxIkiRJmgi7rnSAHdl3331r7dq18073ta99jT333HP5Ay1Sn/P1ORv0O1+fs8Fw+W644YZ7q2q/MUXqrWH6mr7/vqH/GfueD/qfse/5YPaM9jU7xzaN2Ravz/n6nA2Gzzd0P1NVO7zRfancPcCnBtrOpPsm2xvb7diB514DbKL7npgXDrQf3do2ARvnW25V8axnPauGcfXVVw813Urpc74+Z6vqd74+Z6saLh/wsRris7iz34bpa/r++67qf8a+56vqf8a+56uaPaN9zc6xTWO2xetzvj5nqxo+37D9zDDD3s5vhctMb6yqp7fbFQDtW3BPAJ7aXvO/27dw7wKcAxxD9y24J7ZpJUmSxiLJeUnuSfKpgbYzk3wxyY3tduzAc69JsinJrUleONB+dGvblGTjuN+HpMWbt/ipqmvovkl7GMcDF1fVN6vqX+iO8hzRbpuq6vbqvnH74jatJEnSuJyPO3SlibaUc37OSPIK4GPAhqq6HzgAuG5gmi2tDeCOGe3Pnm2mSU4DTgNYs2YNU1NT8wbZunXrUNOtlD7n63M26He+PmeD/ueTpHGrqmuSrB1y8gd36AL/kmR6hy60HboASaZ36H56xHElLYPFFj9vBX4HqPbzbODngMwybTH7EaZZv2Coqs4FzgVYt25drV+/ft4wU1NTDDPdSulzvj5ng37n63M26H8+SeoRd+gOwWyL1+d8fc4Go8+3qOKnqu6evp/k7cD728MtwEEDkx4I3Nnuz9UuCVi78fKRzu/8o/t75ZbV6OYvPsApI/wdbT7ruJHNS9KSuEN3SGZbvD7nG3W20W/PPHqk+Rb1PT9J9h94+BPA9ImDlwEnJHlUkoOBQ4CPAtcDhyQ5OMludGNoL1t8bEmSpKWrqrur6ttV9R3g7Tw0tG2uHbo72tErqefmPfKT5N3AemDfJFuA1wHrkzydbk/HZuC/AFTVLUneQzfudRtwelV9u83nDOCDwC7AeVV1y8jfjSRJ0gIk2b+q7moPZ+7QfVeSNwBP4KEduqHt0KX72o8TgJ8Zb2pJizVv8VNVJ87S/I4dTP97wO/N0n4FcMWC0kmSJI2IO3QlLeVqb5IkSauGO3QlLeqcH0mSJElabSx+JEmSJE0Eix9JkiRJE8HiR5IkSdJEsPiRJEmSNBEsfiRJkiRNBIsfSZIkSRPB4keSJEnSRLD4kSRJkjQRLH4k9V6SXZJ8Isn72+ODk3wkyW1JLkmyW2t/VHu8qT2/diVzS5KkfrH4kbQa/BLwmYHHrwfeWFWHAPcDp7b2U4H7q+rJwBvbdJIkSYDFj6SeS3IgcBzwp+1xgOcD722TXAC8pN0/vj2mPX9Um16SJIldVzqAJM3jj4BfAx7THj8O+HJVbWuPtwAHtPsHAHcAVNW2JA+06e+dOdMkpwGnAaxZs4apqakdhlizB2w4fNsOp1mI+Za3GFu3bl2W+Y5K3/NB/zP2PR+sjoySJpfFj6TeSvLjwD1VdUOS9dPNs0xaQzz38Maqc4FzAdatW1fr16+fbbIHvfmiSzn75tF1mZtP2vHyFmNqaor53sdK6ns+6H/GvueD1ZFR0uSy+JHUZ88DXpzkWGB34LvpjgTtlWTXdvTnQODONv0W4CBgS5JdgccC940/tiRJ6iPP+ZHUW1X1mqo6sKrWAicAH66qk4CrgZe2yU4GLm33L2uPac9/uKpmPfIjSZImj8WPpNXo1cCvJNlEd07PO1r7O4DHtfZfATauUD5JktRDDnuTtCpU1RQw1e7fDhwxyzTfAF421mCSJGnV8MiPJEmSpIlg8SNJkiRpIjjsTZJWwNqNl490fpvPOm6k85MkaWfkkR9JkiRJE8HiR5IkSdJEcNibJO0E1m68nA2Hb+OUEQ2ncxidJGln5JEfSZIkSRPB4keSJEnSRLD4kSRJkjQRLH4kSZIkTQSLH0mSJEkTweJHkiRJ0kSw+JEkSZI0ESx+JEmSJE0Eix9JkiRJE2He4ifJeUnuSfKpgbZ9klyZ5Lb2c+/WniRvSrIpyU1JnjnwmpPb9LclOXl53o4kSZIkzW6YIz/nA0fPaNsIXFVVhwBXtccAxwCHtNtpwFuhK5aA1wHPBo4AXjddMEmSJI2DO3QlzVv8VNU1wH0zmo8HLmj3LwBeMtB+YXWuA/ZKsj/wQuDKqrqvqu4HrmT7gkqSJGk5nY87dKWJtusiX7emqu4CqKq7kjy+tR8A3DEw3ZbWNlf7dpKcRtfJsGbNGqampuYNs3Xr1qGmWyl9ztfnbNDvfKPOtuHwbSObF/R73S1Ekt2Ba4BH0fVZ762q1yU5GLgY2Af4OPCzVfWtJI8CLgSeBXwJ+Omq2rwi4SX1SlVdk2TtjObjgfXt/gXAFPBqBnboAtclmd6hu562QxcgyfQO3Xcvc3xJI7DY4mcumaWtdtC+fWPVucC5AOvWrav169fPu9CpqSmGmW6l9Dlfn7NBv/ONOtspGy8f2bwAzj96z96uuwX6JvD8qtqa5JHAtUk+APwK8MaqujjJ24BT6fbMngrcX1VPTnIC8Hrgp1cqvKTec4fukMy2eH3ON2k7cxdb/NydZP/WSewP3NPatwAHDUx3IHBna18/o31qkcuWNEHaXtet7eEj262A5wM/09ovAM6kK36Ob/cB3gu8JUnafCRpWO7QncFsi9fnfJO2M3exxc9lwMnAWe3npQPtZyS5mG4s7AOtQPog8PsDY2JfALxm8bElTZIkuwA3AE8GzgE+B3y5qqZ3Lw3ueX1wr2xVbUvyAPA44N4Z81zQHtk1e4x+b9aojTLjmy+6dP6JhnT4AY8F+r3nc1rfM/Y9H6yOjDO4Q1eaIPMWP0neTfch3zfJFrqT/M4C3pPkVOALwMva5FcAxwKbgK8DrwSoqvuS/A5wfZvut6fHykrSfKrq28DTk+wFvA/4/tkmaz+H2iu70D2yb77oUs6+edQjhUdrw+Hbeplx80nrgX7v+ZzW94x9zwerI+MM7tCVJsi8/yWr6sQ5njpqlmkLOH2O+ZwHnLegdJI0oKq+nGQKOJLuapK7tqM/03tk4aG9tVuS7Ao8lu2vWClpArlDV1L/dhFK0oAk+wH/0QqfPYAfpbuIwdXAS+mu+DZzb+3JwD+15z/s+T6SwB26kix+JPXf/sAF7byfRwDvqar3J/k0cHGS3wU+AbyjTf8O4J1JNtEd8TlhJUJLkqT+sfiR1GtVdRPwjFnab6f7gsGZ7d/goWErkiRJD3rESgeQJEmSpHGw+JEkSZI0ESx+JEmSJE0Eix9JkiRJE8HiR5IkSdJEsPiRJEmSNBEsfiRJkiRNBIsfSZIkSRPB4keSJEnSRLD4kSRJkjQRLH4kSZIkTQSLH0mSJEkTweJHkiRJ0kSw+JEkSZI0ESx+JEmSJE0Eix9JkiRJE8HiR5IkSdJEsPiRJEmSNBEsfiRJkiRNBIsfSb2V5KAkVyf5TJJbkvxSa98nyZVJbms/927tSfKmJJuS3JTkmSv7DiRJUp9Y/Ejqs23Ahqr6fuBI4PQkhwEbgauq6hDgqvYY4BjgkHY7DXjr+CNLkqS+sviR1FtVdVdVfbzd/yrwGeAA4HjggjbZBcBL2v3jgQurcx2wV5L9xxxbkiT11K4rHUCShpFkLfAM4CPAmqq6C7oCKcnj22QHAHcMvGxLa7trlvmdRnd0iDVr1jA1NbXD5a/ZAzYcvm1J72G59TXj9LrdunXrvOt5pfU9Y9/zwerIKGlyWfxI6r0kjwb+EvjlqvpKkjknnaWtZpuwqs4FzgVYt25drV+/focZ3nzRpZx9c7+7zA2Hb+tlxs0nrQe6Imi+9bzS+p6x7/lgdWSUNLn6919SkgYkeSRd4XNRVf1Va747yf7tqM/+wD2tfQtw0MDLDwTuHF9azWbtxsuBrjg7pd1fis1nHbfkeUiSJpPn/EjqrXSHeN4BfKaq3jDw1GXAye3+ycClA+2vaFd9OxJ4YHp4nCRJkkd+JPXZ84CfBW5OcmNr+3XgLOA9SU4FvgC8rD13BXAssAn4OvDK8caVJEl9ZvEjqbeq6lpmP48H4KhZpi/g9GUNJUmSVi2HvUmSJEmaCBY/kiRJkibCkoqfJJuT3JzkxiQfa237JLkyyW3t596tPUnelGRTkpuSPHMUb0CSJEmShjGKIz8/UlVPr6p17fFG4KqqOgS4qj0GOAY4pN1OA946gmVLkiQtmTt0pcmwHBc8OB5Y3+5fAEwBr27tF7YTkq9Lstf093QsdYE3f/GBkXx3xDS/Q0KSpIn0I1V178Dj6R26ZyXZ2B6/mofv0H023Q7dZ487rKSFW+qRnwL+LskNSU5rbWumC5r28/Gt/QDgjoHXbmltkiRJfXQ83Y