wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrangle and Analyze Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import numpy\n",
    "import requests\n",
    "import tweepy \n",
    "import json\n",
    "import config\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Import data ###\n",
    "\n",
    "# Save csv file as df\n",
    "twitter_archive = pd.read_csv('twitter-archive-enhanced.csv')\n",
    "\n",
    "# Image prediction file to df\n",
    "image_tsv = requests.get('https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv')\n",
    "open('image_predictions.tsv', 'wb').write(image_tsv.content) # write to file\n",
    "image_pred_df = pd.read_csv('image_predictions.tsv', sep = '\\t')\n",
    "\n",
    "# Connect to tweepy auth and instantiate API\n",
    "auth = tweepy.OAuthHandler(config.CONSUMER_KEY, config.CONSUMER_SECRET)\n",
    "auth.set_access_token(config.ACCESS_TOKEN, config.ACCESS_TOKEN_SECRET)\n",
    "\n",
    "api = tweepy.API(auth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.head() # view archive header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list to store tweet records\n",
    "raw_data = []\n",
    "not_found = [] # list to store IDs of tweets that could not get status on\n",
    "\n",
    "for i in twitter_archive.tweet_id:\n",
    "    temp_dict = {} # temporary dict\n",
    "    try:\n",
    "        status = api.get_status(i)\n",
    "        temp_dict['tweet_id'] = i\n",
    "        temp_dict['retweet_count'] = status._json['retweet_count']\n",
    "        temp_dict['favorite_count'] = status._json['favorite_count']\n",
    "        raw_data.append(temp_dict)\n",
    "    except:\n",
    "        not_found.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write dict to json file\n",
    "with open('tweet_json.txt', 'w') as json_file:\n",
    "    json.dump(raw_data, json_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "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>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>7417</td>\n",
       "      <td>35140</td>\n",
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       "      <th>1</th>\n",
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       "      <td>5517</td>\n",
       "      <td>30410</td>\n",
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       "      <th>2</th>\n",
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       "      <td>3640</td>\n",
       "      <td>22868</td>\n",
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       "      <th>3</th>\n",
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       "      <td>7584</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>8166</td>\n",
       "      <td>36652</td>\n",
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      "text/plain": [
       "             tweet_id  retweet_count  favorite_count\n",
       "0  892420643555336193           7417           35140\n",
       "1  892177421306343426           5517           30410\n",
       "2  891815181378084864           3640           22868\n",
       "3  891689557279858688           7584           38396\n",
       "4  891327558926688256           8166           36652"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# json data to pandas df\n",
    "tweets_df = pd.read_json('tweet_json.txt')\n",
    "tweets_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Quality Issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Completeness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WeRateDogs Archive Data:\n",
      " in_reply_to_status_id         2278\n",
      "in_reply_to_user_id           2278\n",
      "retweeted_status_id           2175\n",
      "retweeted_status_user_id      2175\n",
      "retweeted_status_timestamp    2175\n",
      "expanded_urls                   59\n",
      "dtype: int64 \n",
      "\n",
      "Image Predictions Data:\n",
      " Series([], dtype: int64) \n",
      "\n",
      "Tweet RT & Favorite Count Data:\n",
      " Series([], dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "# Identify completeness issues \n",
    "print(\"WeRateDogs Archive Data:\\n\",twitter_archive.isnull().sum().loc[lambda x: x>0].dropna(), '\\n')\n",
    "print(\"Image Predictions Data:\\n\", image_pred_df.isnull().sum().loc[lambda x: x>0].dropna(), '\\n')\n",
    "print(\"Tweet RT & Favorite Count Data:\\n\", tweets_df.isnull().sum().loc[lambda x: x>0].dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The WeRateDogs Twitter archive data has 6 fields out of 17 total with missing values. The other data sets do not have missing values in their fields. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image Prediction Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Image predictions df info\n",
    "image_pred_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `tweet_id` should be a string.\n",
    "- `p1`, `p2`, and `p3` the predictions, have dog breed names with underscores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WeRateDogs Archive Tweet Archive Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Archive data info\n",
    "twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.text[3] # view the text field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56       A\n",
       "649      A\n",
       "759     AN\n",
       "801      A\n",
       "1002     A\n",
       "        ..\n",