5c2s+XDLRfWJ3rgL3aFy5L6rmlHvl5XlXdmeTxwJVJPruDaWe7XG1tN1FXRJ0GsGbNGqampuYNsWaP7pvDR2WYZS7E1q1bRz7PUelzNuh3vlFnG+XfMPR73UlSD03v0C3gT6rqXGbs0G3bOzD3Dt2HjWZZzDbNPfc9wJsvunTe6YZx+AGPHcl8pvX5/0qfs0G/803a9sySip+qurP9vCfJ+4AjgLunh7O1vSD3tMm3AAcNvPxA4M5Z5nkucC7AunXrav369fPmePNFl3L2zaMbwbf5pPmXuRBTU1MM8z5WQp+zQb/zjTrbKIduApx/9J69XXeS1EMj36G70ts0bs/0R5/zTdr2zKI/XUn2BB5RVV9t918A/DZwGXAy3TewnwxM7764DDgjycV042IfGMX5PpKkybJ2xP9YPc9TsDw7dCX1z1LO+VkDXJvkk8BHgcur6m/pip4fS3Ib8GPtMcAVwO3AJuDtwH9dwrIlSZJGIsmeSR4zfZ9uh+6neGiHLmy/Q/cV7apvR+IOXWnVWPSRn6q6HfjBWdq/BBw1S3sBpy92eZIkSctkDfC+JNBtG72rqv42yfXAe5KcCnwBeFmb/grgWLodul8HXjn+yJIWYzkudS1JkrRquENXmhyj+JJTSZIkSeo9ix9JkiRJE8HiR5IkSdJEsPiRJEmSNBEsfiRJkiRNBIsfSZIkSRPB4kdSryU5L8k9ST410LZPkiuT3NZ+7t3ak+RNSTYluSnJM1cuuSRJ6huLH0l9dz5w9Iy2jcBVVXUIcFV7DHAMcEi7nQa8dUwZJUnSKmDxI6nXquoa4L4ZzccDF7T7FwAvGWi/sDrXAXsl2X88SSVJUt/tutIBJGkR1lTVXQBVdVeSx7f2A4A7Bqbb0trumjmDJKfRHR1izZo1TE1N7XiBe8CGw7ctPfky6nvGvuYb/N1v3bp13r+FldT3fLA6MkqaXBY/knYmmaWtZpuwqs4FzgVYt25drV+/foczfvNFl3L2zf3uMjccvq3XGfuab/NJ6x+8PzU1xXx/Cyup7/lgdWSUNLkc9iZpNbp7ejhb+3lPa98CHDQw3YHAnWPOJkmSesriR9JqdBlwcrt/MnDpQPsr2lXfjgQemB4eJ0mS1L/xB5I0IMm7gfXAvkm2AK8DzgLek+RU4AvAy9rkVwDHApuArwOvHHtgSZLUWxY/knqtqk6c46mjZpm2gNOXN5F2Nms3Xv7g/Q2Hb+OUgceLsfms45YaSZK0TBz2JkmSJGkiWPxIkiRJmggWP5IkSZImgsWPJEmSpIlg8SNJkiRpIlj8SJIkSZoIFj+SJEmSJoLFjyRJkqSJYPEjSZIkaSJY/EiSJEmaCLuudABJknYmazdePrJ5bT7ruJHNS5LkkR9JkiRJE8LiR5IkSdJEsPiRJEmSNBEsfiRJkiRNBC94IElST828eMKGw7dxyhIuqOAFFCRNOo/8SJIkSZoIFj+SJEmSJsLYh70lORr4Y2AX4E+r6qxxZ5C0c7OfkWY3yu8ggskeRmc/I61OYz3yk2QX4BzgGOAw4MQkh40zg6Sdm/2MpOVmPyOtXuMe9nYEsKmqbq+qbwEXA8ePOYOknZv9jKTlZj8jrVLjLn4OAO4YeLyltUnSqNjPSFpu9jPSKjXuc34yS1s9bILkNOC09nBrkluHmO++wL1LzPZQhtePak4PGmm+EetzNuh3vj5n40deP1S+J40jy5jN28/AovqaXv++Af57zzP2PR/0P2Pf8s3x/3K2jDtbX7Nc/QyM8Hfs9kyv9Dlfn7MNuz0DQ/Yz4y5+tgAHDTw+ELhzcIKqOhc4dyEzTfKxqlq39HjLo8/5+pwN+p2vz9mg//mW0bz9DCy8r1kN67PvGfueD/qfse/5YHVkHIFl6Weg3+vPbIvX53x9zgajzzfuYW/XA4ckOTjJbsAJwGVjziBp52Y/I2m52c9Iq9RYj/xU1bYkZwAfpLs05HlVdcs4M0jaudnPSFpu9jPS6jX27/mpqiuAK0Y82wUdUl4Bfc7X52zQ73x9zgb9z7dsJrSfgf5n7Hs+6H/GvueD1ZFxyZapn4F+rz+zLV6f8/U5G4w4X6q2Oz9PkiRJknY64z7nR5IkSZJWxKoqfpIcneTWJJuSbJzl+UcluaQ9/5Eka3uU7VeSfDrJTUmuSjLWy37Ol29gupcmqSRju+rHMNmS/FRbf7ckede4sg2TL8kTk1yd5BPt93vsGLOdl+SeJJ+a4/kkeVPLflOSZ44r22rV535mARl/OMnHk2xL8tIe5ut1f5jkF5PcnOTGJNcmOWyc+YbJODDd2PvsYfIlOSXJv7V1eGOSnx9nvr7rez/T58/wav9stGlWZJvG7ZmmqlbFje6Ews8B3wvsBnwSOGzGNP8VeFu7fwJwSY+y/QjwXe3+q8aVbdh8bbrHANcA1wHr+pINOAT4BLB3e/z4Pq07urGor2r3DwM2jzHfDwPPBD41x/PHAh+g+06KI4GPjCvbarz1uZ9ZYMa1wNOAC4GX9jBfr/tD4LsH7r8Y+Nu+rcM23dj77AWsw1OAt4xzva2WW9/7mT5/hneSz8aKbNMMmW0itmdW05GfI4BNVXV7VX0LuBg4fsY0xwMXtPvvBY5KMtsXkY09W1VdXVVfbw+vo/tOgHEZZt0B/A7wv4Bv9CzbLwDnVNX9AFV1T8/yFfDd7f5jmeW7HpZLVV0D3LeDSY4HLqzOdcBeSfYfT7pVqc/9zNAZq2pzVd0EfGeMuRaSr9f9YVV9ZeDhnszy5ZXLrM99NgyfT7Prez/T58/wzvDZWKltGrdnmtVU/BwA3DHweEtrm3WaqtoGPAA8rifZBp1KV72Oy7z5kjwDOKiq3j/GXDDcujsUODTJPyS5LsnRY0s3XL4zgZcn2UJ35Z//Np5oQ1no3+ak63M/s93ym779Tld9fwiQ5PQkn6PbgPrvY8o2rc99Ngz/O/7JNjzlvUkOmuX5SdX3fqbPn+Gd4bOxUts0bs80q6n4mW2Px8y9ccNMsxyGXm6SlwPrgD9Y1kQzFjtL24P5kjwCeCOwYWyJHjLMutuV7jDxeuBE4E+T7LXMuaYNk+9E4PyqOpDusOw72zrtg5X6TKxWfe5n+rL8+azq/vDBhqpzqur7gFcDv7nsqR6uz302DLcO/wZYW1VPAz7EQ0cx1P9+ps+f4Z3hs7FS2zRuzzR9eUPD2AIM7jk6kO0Pxz04TZJd6Q7Z7egQ2jizkeRHgd8AXlxV3xxDrmnz5XsM8APAVJLNdGMpLxvTSYLD/l4vrar/qKp/AW6l6zjGYZh8pwLvAaiqfwJ2B/YdS7r5DfW3qQf1uZ/ZbvlN336nq70/nOli4CXLmmh7fe6zh8lHVX1p4Pf6duBZY8q2GvS9n+nzZ3jVfzZYuW0at2emjetEpqXe6Crl24GDeehErafOmOZ0Hn6C4P9l7+7DbSvrev+/P4KoIQmI7hDQjbUtUVJph5q/bHMoQMjQc7SDhxSMDlnSI/1qW/6OHB9O2BWWGGl0JKCQhx4MUkq36MosUbCIB5HY4lY2IChPuiHNrd/fH+NeOVmsh7n3mmvOueZ8v65rXnPOe9xjjO895ppjze8Y97jHJWMU23PoLjRbN47bbk79GYY34EE/2+4o4Lz2eh+6056PH6P4/hY4sb1+evsyZoif71oWvkDwGB56geAnh/33t5oe47yf2ZEYe+qey/AHPFj1+8PeuIAXA1ePW4xz6g9tn70D23DfntcvBa4c9mc9ro9x38+M83d4Qr4bI/lN02dsU/F7Zmh/sAPaKEcD/9a+cL/Vyt5Id9QBugz1z4HNwCeBp45RbB8C7gSuaY/Lxmnbzak77J3FUtsuwNuATwPXAceN07ajGxHlH9uO5BrgiCHGdiFwB/ANuqMiJwGvAV7Ts+3OarFfN8zPdbU+xnk/swMx/mD7e3gAuBu4YcziG+v9IfB24IYW20dY5MfVqGKcU3eo++w+t+Fvt234r20bft+wt+E4P8Z9PzPO3+EJ+G6M7DdNH7FNxe+ZtAVKkiRJ0kRbTdf8SJIkSdJOM/mRJEmSNBVMfiRJkiRNBZMfSZIkSVPB5EeSJEnSVDD5kSRJkjQVTH4kSZIkTQWTH0mSJElTweRHYy3JzyW5M8m2JI8fdTySxkuSE5N8bNRxSOpPkncl+f+GvM6ZJD8zzHUuJcnxST446jimkcmPHiLJQUmuTnJve3woyUEjiuWRwNuAI6rqsVV19yjikDR447SvkbQy5js4UVWvqao3jSqmcVFVF1TVEYNYVpItSX50EMuaBiY/mut24GXA3sA+wFl1wPQAACAASURBVGXARSOKZQ3waOCGEa1f0soZp32NpJ2QZNdRx