       "2349    AN\n",
       "2350     A\n",
       "2352     A\n",
       "2353     A\n",
       "2354     A\n",
       "Name: name, Length: 70, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive.name = twitter_archive.name.map(lambda x: x.upper())\n",
    "twitter_archive.name[twitter_archive.name.isin(['THE', 'A', 'AN'])] # find names that are definite articles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `tweet_id` should be a string. This field does not provide information as a numerical value.\n",
    "- `timestamp` should be in datetime format instead of string format.\n",
    "- `name` field includes invalid dog names that are definite articles. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tweet RT Count & Favorite Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 877 entries, 0 to 876\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   tweet_id        877 non-null    int64\n",
      " 1   retweet_count   877 non-null    int64\n",
      " 2   favorite_count  877 non-null    int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 20.7 KB\n"
     ]
    }
   ],
   "source": [
    "# Tweet rt count and favorite df info\n",
    "tweets_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `tweet_id` should be a string."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consistency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ \n",
      "\n",
      "13 10\n"
     ]
    }
   ],
   "source": [
    "# Accessing third series element as example\n",
    "print(twitter_archive.text[3], '\\n')\n",
    "print(twitter_archive.rating_numerator[3], twitter_archive.rating_denominator[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The `text` field contains dog image URLs which do not provide informative textual information. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seeing as the `text` field from the archive data contained the dog ratings, I decided to extract the ratings in this field and compare them to the `rating_numerator` and `rating_denominator` fields to test for accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract ratings from [text] field in twitter_archive using regex expressions\n",
    "numerator_test = twitter_archive.text.str.extract(r'([0-9]{1,4})\\/', expand=False) # create a series with extracted rating numerator from text\n",
    "numerator_test = pd.to_numeric(numerator_test, errors='coerce').fillna(0).astype(np.int64)\n",
    "\n",
    "denominator_test = twitter_archive.text.str.extract(r'\\/([0-9]{1,3})', expand=False) # create series with extracted rating denominator from text\n",
    "denominator_test = pd.to_numeric(denominator_test, errors='coerce').fillna(0).astype(np.int64)\n",
    "\n",
    "# Create series with rating as decimal\n",
    "ratings_test_series = pd.Series(numerator_test / denominator_test).replace(np.inf, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rating numerator and denominators are extracted from the `text` field and saved as separate Series. Then, I create a series that calculates the rating by dividing the extracted numerator by the extracted denominator. Many othe values are greater than 1 because the WeRateDogs page nearly always gives ratings greater than 10/10 because the animals are just so cute that they deserve 12/10--or even greater in some instances!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177.6"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_test_series.max() # extract max rating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The greatest rating extracted from the text was 66$ out of 10! Quite the prankster rating.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh\n",
      "1776 / 10\n"
     ]
    }
   ],
   "source": [
    "print(twitter_archive.text[979])\n",
    "print(twitter_archive.rating_numerator[979], '/' , twitter_archive.rating_denominator[979])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a series calculating rating as [rating_numerator] / [rating_denominator]\n",
    "archive_rating = pd.Series(twitter_archive.rating_numerator / twitter_archive.rating_denominator).replace(np.inf, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [self, other]\n",
      "Index: []\n",
      "\n",
      "Count -->  0\n"
     ]
    }
   ],
   "source": [
    "# Compare numerators\n",
    "print(archive_rating.compare(ratings_test_series))\n",
    "print(\"\\nCount --> \", len(archive_rating.compare(ratings_test_series)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dog ratings do appear to be successfully parsed from the tweet text since the comparison yields no differences between the two series. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tidiness Issues\n",
    "\n",
    "- The retweet and favorite counts are in a separate table than the tweet archive table.\n",
    "- The `doggo`, `floofer`, `pupper`, and `puppo` fields represent an original ranking that should be one column. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image Prediction Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   object \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(1), object(5)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# tweet_id should be a string\n",
    "image_pred_df['tweet_id'] = image_pred_df['tweet_id'].astype(str)\n",
    "image_pred_df.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `tweet_id` field is now a string."