6DlmcbP0ORnSrWjBK9L8ul21PVPkjy6qu6rqi1VVUCAbwLf08fyHpPkjCSfT3J/ko8leUyb9hNJbkhyXzv1/PQ5cfxakmvbfBcneXSSpwE3tWr3JfnwCmwGSStsBfY1j09yWZKvJPkk8N1zpv9Qkqva/uSqJD/UM+3AJB9N8tV2pumsJH826DZLk6x9p38jybXAA0len+Sz7Xv16SQvbfWeDrwLeH7run5fKz83yZvb6w1JtiY5NcldSe5I8uqedT0+yd+07/tVSd7cTzfXJD+W5DNtP/AHdPuY3uk/neTGtk/6QJKn9EyrJK9JcnObflaStGmPaO39fIv3/CSPa9PWtnlfneTWNu9rkvxg+41zX4tldj0POSu2xHq/O8mHk9yd5MtJLkiyZ5v2p8CTgb9p2/nXW/lSv716P8OpSoBMfqbb8cCRdD8enga8fnZC20l9DXgH8H/6WNbvAj8A/BDdkdxfB77VkpgLgV8GngBcTvcF3a1n3p8EjgIOBL4fOLGq/g14Rpu+Z1X9l51so6TRG+S+5qxWf1/gp9tjdll7A+8HzgQeT9dt9v359vWC7wE+2aadBrxyGW2SptkrgGOAPekOVP4w8DjgfwN/lmTfqroReA3w8dZ1fc8FlvVdbd79gJOAs5Ls1aadBTzQ6pzQHotKsg/wl3T7mX2AzwIv6Jn+EuA3gf9K97vkH+h+p/T6ceAHgWfR/UY5spWf2B6HAU8FHgv8wZx5nwusA/478PvAbwE/Sveb5ieT/Mgi4S+03gC/DTwJeDpwAN0+jKp6JfAF4MVtO/9On7+9/vMzrKrti8Q0cUx+ptsfVNWtVXUP8Ba6LwIAbSf1OOAU4F8WW0iSR9D9APmlqrqtqr5ZVf9UVV+n+/K/v6o2VdU36JKkx9AlSbPOrKrbWxx/Azx7gG2UNHqD2tfsAvw34H9V1QNVdT1wXk+VY4Cbq+pPq2p7VV0IfAZ4cZIn0/2o+F9V9R9V9TG6rnaSdtyZ7Tv971X15+1/+Leq6mLgZuDQHVjWN4A3VtU3qupyYBvwvT3f9zdU1YNV9Wke+n1fyNHAp6vqL9rvjt8Hvtgz/WeB366qG9uP/v8DPLv37A9wejs7/QXgI3z7d8nxwNuq6paq2ga8DjhuzpmTN1XV16rqg3SJ24VVdVdV3UaXaD1nkdjnXW9VbW6/o75eVV+iO7CzWBLV72+vW6vq3xdZzkQy+Zlut/a8/jzdEYX/VFUP0J2yPj/JExdZzj501+Z8dp5pT2rLnl3mt9p69+up07tTepDuSIqkyTGofc0TgF3nWd6sJ815Pzt9vzbtnqp6cIG4JPXvP787SV6V5JrWveo+4Jl0vwv6dfecMw+zvwPm+7738519Um+91rW2d76nAG/vifceujMr/fwumbuP+XyLcU1P2Z09r/99nveL/caZd71JnpjkoiS3JfkK8Gcsvo37+e01tfs/k5/pdkDP6yfTXYA81yOA7+ChX5i5vkzXDeW755l2O92OBoDWf/UA4LYdDVbSqjWofc2XgO3zLG/WQ/Y3PdNvA+4A9k7yHQvEJal/BdDOlvwx3Znbx7czudfz7WtsahnrmP2+799T1s939o7eej2/O2bdCvxsVe3Z83hMVf1TH8ueu495covxzvmrD8xv023L76+q7wR+iodexzR3O/fz22s5n82qZvIz3V6bZP/WT/43gYvbRYLPSbJLku+kO7V6L3DjQgtpRxTOAd6W5Elt3ucneRRwCXBMksPTDV19KvB1oJ+djKTJMKh9zTeBvwJOS/Id6YbG7r0G4HLgaUn+R5Jdk/x34CDgfVX1eeDqNu9uSZ4PvHhFWitNj93pfkR/CSDdYAXP7Jl+J7D/nGtN+jLP9/37gFf1Mev7gWck+a+tO9ov0l0zNOtdwOuSPKPF/LgkL+8zrAuBX0k3eMpj6brMXTyEa2b2oOsOeF+S/YD/d870O+muQZrlb69FmPxMt/cAHwRuaY830128eCFwP103tu8Bjqqqry2xrF8DrgOuojuF/FbgEVV1E90RinfQnSF6Md1Fef8x8NZIGleD3NecQtcV5IvAucCfzE5o9wL7cbp/9HfTDbzy41X15VbleOD5bdqbgYvpfhBI2gntOpwzgI/T/QA/GPjHniofprtdxReTfPnhS1jSKXTXBH4R+FO6fcai39n2fX85cDrdd31db0xV9V663ygXtS5k1wMv6jOec1ocHwU+R9fr5Rf6b85O+9/AIXT7y/fTJYW9fht4fevK92v+9lpcuq6QmjZJtgA/U1UfGnUskibXOO9rklwMfKaq3jDqWCQtLclbge+qqiVHfZMW4pkfSdJUaPfb+O509+o4CjgW+OtRxyVpfkm+L8n3p3Mo3VDY7x11XFrdTH7Ut3azrG3zPI4fdWySJscK7mu+C5ih6zt/JvBzVbXo8NqSRmoPui5eD9Bdx3IGcGmSH15gH7FtpNFqVbDbmyRJkqSp4JkfSSOX5JwkdyW5vqfstHZPg2va4+ieaa9LsjnJTUmO7Ck/qpVtTrJx2O2QJEnjzTM/kkYuyQvpuiKdX1XPbGWnAduq6nfn1D2IbsSfQ+lu5PYh4Glt8r8BPwZspRt58BVtNCJJkiR2HXUAi9lnn31q7dq1S9Z74IEH2H333Vc+oD4Zz9LGLaZpjedTn/rUl6vqCSu+oiVU1UeTrO2z+rHARVX1deBzSTbTJUIAm6vqFoAkF7W6SyY//exrxu1vZK5xjm+cY4Pxjm+cY4P+4xuXfc0oLbafGffPeZBs6+QZl3b2u58Z6+Rn7dq1XH311UvWm5mZYcOGDSsfUJ+MZ2njFtO0xpPk8yu+kuU5Jcmr6G5OeWpV3QvsB1zZU2drK4Puzt295c9daMFJTgZOBlizZg2/+7u/u1BVALZt28ZjH/vYHW7AsIxzfOMcG4x3fOMcG/Qf32GHHTbyfU2SA4Dz6Qa++BZwdlW9vd1892JgLbAF+MmqujdJgLcDRwMPAidW1T+3ZZ0AvL4t+s1Vdd5S61/sN824/Q9aSbZ18oxLO/v9TTPWyY+kqfZO4E10dw9/E90oPz8NZJ66xfzXMC7Yr7eqzgbOBli/fn0tteMel537QsY5vnGODcY7vnGODcY/vjm20x1E+eckewCfSrIJOBG4oqpOb9cKbgR+g+7Gl+va47l0+6TntmTpDcB6un3Mp5Jc1g7OSBpzDnggaSxV1Z1V9c2q+hbwx3y7a9tW4ICeqvsDty9SLklU1R2zZ26q6qvAjXRnjY8FZs/cnAe8pL0+lu46xKqqK4E9k+wLHAlsqqp7WsKzCThqiE2RtAwmP5LGUvuRMeulwOxIcJcBxyV5VJID6Y7KfpJugIN1SQ5MshtwXKsrSQ/RrjF8DvAJYE1V3QFdggQ8sVXbj4d3pd1vkXJJq4Dd3iSNXJILgQ3APkm20nUp2ZDk2XTdSrYAPwtQVTckuYRuIIPtwGur6pttOacAHwB2Ac6pqhuG3BRJYy7JY4G/BH65qr7SXdozf9V5ymqR8vnW9ZBrC2dmZuZd0bZt2xacNmls6+RZbe2ciOTnutvu58SN7x/Y8racfszAliVpaVX1inmK371I/bcAb5mn/HLg8gGGJk2UtQP8Xwlw7lGjH+FpRyR5JF3ic0FV/VUrvjPJvlV1RzvjfFcrX6yL7YY55TPzra/fawt7r50a5Gc0jr9nVtl1YssyLW1dbe2025skSZp4bfS2dwM3VtXbeiZdBpzQXp8AXNpT/qp0ngfc37rFfQA4IsleSfYCjmhlklaBiTjzI0mStIQXAK8ErktyTSv7TeB04JIkJwFfAF7epl1ON8z1Zrqhrl8NUFX3JHkT3XWGAG+sqnuG0wRJy2XyI0mSJl5VfYz5r9cBOHye+gW8doFlnQOcM7joJA2L3d4kSZIkTQWTH0mSJElTweRHkiRJ0lQw+ZEkSZI0FUx+JEmSJE0Fkx9JkiRJU2FZyU+SPZP8RZLPJLkxyfOT7J1kU5Kb2/NerW6SnJlkc5JrkxwymCZIkiRJ0tKWe+bn7cDfVdX3Ac8CbgQ2AldU1TrgivYe4EXAuvY4GXjnMtctSZIkSX3b6eQnyXcCLwTeDVBV/1FV9wHHAue1aucBL2mvjwXOr86VwJ5J9t3pyCVJkiRpB+y6jHmfCnwJ+JMkzwI+BfwSsKaq7gCoqjuSPLHV3w+4tWf+ra3sjt6FJjmZ7swQa9asYWZmZslA1jwGTj14+zKa8lD9rHMx27ZtW/YyBmnc4oHxi8l4JEmSJt9ykp9dgUOAX6iqTyR5O9/u4jafzFNWDyuoOhs4G2D9+vW1YcOGJQN5xwWXcsZ1y2nKQ205ful1LmZmZoZ+4h6WcYsHxi8m45EkSZp8y7nmZyuwtao+0d7/BV0ydOdsd7b2fFdP/QN65t8fuH0Z65ckSZKkvu108lNVXwRuTfK9rehw4NPAZcAJrewE4NL2+jLgVW3Ut+cB9892j5MkSZKklbbcvmK/AFyQZDfgFuDVdAnVJUlOAr4AvLzVvRw4GtgMPNjqSpIkSdJQLCv5qaprgPXzTDp8nroFvHY565MkSZKknbXc+/xIkiRJ0qpg8iNJkiRpKpj8SJIkSZoKJj+SJEmSpoLJjyRJkqSpYPIjSZIkaSqY/EiSJEmaCiY/kiRJkqaCyY8kSZoKSc5JcleS63vKTktyW5Jr2uPonmmvS7I5yU1JjuwpP6qVbU6ycdjtkLTzTH4kSdK0OBc4ap7y36uqZ7fH5QBJDgKOA57R5vnDJLsk2QU4C3gRcBDwilZX0iqw66gDkCRJGoaq+miStX1WPxa4qKq+DnwuyWbg0DZtc1XdApDkolb30wMOV9IK8MyPJEmadqckubZ1i9urle0H3NpTZ2srW6hc0irgmR9JkjTN3gm8Caj2fAbw00DmqVvMf+C45ltwkpOBkwHWrFnDzMzMvAFs27btP6edevD2HYl9UQutb5R62zrppqWtq62dJj+SJGlqVdWds6+T/DHwvvZ2K3BAT9X9gdvb64XK5y77bOBsgPXr19eGDRvmjWFmZobZaSdufP8OtmBhW46ff32j1NvWSTctbV1t7bTbmyRJmlpJ9u15+1JgdiS4y4DjkjwqyYHAOuCTwFXAuiQHJtmNblCEy4YZs6Sd55kfSZI0FZJcCGwA9kmyFXgDsCHJs+m6rm0Bfhagqm5IcgndQAbbgddW1Tfbck4BPgDsApxTVTcMuSmSdpLJjyRJmgpV9Yp5it+9SP23AG+Zp/xy4PIBhiZpSOz2JmksLHDzwb2TbEpyc3veq5UnyZntBoPXJjmkZ54TWv2bk5wwirZIkqTxZPIjaVycy8NvPrgRuKKq1gFXtPfQ3VxwXXucTDdaE0n2puvG8ly6+3G8oWfYWkmSNOVMfiSNhar6KHDPnOJjgfPa6/OAl/SUn1+dK4E920XLRwKbquqeqroX2MT8d3OXJElTyGt+JI2zNVV1B0BV3ZHkia182Tcf7Pf+G7PG/T4G4xzfOMcG4x3foGMb5D1kYLy3nSTNx+RH0mq00M0HFyp/eGGf99+YNe73MRjn+MY5Nhjv+AYd2yDvIQNw7lG7j+22k6T52O1N0ji7c/YeHO35rla+0M0HF7spoSRJmnImP5LG2WXA7IhtJwCX9pS/qo369jzg/tY97gPAEUn2agMdHNHKJEmS7PYmaTwscPPB04FLkpwEfAF4eat+OXA0sBl4EHg1QFXdk+RNdHdgB3hjVc0dREGSJE0pkx9JY2GBmw8CHD5P3QJeu8ByzgHOGWBokiRpQtjtTZIkSdJUMPmRJEmSNBVMfiRJkiRNhWUnP0l2SfIvSd7X3h+Y5BNJbk5ycZLdWvmj2vvNbfra5a5bkiRJkvo1iDM/vwTc2PP+rcDvVdU64F7gpFZ+EnBvVX0P8HutniRJkiQNxbKSnyT7A8cA/7e9D/BfgL9oVc4DXtJeH9ve06Yf3upLkiRJ0opb7lDXvw/8OrBHe/944L6q2t7ebwX2a6/3A24FqKrtSe5v9b/cu8AkJwMnA6xZs4aZmZklg1jzGDj14O1L1utXP+tczLZt25a9jEEat3hg/GIyHkmSpMm308lPkh8H7qqqTyXZMFs8T9XqY9q3C6rOBs4GWL9+fW3YsGFulYd5xwWXcsZ1g7tl0Zbjl17nYmZmZugn7mEZt3hg/GIyHkmSpMm3nIzhBcBPJDkaeDTwnXRngvZMsms7+7M/cHurvxU4ANiaZFfgcYB3XpckSZI0FDud/FTV64DXAbQzP79WVccn+XPgZcBFwAnApW2Wy9r7j7fpH253aR87aze+f1nzn3rwdk7sWcaW049ZbkiSJEmSlmkl7vPzG8CvJtlMd03Pu1v5u4HHt/JfBTauwLolSZIkaV4DuVCmqmaAmfb6FuDQeep8DXj5INYnSZIkSTtqJc78SJIkSdLYMfmRJEmSNBVMfiRJ0lRIck6Su5Jc31O2d5JNSW5uz3u18iQ5M8nmJNcmOaRnnhNa/ZuTnDCKtkjaOSY/kiRpWpwLHDWnbCNwRVWtA67g2wMyvQhY1x4nA++ELlkC3gA8l+4a5zfMJkySxp/JjyRJmgpV9VEefo/BY4Hz2uvzgJf0lJ9fnSvp7mO4L3AksKmq7qmqe4FNPDyhkjSmTH4kSdI0W1NVdwC05ye28v2AW3vqbW1lC5VLWgUGMtS1Frfcm6b28oapkiQNReYpq0XKH76A5GS6LnOsWbOGmZmZeVe0bdu2/5x26sHbdzzSBSy0vlHqbeukm5a2rrZ2mvxIkqRpdmeSfavqjtat7a5WvhU4oKfe/sDtrXzDnPKZ+RZcVWcDZwOsX7++NmzYMF81ZmZmmJ124iAPmB4///pGqbetk25a2rra2mm3N0mSNM0uA2ZHbDsBuLSn/FVt1LfnAfe3bnEfAI5Islcb6OCIViZpFfDMjyRJmgpJLqQ7a7NPkq10o7adDlyS5CTgC8DLW/XLgaOBzcCDwKsBquqeJG8Crmr13lhVcwdRkDSmTH4kSdJUqKpXLDDp8HnqFvDaBZZzDnDOAEOTNCR2e5MkSZI0FUx+JEmSJE0Fkx9JkiRJU8HkR5IkSdJUMPmRJEmSNBVMfiRJkiRNBZMfSZIkSVPB5EeSJEnSVDD5kSRJkjQVdh11ANoxaze+f4fnOfXg7Zy4wHxbTj9muSFJkiRJq4JnfiRJkiRNBc/8SFIfrrvt/gXPoO4Mz7pKkjR8nvmRNNaSbElyXZJrklzdyvZOsinJze15r1aeJGcm2Zzk2iSHjDZ6SZI0Tkx+JK0Gh1XVs6tqfXu/EbiiqtYBV7T3AC8C1rXHycA7hx6pJEkaWyY/klajY4Hz2uvzgJf0lJ9fnSuBPZPsO4oAJUnS+PGaH0njroAPJingj6rqbGBNVd0BUFV3JHliq7sfcGvPvFtb2R1zF5rkZLqzQ6xZs4aZmZlFg1jzmG7kxEFZan07atu2bQNf5qCMc2ww3vENOrZB/g3DeG87SZqPyY+kcfeCqrq9JTibknxmkbqZp6zmq9iSqLMB1q9fXxs2bFg0iHdccClnXDe4XeaW4xdf346amZlhqTaMyjjHBuMd36BjG+SgHQDnHrX72G47SZqP3d4kjbWqur093wW8FzgUuHO2O1t7vqtV3woc0DP7/sDtw4tWkiSNs51OfpIckOQjSW5MckOSX2rljsIkaSCS7J5kj9nXwBHA9cBlwAmt2gnApe31ZcCr2v7mecD9s93jJEmSlnPmZztwalU9HXge8NokB+EoTJIGZw3wsST/CnwSeH9V/R1wOvBjSW4Gfqy9B7gcuAXYDPwx8PPDD1mSJI2rne7A3o6mzl5w/NUkN9JdWHwssKFVOw+YAX6DnlGYgCuT7JlkX4/KSlpIVd0CPGue8ruBw+cpL+C1QwhNkiStQgO5ejfJWuA5wCdY5ihMOzoCEwx+FKblWk3xjGqUnnEbIch4JEmSJt+yk58kjwX+EvjlqvpKMt9gS13VecoeNgrTjo7ABIMfhWm5Tj14+6qJZ9AjTvVr3EZXMh5JkqTJt6zR3pI8ki7xuaCq/qoVOwqTJElaVZJsSXJdkmuSXN3KHMRJmjDLGe0twLuBG6vqbT2THIVJkiStRodV1bOran177yBO0oRZzpmfFwCvBP5LO0pyTZKjcRQmSZI0GY6lG7yJ9vySnvLzq3MlsOdsrxdJ4205o719jPmv4wFHYZIkSatLAR9MUsAftWuQlzWIk6TxMz5X5UuSJI3OC6rq9pbgbErymUXq9jWIU78j2PaO8DnI0WLHcdTQaRrNdFrautraafIz5dZufP9Al7fl9GMGujxJkoahqm5vz3cleS9wKG0Qp3bWZ4cHcep3BNveET5PHOD/5VGN6LqYaRrNdFrautrauazR3iRJkla7JLsn2WP2NXAEcD0O4iRNHM/8SJKkabcGeG+7V+GuwHuq6u+SXAVckuQk4AvAy1v9y4Gj6QZxehB49fBDlrQzTH4kSdJUq6pbgGfNU343DuIkTRS7vUmSJEmaCiY/kiRJkqaCyY8kSZKkqWDyI0mSJGkqmPxIkiRJmgomP5IkSZKmgsmPJEmSpKlg8iNJkiRpKniTUw3U2o3v76veqQdv58Q+6m45/ZjlhiRJkiQBnvmRJEmSNCVMfiRJkiRNBZMfSZIkSVPB5EeSJEnSVDD5kSRJkjQVTH4kSZIkTQWTH0mSJElTweRHkiRJ0lTwJqcaa/3eNLUf3jBVkiRpunnmR5IkSdJUMPmRJEmSNBVMfiRJkiRNBZMfSZIkSVPB5EeSJEnSVBj6aG9JjgLeDuwC/N+qOn3YMWg6LTZy3KkHb+fEHRxZztHjxpf7GUkrzf2MtDoN9cxPkl2As4AXAQcBr0hy0DBjkDTZ3M9IWmnuZ6TVa9jd3g4FNlfVLVX1H8BFwLFDjkHSZHM/I2mluZ+RVqlhd3vbD7i15/1W4LlDjkEaiEHegHWunemGN9cUd8tzPyNppU3lfmYQ//d6/79N8f8pjdCwk5/MU1YPqZCcDJzc3m5LclMfy90H+PIyYxuYXzSeJY1bTJMYT97aV7WnLGcdY2rJ/Qzs1L5moH8jfX4+O2Ks/obnGOfYYLzjG+fYOOytfcc3afuaQe9nVuRzXoH9zLL1/n8bx/gGbKy/vwM0Lu3saz8z7ORnK3BAz/v9gdt7K1TV2cDZO7LQJFdX1frlhzcYxrO0cYvJeCbKkvsZ2PF9zbh/JuMc3zjHBuMd3zjHBuMf3woa6H5mmrajbZ08q62dw77m5ypgXZIDk+wGHAdcNuQYJE029zOSVpr7GWmVGuqZn6ranuQU4AN0Q0OeU1U3DDMGSZPN/YykleZ+Rlq9hn6fn6q6HLh8wIvdoW5yQ2A8Sxu3mIxngkzJfmaucY5vnGOD8Y5vnGOD8Y9vxQx4PzNN29G2Tp5V1c5UPez6PEmSJEmaOMO+5keSJEmSRmLsk58kRyW5KcnmJBvnmf6oJBe36Z9IsrZn2uta+U1JjhxlPEnWJvn3JNe0x7uGFM8Lk/xzku1JXjZn2glJbm6PE8Ygnm/2bJ+BXDjaRzy/muTTSa5NckWSp/RMG8X2WSyegW8fPdxy9jljEt+Cf0Ojjq2n3suSVJKhjQ7UT2xJfrJtuxuSvGdYsfUTX5InJ/lIkn9pn+3RQ4ztnCR3Jbl+gelJcmaL/dokhwwrtknQ7/dmNUqyJcl17f/W1a1s7ySb2v/WTUn2GnWcO2O+78VCbVvt35EF2npaktt6fpcc3TNt4L+/B6qqxvZBdxHhZ4GnArsB/wocNKfOzwPvaq+PAy5urw9q9R8FHNiWs8sI41kLXD+C7bMW+H7gfOBlPeV7A7e0573a671GFU+btm0E2+cw4Dva65/r+bxGtX3mjWclto+Pnf6M5v2Oj1F8C/4NjTq2Vm8P4KPAlcD6cYkNWAf8y+z3HHjimH2uZwM/114fBGwZYnwvBA5hgf9hwNHA39Ld++Z5wCeGFdtqf/T7vVmtD2ALsM+cst8BNrbXG4G3jjrOnWzbw74XC7VttX9HFmjracCvzVN34L+/B/0Y9zM/hwKbq+qWqvoP4CLg2Dl1jgXOa6//Ajg8SVr5RVX19ar6HLC5LW9U8ayEJeOpqi1VdS3wrTnzHglsqqp7qupeYBNw1AjjWQn9xPORqnqwvb2S7l4NMLrts1A8Go5x+47vcHwj/BvqZ9sBvInuB8LXhhRXv7H9T+Cs9n2nqu4as/gK+M72+nHMc0+ZlVJVHwXuWaTKscD51bkS2DPJvsOJbtXr93szSXr3oecBLxlhLDttge/FQm1b1d+RPvYBvVbi9/dAjXvysx9wa8/7ra1s3jpVtR24H3h8n/MOMx6AA1uXhb9P8sPLjKXfeFZi3pVa5qOTXJ3kyiSD2BnuaDwn0R2Z2Zl5VzoeGPz20cMt9zu+0pb7N7SSlowtyXOAA6rqfUOKaVY/2+1pwNOS/GP7ji33YMeO6Ce+04CfSrKVboSxXxhOaH1Zif3ltJj0bVfAB5N8KsnJrWxNVd0B0J6fOLLoBm+htk3q53xK68Z3Tk/3xbFv69CHut5B8x1NnTs83UJ1+pl3mPHcATy5qu5O8gPAXyd5RlV9ZYXjWYl5V2qZT66q25M8Ffhwkuuq6rPDiCfJTwHrgR/Z0XmHFA8Mfvvo4ZbzHR+G5f4NraRFY0vyCOD3gBOHFE+vfrbbrnRd3zbQnS37hyTPrKr7Vjg26C++VwDnVtUZSZ4P/GmLbxhn0Zcyyu/Eajfp2+4F7f/WE4FNST4z6oBGZBI/53fSncmv9nwG8NOsgraO+5mfrcABPe/35+Gn+v+zTpJd6boD3NPnvEOLp53+uxugqj5F1wfyaUOIZyXmXZFlVtXt7fkWYAZ4zjDiSfKjwG8BP1FVX9+ReYcYz0psHz3ccvY5w7Csv6ERx7YH8ExgTOn62QAAHWJJREFUJskWun7vlw1p0IN+P9dLq+obravGTXTJ0DD0E99JwCUAVfVx4NHAPkOJbmkrsb+cFhO97Xr+b90FvJeu+9Ods12+2vMwu5iutIXaNnGfc1XdWVXfbAdg/phvd20b+7aOe/JzFbAuyYFJdqO7uHjuKFeXAbMjcb0M+HBVVSs/Lt3ITAfS/RP75KjiSfKEJLsAtCP36+guol/peBbyAeCIJHu1U5VHtLKRxNPieFR7vQ/wAuDTKx1P64bzR3Q/Ent3wCPZPgvFs0LbRw+3nH3OWMS3yN/0SGOrqvurap+qWltVa+muR/qJqrp61LE1f003WMTsd+xpLH8fPcj4vgAc3uJ7Ol3y86UhxbeUy4BXpfM84P7Zrj9a0nL+j4+1JLsn2WP2Nd3/0et56D70BODS0US4IhZq28R9R+Zcs/RSus8WVub392Atd8SElX7QjZDxb3RnSn6rlb2R7p8mdP8A/pzugqpPAk/tmfe32nw3AS8aZTzAfwNuoBsB45+BFw8pnh+ky8IfAO4GbuiZ96dbnJuBV48yHuCHgOva9rkOOGlI8XwIuBO4pj0uG/H2mTeeldo+PnbqM1pwnzMm8S34Nz3q2ObUnWFIo731ud0CvI3uoMJ1wHFj9rkeBPxj2wdcAxwxxNgupOu6/Y22/z4JeA3wmp5td1aL/bphfq6T8Jjvs5+EB90Idv/aHjf0/F0/HrgCuLk97z3qWHeyffN9L+Zt22r/jizQ1j9tbbmWLuHZt6f+wH9/D/KRFqQkSZIkTbRx7/YmSZIkSQNh8iNJkiRpKpj8SJIkSZoKJj+SJEmSpoLJjyRJkqSpYPIjSZIkaSqY/EiSJEmaCiY/kiRJkqaCyc8ql+RdSf6/UcchSZIkjbtU1ahjUJ+SnAj8TFX9P6OOZTVIshb4HPDIqto+2mgkSZI0ap75GSNJdh11DKtJkl1WePl+HtICkpyW5M+GvM61SWpcv5tJTkzysVHHIUlamMnPiCXZkuQ3klwLPJDk9Uk+m+SrST6d5KWt3tOBdwHPT7ItyX2t/Nwkb26vNyTZmuTUJHcluSPJq3vW9fgkf5PkK0muSvLmfv5Rtx8br0lyc5J7k5yVJG3aQ34Azf1xkmSmreefWtx/0+K4oCeOtT3zf1+STUnuSXJTkp/smXZukncmuTzJA8BhSY5J8i9tWbcmOa0n9I+25/vaup+f5BFtG3++baPzkzxuTuwnJfkC8OH+P0lpsoxrgiFJ0nKY/IyHVwDHAHsCNwE/DDwO+N/AnyXZt6puBF4DfLyqHltVey6wrO9q8+4HnASclWSvNu0s4IFW54T26NePAz8IPAv4SeDIHZj3OOCVLabvBj4O/AmwN3Aj8AaAJLsDm4D3AE+k2y5/mOQZPcv6H8BbgD2Aj7X2vIpu2x0D/FySl7S6L2zPe7Zt9nHgxPY4DHgq8FjgD+bE+yPA03ewjdKqN8/BmCcn+cskX0ryuSS/2OodBfwm8N/bgYV/TXJYkut6lvWhJJ/sef+x2e9mkifNt9w27RFJNraDQHcnuSTJ3m3yww5oLNGe/5nkxp6DSYe08qe3AzP3JbkhyU/0zDOT5Gd63j/kbM5CB4MWOkAlSRovJj/j4cyqurWq/r2q/ryqbq+qb1XVxcDNwKE7sKxvAG+sqm9U1eXANuB703UR+2/AG6rqwar6NHDeDiz39Kq6r6q+AHwEePYOzPsnVfXZqrof+Fvgs1X1oXYdzp8Dz2n1fhzYUlV/UlXbq+qfgb8EXtazrEur6h/b9vlaVc1U1XXt/bXAhXTJy0KOB95WVbdU1TbgdcBxc45yn1ZVD1TVv+9AG6VJMXswZm/gvcC/0h24OBz45SRHVtXfAf8HuLgdWHgW3UGN70myT/s+PRPYP8keSR4D/ADwD0keAfzNfMtt6/9F4CV03+MnAffSHbiB+Q9ozCvJy4HT6A6OfCfwE8DdSR7Z1v9BuoMsvwBckOR7d2AbPexg0A4coJIkjZDJz3i4dfZFklcluaYdkbyP7gfEPjuwrLvnXNz/IN3ZjScAu/aua87rpXxxnmX2686e1/8+z/vZZT0FeO5s21v7j6c7UzXrITEneW6Sj7QjyPfT/fhYbHs9Cfh8z/vP022XNQutQ5oyZ1bVrXT7nidU1Rur6j+q6hbgj+nO5D5MVX0NuJouQVkPXEt3dvYFwPOAm6vqbrqkYbHl/izwW1W1taq+TpfAvGwnuuH9DPA7VXVVdTZX1edbLI+lO6DzH1X1YeB9dElfv5ZzMEiSNEL26R4PBZDkKXQ/Ag6nO3r4zSTXAOmtt5O+BGwH9gf+rZUdsIzlzXoA+I6e99+1UMU+3Ar8fVX92CJ15m6D99B1W3tRVX0tye/z7eRnvu11O12SNevJdNvlTrpts9B80rSYTf6fAjxpTvetXYB/WGTevwc2AFvb63vpzuB8vb3vZ7lPAd6b5Fs907/JQw9Q9OMA4LPzlD8JuLWqepf/ebqzUP1azsEgSdIIeeZnvOxO98P7SwDpBit4Zs/0O+m6key2owuuqm8CfwWcluQ7knwfXXeQ5boGeGG7NuBxdN3Idtb7gKcleWWSR7bHD7a+9AvZA7inJT6H0l0TNOtLwLforu2ZdSHwK0kOTPJYvt11x6Gwpc5s8n8r8Lmq2rPnsUdVHT2nXq/Z5OeF7fXf0yU/P8K3k5+llnsr3cGM3umPrqrbFljnQm6lu8ZwrtuBA1r3u1lPBm5rr5dzQMcDJ5I05kx+xki7DucMur7zdwIHA//YU+XDwA3AF5N8eSdWcQrdYAhfBP6ULhH4+jJj3gRcTNfF5VN0CczOLuurwBF03V9ub3G+FXjUIrP9PPDGJF8F/hdwSc/yHqQbHOEfWze65wHn0LX9o3T3APoaXZ9/SQ/1SeArbQCExyTZJckzk/xgm34nsHZOEvFPwPfSXaf4yaq6gdadlW8PVrDUct8FvKWdCSfJE5Ic26bNd0BjIf8X+LUkP9AGJPietsxP0CU4v94OsGwAXgxc1Oa7Bviv7SDR99ANHNOvnT5AJUkaDm9yOsWSvBX4rqrakVHfJE2oJFvobqT8ofb+SXQHZA6jOwhxE/D6qvpQkscDlwLPoDuTMzuS2seBr1XVYe39XwDPqKqn96xnseU+Avhlumt/ngTcRXd29jfbvG8Efg54JHBUVV25SHteA/wKXZe2LcArq+pf0o0g+Yd01+rcRneN0XvbPPvQdad9Pt1BnU3Aj87eXDpJAeuqanN7fy6wtape35Ke97Z5v1VVO3K9piRpCEx+pkjr6rYbcB3dRceX0/3Q+euRBiZJkiQNgQMeTJc96Lq6zR5NPQO4NMkP0w1B/TBV5YW8kiRJmgie+ZEkrUpJ3gX81DyT/qyqXjPseCRJ48/kR5IkSdJUcLQ3SZIkSVNhrK/52WeffWrt2rVL1nvggQfYfffdVz6gEZnk9k1y22D82/epT33qy1X1hFHHMWr97GvG/bPcEbZlPE1yW9zXSBoXY538rF27lquvvnrJejMzM2zYsGHlAxqRSW7fJLcNxr99ST4/6hjGQT/7mnH/LHeEbRlPk9wW9zWSxsWS3d6SHJDkI0luTHJDkl9q5Xsn2ZTk5va8VytPkjOTbE5ybZJDepZ1Qqt/cxLvLSNJkiRpaPq55mc7cGq7Qd3zgNcmOQjYCFxRVeuAK9p7gBcB69rjZOCd0CVLwBvo7vR9KPCG2YRJkiRJklbakslPVd1RVf/cXn8VuJHubtnHAue1aucBL2mvjwXOr86VwJ5J9gWOBDZV1T1VdS/dXbOPGmhrJEmSJGkBO3TNT5K1wHOATwBrquoO6BKkJE9s1fYDbu2ZbWsrW6h87jpOpjtjxJo1a5iZmVkyrm3btvVVb7Wa5PZNcttg8tsnSZK0mvSd/CR5LPCXwC9X1VeSLFh1nrJapPyhBVVnA2cDrF+/vvq5+PMdF1zKGR97YMl6/dpy+jEDW9YgTNJFsHNNcttg8ts3Ta677X5O3Pj+gS1v3PYzkiRNg77u85PkkXSJzwVV9Vet+M7WnY32fFcr3woc0DP7/sDti5RLkiRJ0orrZ7S3AO8Gbqyqt/VMugyYHbHtBODSnvJXtVHfngfc37rHfQA4IslebaCDI1qZJEmSJK24frq9vQB4JXBdkmta2W8CpwOXJDkJ+ALw8jbtcuBoYDPwIPBqgKq6J8mbgKtavTdW1T0DaYUkSZIkLWHJ5KeqPsb81+sAHD5P/QJeu8CyzgHO2ZEAJUmSJGkQ+rrmR5IkSZJWO5MfSZIkSVPB5EeSJEnSVDD5kSRJkjQVTH4kSZIkTQWTH0mSJElTweRHkiRJ0lQw+ZEkSZI0FUx+JEmSJE0Fkx9JkiRJU8HkR5IkSdJUMPmRJEmSNBVMfiRJkiRNBZMfSZIkSVPB5EeSJEnSVDD5kSRJkjQVTH4kjVySc5LcleT6nrLTktyW5Jr2OLpn2uuSbE5yU5Ije8qPamWbk2wcdjskSdJ4M/mRNA7OBY6ap/z3qurZ7XE5QJKDgOOAZ7R5/jDJLkl2Ac4CXgQcBLyi1ZUkSQJg11EHIElV9dEka/usfixwUVV9Hfhcks3AoW3a5qq6BSDJRa3upwccriRJWqU88yNpnJ2S5NrWLW6vVrYfcGtPna2tbKFySZIkwDM/ksbXO4E3AdWezwB+Gsg8dYv5D+bUQgtPcjJwMsCaNWuYmZlZNJg1j4FTD97eT9x9WWp9K2nbtm0jXf8g2ZbxNEltkTRZTH4kjaWqunP2dZI/Bt7X3m4FDuipuj9we3u9UPl8yz8bOBtg/fr1tWHDhkXjeccFl3LGdYPbZW45fvH1raSZmRmWau9qYVvG0yS1RdJksdubpLGUZN+ety8FZkeCuww4LsmjkhwIrAM+CVwFrEtyYJLd6AZFuGyYMUuSpPG2ZPLjELSSVlqSC4GPA9+bZGuSk4DfSXJdkmuBw4BfAaiqG4BL6AYy+DvgtVX1zaraDpwCfAC4Ebik1ZUkSQL66/Z2LvAHwPlzyn+vqn63t2DOELRPAj6U5Glt8lnAj9F1WbkqyWVV5ShMkqiqV8xT/O5F6r8FeMs85ZcDlw8wNEmSNEGWTH4cglaSJEnSJFjO1bunJHkVcDVwalXdSzes7JU9dXqHmp07BO1z51vojo7ABJM1CtN8JnnUnEluG0x++yRJklaTnU1+VmwI2h0dgQkmaxSm+UzyqDmT3DaY/PZJkiStJjuVMaz0ELSSJEmSNGg7NdS1Q9BKkiRJWm2WPPPThqDdAOyTZCvwBmBDkmfTdV3bAvwsdEPQJpkdgnY7bQjatpzZIWh3Ac5xCFpJkiRJw9TPaG8OQStJkiRp1dupbm+SJEmStNqY/EiSJEmaCiY/kiRJkqaCyY8kSZKkqWDyI0mSJGkqmPxIkiRJmgomP5IkSZKmgsmPJEmSpKlg8iNJkiRpKpj8SJIkSZoKJj+SJEmSpoLJjyRJkqSpYPIjSZIkaSqY/EiSJEmaCiY/kiRJkqaCyY8kSZKkqWDyI0mSJGkqmPxIkiRJmgomP5IkSZKmgsmPpLGQ5JwkdyW5vqds7ySbktzcnvdq5UlyZpLNSa5NckjPPCe0+jcnOWEUbZEkSePJ5EfSuDgXOGpO2UbgiqpaB1zR3gO8CFjXHicD74QuWQLeADwXOBR4w2zCJEmSZPIjaSxU1UeBe+YUHwuc116fB7ykp/z86lwJ7JlkX+BIYFNV3VNV9wKbeHhCJUmSptSuow5AkhaxpqruAKiqO5I8sZXvB9zaU29rK1uo/GGSnEx31og1a9YwMzOzeCCPgVMP3r4TTZjfUutbSdu2bRvp+gfJtoynSWqLpMmyZPKT5Bzgx4G7quqZrWxv4GJgLbAF+MmqujdJgLcDRwMPAidW1T+3eU4AXt8W++aqOg9J2jmZp6wWKX94YdXZwNkA69evrw0bNiy6wndccClnXDe440Vbjl98fStpZmaGpdq7WtiW8TRJbZE0Wfrp9nYu9sOXNBp3tu5stOe7WvlW4ICeevsDty9SLkmStHTyYz98SSN0GTA7YtsJwKU95a9qo749D7i/dY/7AHBEkr3aAZYjWpkkSdJOX/MzNv3wYbL64s9nkvtOT3LbYPLbN0hJLgQ2APsk2Up3tvh04JIkJwFfAF7eql9O1712M10X21cDVNU9Sd4EXNXqvbGq5h68kSRJU2rQAx4MvR8+TFZf/PlMct/pSW4bTH77BqmqXrHApMPnqVvAaxdYzjnAOQMMTZIkTYidHerafviSJEmSVpWdTX7shy9JkiRpVelnqGv74UuSJEla9ZZMfuyHL0mSJGkS7Gy3N0mSJElaVUx+JEmSJE0Fkx9JkiRJU8HkR5IkSdJUMPmRJEmSNBVMfiRJkiRNBZMfSZIkSVPB5EeSJEnSVDD5kSRJkjQVTH4kSZIkTQWTH0mSJElTweRHkiRJ0lQw+ZEkSZI0FUx+JEmSJE0Fkx9JkiRJU8HkR5IkSdJUMPmRJEmSNBVMfiRJkiRNBZMfSZIkSVPB5EeSJEnSVDD5kSRJkjQVTH4kjbUkW5Jcl+SaJFe3sr2TbEpyc3veq5UnyZlJNie5Nskho41ekiSNE5MfSavBYVX17Kpa395vBK6oqnXAFe09wIuAde1xMvDOoUcqSZLG1rKSH4/IShqRY4Hz2uvzgJf0lJ9fnSuBPZPsO4oAJUnS+Nl1AMs4rKq+3PN+9ojs6Uk2tve/wUOPyD6X7ojscwewfkmTrYAPJingj6rqbGBNVd0BUFV3JHliq7sfcGvPvFtb2R1zF5rkZLqzQ6xZs4aZmZlFg1jzGDj14O3LbMq3LbW+lbRt27aRrn+QbMt4mqS2SJosg0h+5joW2NBenwfM0CU//3lEFrgyyZ5J9p39ASNJC3hBVd3eEpxNST6zSN3MU1bzVWxJ1NkA69evrw0bNiwaxDsuuJQzrhvcLnPL8YuvbyXNzMywVHtXC9syniapLZImy3L/kw/8iOyOHo2FyToiO59JPoI2yW2DyW/fMFTV7e35riTvBQ4F7pw9eNK6td3Vqm8FDuiZfX/g9qEGLEmSxtZyk5+BH5Hd0aOxMFlHZOczyUfQJrltMPntW2lJdgceUVVfba+PAN4IXAacAJzeni9ts1wGnJLkIrputfd7dlmSJM1aVsbgEVlJK2wN8N4k0O2v3lNVf5fkKuCSJCcBXwBe3upfDhwNbAYeBF49/JAlSdK42unkxyOyklZaVd0CPGue8ruBw+cpL+C1QwhNkiStQss58+MRWUmSJEmrxk4nPx6RlSRJkrSaLOsmp5IkSZK0Wpj8SJIkSZoKJj+SJEmSpoLJjyRJkqSpYPIjSZIkaSqY/EiSJEmaCiY/kiRJkqaCyY8kSZKkqWDyI0mSJGkqmPxIkiRJmgomP5IkSZKmgsmPJEmSpKlg8iNJkiRpKpj8SJIkSZoKJj+SJEmSpoLJjyRJkqSpYPIjSZIkaSqY/EiSJEmaCiY/kiRJkqaCyY8kSZKkqbDrqAOQpGm0duP7B7q8LacfM9DlSZI0iTzzI0mSJGkqmPxIkiRJmgpDT36SHJXkpiSbk2wc9volTT73M5IkaT5DveYnyS7AWcCPAVuBq5JcVlWfHmYckibXtO5nduQaolMP3s6JS9T3GiJJ0iQa9oAHhwKbq+oWgCQXAccCE/2jRNJQuZ8ZgEEOyGAiJUkaF8NOfvYDbu15vxV4bm+FJCcDJ7e325Lc1Mdy9wG+PJAIgbx1UEsamIG2b8xMcttg/Nv3lFEHsAKW3M/ATu1rxv2z7NsvDrktK7xPnZjPhcluyyTuayStQsNOfjJPWT3kTdXZwNk7tNDk6qpav5zAxtkkt2+S2waT374xteR+BnZ8XzNJn6VtGU+2RZJW3rAHPNgKHNDzfn/g9iHHIGmyuZ+RJEnzGnbycxWwLsmBSXYDjgMuG3IMkiab+xlJkjSvoXZ7q6rtSU4BPgDsApxTVTcMYNE71E1uFZrk9k1y22Dy2zd23M/0xbaMJ9siSSssVQ/rCi9JkiRJE2foNzmVJEmSpFEw+ZEkSZI0FVZ98pPkqCQ3JdmcZOOo49kZSbYkuS7JNUmubmV7J9mU5Ob2vFcrT5IzW3uvTXLIaKN/uCTnJLkryfU9ZTvcniQntPo3JzlhFG2Za4G2nZbktvb5XZPk6J5pr2ttuynJkT3lq/7vdpqM4+eV5IAkH0lyY5IbkvxSKx/Ydy3JD7R90+Y273zDiA+yTbsk+Zck72vvD0zyiRbXxW0AC5I8qr3f3Kav7VnGyL9zSfZM8hdJPtM+n+ev1s8lya+0v6/rk1yY5NGr9XORJACqatU+6C5m/izwVGA34F+Bg0Yd1060Ywuwz5yy3wE2ttcbgbe210cDf0t3L5PnAZ8YdfzztOeFwCHA9TvbHmBv4Jb2vFd7vdeYtu004NfmqXtQ+5t8FHBg+1vdZVL+bqflMa6fF7AvcEh7vQfwb+1vbmDfNeCTwPPbPH8LvGiF2/SrwHuA97X3lwDHtdfvAn6uvf554F3t9XHAxe31WHzngPOAn2mvdwP2XI2fC90Ng///du4uRMoqjuP49w+96ZZZQWEa1IZ0myIhGREa9kpb4EUQ2Bt0UxddBeFV9xVdFF6kBUokZFbelVTQTUkZZS/2srWSm5srmCZdpOWvi/MfG5adpV1G5zkzvw8c5nnO8+w+57//5+zMmTlzxoB5bfl4qNa8uLi4uEiq/pOfG4BRST9LOgFsA0Z63KZuGaE8gZKP97bVb1HxCbAwIhb1ooGdSPoIODKlerbx3AbsknRE0u/ALuD2M9/6mXWIrZMRYJukvySNAaOUe7af79t+1Mh8SZqQ9HluHwf2UV6sdqWv5bEFkj6WJGBL2+/quohYAtwFbMr9AFYD2zvE0opxO7Amz+95n4uIBZQ3STYDSDoh6SiV5oWyKuy8iDgHmA9MUGFezMxaah/8LAYOtO2PZ11tBLwXEXsi4rGsu0LSBJQXOcDlWV9rzLONp7Y4n8gpK6+0prPQP7ENusbnK6cXLQN2072+tji3p9afKS8ATwGncv8y4Kikv6e5/uk25/FjeX4T+twwcBh4NafwbYqIISrMi6RfgWeBXyiDnmPAHurMi5kZUP/gZ7p5zjWu3b1K0nLgDuDxiLh5hnP7JeaWTvHUFOdG4FrgesoLhOeyvh9is4bnKyIuBN4EnpT0x0ynTlM30/141uKOiLuBSUl72qtnuH5jY6F8UrIc2ChpGfAnZZpbJ42NJd/IGaFMVbsSGKI8T3W6fmNjMTNrqX3wMw5c1ba/BDjYo7bMmaSD+TgJvEWZCnCoNZ0tHyfz9Fpjnm081cQp6ZCkfySdAl6m5A/6IDYDGpyviDiXMvB5TdKOrO5WXxvP7an1Z8Iq4J6I2E+Z+rSa8knQwpxuNfX6p9ucxy+mTEdtQp8bB8Yl7c797ZTBUI15uRUYk3RY0klgB3AjdebFzAyof/DzKbA0V545j/IFy509btOsRMRQRFzU2gbWAl9T4mit7vMg8E5u7wTW5wpBK4FjrakUDTfbeN4F1kbEJfnu49qsa5wp37m6j5I/KLHdnysgXQMspXxRufr7dsA0Ml/5XYrNwD5Jz7cd6kpfy2PHI2JlXmt92+/qKklPS1oi6WrK3/cDSQ8AHwLrOsTSinFdni8a0Ock/QYciIjrsmoN8C0V5oUy3W1lRMzPa7ViqS4vZman9WqlhW4Vyko5P1BWjNnQ6/bMof3DlBVuvgS+acVAmSf9PvBjPl6a9QG8lPF+BazodQzTxPQ6ZfrXSco7e4/OJR7gEcoXY0eBh3sd1wyxbc2276U8cS9qO39DxvY9bSsy1X7fDlppYr6AmyhThPYCX2S5s5t9DVhBGcz/BLwIxFmI6xb+W+1tmPIieRR4Azg/6y/I/dE8Ptz28z3vc5QpsJ9lbt6mrNZWZV6AZ4Dv8npbKSu2VZkXFxcXF0nlH6aZmZmZmVm/q33am5mZmZmZ2f/iwY+ZmZmZmQ0ED37MzMzMzGwgePBjZmZmZmYDwYMfMzMzMzMbCB78mJmZmZnZQPDgx8zMzMzMBsK/e1Rjavv8ff0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff24c7cea58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram of \"full_data\"\n",
    "full_data.hist(figsize=(14,12));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff249e89518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pair plot of the ratings, favorite count and retweet counts colored by \"dogo_stage\"\n",
    "sns.pairplot(full_data[['dogo_stage','rating_numerator','rating_denominator','favorite_count','retweet_count']],\\\n",
    "             hue=\"dogo_stage\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that in the case of favorite and retweet there is a correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff249e53710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot with a linear regression between \"retweet_count\" and \"favorite_count\"\n",