   ]
  },
  {
   "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>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>p3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Welsh Springer Spaniel</td>\n",
       "      <td>Collie</td>\n",
       "      <td>Shetland Sheepdog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Redbone</td>\n",
       "      <td>Miniature Pinscher</td>\n",
       "      <td>Rhodesian Ridgeback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>German Shepherd</td>\n",
       "      <td>Malinois</td>\n",
       "      <td>Bloodhound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rhodesian Ridgeback</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Miniature Pinscher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Miniature Pinscher</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>Doberman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bernese Mountain Dog</td>\n",
       "      <td>English Springer</td>\n",
       "      <td>Greater Swiss Mountain Dog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       p1                  p2                          p3\n",
       "0  Welsh Springer Spaniel              Collie           Shetland Sheepdog\n",
       "1                 Redbone  Miniature Pinscher         Rhodesian Ridgeback\n",
       "2         German Shepherd            Malinois                  Bloodhound\n",
       "3     Rhodesian Ridgeback             Redbone          Miniature Pinscher\n",
       "4      Miniature Pinscher          Rottweiler                    Doberman\n",
       "5    Bernese Mountain Dog    English Springer  Greater Swiss Mountain Dog"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove underscores from name and title them\n",
    "image_pred_df[['p1','p2','p3']] = image_pred_df[['p1','p2','p3']].applymap(lambda x: x.replace(\"_\", \" \").title())\n",
    "image_pred_df[['p1','p2','p3']].loc[:5,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted breeds are now titled, more visually appealing and easier to use in any visualizations or table operations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WeRateDogs Archive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>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>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>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>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>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>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>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>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>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>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>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>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                  timestamp  \\\n",
       "0  892420643555336193  2017-08-01 16:23:56 +0000   \n",
       "1  892177421306343426  2017-08-01 00:17:27 +0000   \n",
       "2  891815181378084864  2017-07-31 00:18:03 +0000   \n",
       "3  891689557279858688  2017-07-30 15:58:51 +0000   \n",
       "4  891327558926688256  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  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 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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove fields with significant missing data\n",
    "remove_cols = [\"in_reply_to_status_id\", \"in_reply_to_user_id\", \"retweeted_status_id\", \"retweeted_status_user_id\", \"retweeted_status_timestamp\", \"expanded_urls\"]\n",
    "twitter_archive = twitter_archive.drop(remove_cols, axis=1)\n",
    "twitter_archive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change tweet_id to str\n",
    "twitter_archive['tweet_id'] = twitter_archive['tweet_id'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2356 non-null   object             \n",
      " 1   timestamp           2356 non-null   datetime64[ns, UTC]\n",
      " 2   source              2356 non-null   object             \n",
      " 3   text                2356 non-null   object             \n",
      " 4   rating_numerator    2356 non-null   int64              \n",
      " 5   rating_denominator  2356 non-null   int64              \n",
      " 6   name                2356 non-null   object             \n",
      " 7   doggo               2356 non-null   object             \n",
      " 8   floofer             2356 non-null   object             \n",
      " 9   pupper              2356 non-null   object             \n",
      " 10  puppo               2356 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), int64(2), object(8)\n",
      "memory usage: 202.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Change timestamp to datetime format\n",
    "twitter_archive['timestamp'] = pd.to_datetime(twitter_archive['timestamp'])\n",
    "twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace invalid dog names with empty str\n",
    "twitter_archive['name'] = twitter_archive['name'].replace(to_replace=['THE', 'A', 'AN'], value=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove URL from text field\n",
    "twitter_archive['text'] = twitter_archive['text'].str.replace(r'(\\bhttps.*)', repl=\"\", regex=True) # remove URLS\n",
    "twitter_archive['text'] = twitter_archive['text'].str.strip() # strip whitespace\n",
    "twitter_archive['text'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The text field is now cleaned of the image URL. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RTs and Favorites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 877 entries, 0 to 876\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   tweet_id        877 non-null    object\n",
      " 1   retweet_count   877 non-null    int64 \n",
      " 2   favorite_count  877 non-null    int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 20.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Change tweet_id to string\n",
    "tweets_df['tweet_id'] = tweets_df['tweet_id'].astype(str)\n",
    "tweets_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Tidy Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join retweet and favorite counts to archive data\n",
    "# Set tweet_id as index, comment out after\n",
    "tweets_df.set_index('tweet_id', inplace=True)\n",
    "twitter_archive.set_index('tweet_id', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join the DFs\n",
    "twitter_archive = twitter_archive.join(tweets_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The archive data now includes retweet (RT) counts and favorite counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the \"Dogtionary\" columns into a new column and drop them\n",
    "merge_cols = [\"doggo\", \"floofer\",\"pupper\",\"puppo\"]\n",