    "sns.regplot(data=full_data, x=\"retweet_count\", y=\"favorite_count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to check the percentage of dogo's types on the rows we have dogo we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No doggo    1699\n",
       "pupper       203\n",
       "doggo         63\n",
       "puppo         22\n",
       "floofer        7\n",
       "Name: dogo_stage, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of tweets with each doggo category\n",
    "full_data.dogo_stage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff24a4b7c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a pie plot with the different percentages of dogos\n",
    "full_data.query('dogo_stage != \"No doggo\"').dogo_stage.value_counts().plot.pie(autopct='%1.1f%%', figsize=(8,8));\n",
    "plt.title('Percentage of different types of dogos')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next plot I will create a bar plot with the top 10 most favorites breeds of dogs that are predicted with a confidence greater than or equal to 95%. To do that I did it using a SQL query and querying the data in the database that I created before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>breed</th>\n",
       "      <th>total_sum_favorites</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pembroke</td>\n",
       "      <td>313152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Samoyed</td>\n",
       "      <td>246169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>149438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>137622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pug</td>\n",
       "      <td>119400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>116557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>96036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>chow</td>\n",
       "      <td>65927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>57867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>44666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                breed  total_sum_favorites\n",
       "0            Pembroke               313152\n",
       "1             Samoyed               246169\n",
       "2    golden_retriever               149438\n",
       "3  Labrador_retriever               137622\n",
       "4                 pug               119400\n",
       "5      French_bulldog               116557\n",
       "6          Pomeranian                96036\n",
       "7                chow                65927\n",
       "8   Shetland_sheepdog                57867\n",
       "9           Chihuahua                44666"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Table with the breeds of dogs with more favorites\n",
    "favorite_breeds = pd.read_sql('''SELECT p1 AS breed, SUM(favorite_count) AS total_sum_favorites \n",
    "                                 FROM twitter_data \n",
    "                                 WHERE p1_conf >= 0.95 AND p1 != \"Not a dog\"\n",
    "                                 GROUP BY p1 \n",
    "                                 ORDER BY total_sum_favorites DESC\n",
    "                                 LIMIT 10''', conn)\n",
    "\n",
    "#Show the top 10 most favorites breeds\n",
    "favorite_breeds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create the next plot I found some help on how to change the size of a Seaborn barplot on this [page](https://kite.com/python/answers/how-to-change-the-seaborn-plot-figure-size-in-python)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff24a0b6f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Configure the dimensions of the plot\n",
    "fig_dims = (15, 8)\n",
    "fig, ax = plt.subplots(figsize=fig_dims)\n",
    "\n",
    "# Plot in a barplot the top 10 most favorites breeds of dogs\n",
    "sns.barplot(data = favorite_breeds, x = 'breed', y = 'total_sum_favorites', ax=ax);\n",
    "plt.xlabel('Breed of dog');\n",
    "plt.ylabel('Total sum of favorites');\n",
    "plt.title('Top 10 most favorites breeds of dogs');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create HTML file\n",
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'wrangle_act.ipynb'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}