    "twitter_archive['dogtionary'] = twitter_archive['text'].str.extract('(doggo|floofer|pupper|puppo)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive = twitter_archive.drop(merge_cols, axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2356 entries, 892420643555336193 to 666020888022790149\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   timestamp           2356 non-null   datetime64[ns, UTC]\n",
      " 1   source              2356 non-null   object             \n",
      " 2   text                2356 non-null   object             \n",
      " 3   rating_numerator    2356 non-null   int64              \n",
      " 4   rating_denominator  2356 non-null   int64              \n",
      " 5   name                2356 non-null   object             \n",
      " 6   retweet_count       877 non-null    float64            \n",
      " 7   favorite_count      877 non-null    float64            \n",
      " 8   dogtionary          399 non-null    object             \n",
      "dtypes: datetime64[ns, UTC](1), float64(2), int64(2), object(4)\n",
      "memory usage: 248.6+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>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>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>dogtionary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tweet_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>890240255349198849</th>\n",
       "      <td>2017-07-26 15:59:51+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Cassie. She is a college pup. Studying...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>CASSIE</td>\n",
       "      <td>6428.0</td>\n",
       "      <td>29052.0</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889665388333682689</th>\n",
       "      <td>2017-07-25 01:55:32+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here's a puppo that seems to be on the fence a...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>NONE</td>\n",
       "      <td>8795.0</td>\n",
       "      <td>43747.0</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889531135344209921</th>\n",
       "      <td>2017-07-24 17:02:04+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Stuart. He's sporting his favorite fan...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>STUART</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>13853.0</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886366144734445568</th>\n",
       "      <td>2017-07-15 23:25:31+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Roscoe. Another pupper fallen victim t...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>ROSCOE</td>\n",
       "      <td>2781.0</td>\n",
       "      <td>19266.0</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884162670584377345</th>\n",
       "      <td>2017-07-09 21:29:42+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Yogi. He doesn't have any important dog m...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>YOGI</td>\n",
       "      <td>2631.0</td>\n",
       "      <td>18613.0</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672481316919734272</th>\n",
       "      <td>2015-12-03 18:23:34+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Say hello to Jazz. She should be on the cover ...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>JAZZ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672254177670729728</th>\n",
       "      <td>2015-12-03 03:21:00+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Rolf. He's having the time of his life...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>ROLF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672205392827572224</th>\n",
       "      <td>2015-12-03 00:07:09+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Opal. He's a Royal John Coctostan. Rea...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>OPAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672160042234327040</th>\n",
       "      <td>2015-12-02 21:06:56+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Bubba. He's a Titted Peebles Aorta. Ev...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>BUBBA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669993076832759809</th>\n",
       "      <td>2015-11-26 21:36:12+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Zoey. Her dreams of becoming a hippo b...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>ZOEY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>399 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   timestamp  \\\n",
       "tweet_id                                       \n",
       "890240255349198849 2017-07-26 15:59:51+00:00   \n",
       "889665388333682689 2017-07-25 01:55:32+00:00   \n",
       "889531135344209921 2017-07-24 17:02:04+00:00   \n",
       "886366144734445568 2017-07-15 23:25:31+00:00   \n",
       "884162670584377345 2017-07-09 21:29:42+00:00   \n",
       "...                                      ...   \n",
       "672481316919734272 2015-12-03 18:23:34+00:00   \n",
       "672254177670729728 2015-12-03 03:21:00+00:00   \n",
       "672205392827572224 2015-12-03 00:07:09+00:00   \n",
       "672160042234327040 2015-12-02 21:06:56+00:00   \n",
       "669993076832759809 2015-11-26 21:36:12+00:00   \n",
       "\n",
       "                                                               source  \\\n",
       "tweet_id                                                                \n",
       "890240255349198849  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "889665388333682689  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "889531135344209921  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "886366144734445568  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "884162670584377345  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                               ...   \n",
       "672481316919734272  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "672254177670729728  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "672205392827572224  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "672160042234327040  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "669993076832759809  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                                 text  \\\n",
       "tweet_id                                                                \n",
       "890240255349198849  This is Cassie. She is a college pup. Studying...   \n",
       "889665388333682689  Here's a puppo that seems to be on the fence a...   \n",
       "889531135344209921  This is Stuart. He's sporting his favorite fan...   \n",
       "886366144734445568  This is Roscoe. Another pupper fallen victim t...   \n",
       "884162670584377345  Meet Yogi. He doesn't have any important dog m...   \n",
       "...                                                               ...   \n",
       "672481316919734272  Say hello to Jazz. She should be on the cover ...   \n",
       "672254177670729728  This is Rolf. He's having the time of his life...   \n",
       "672205392827572224  This is Opal. He's a Royal John Coctostan. Rea...   \n",
       "672160042234327040  This is Bubba. He's a Titted Peebles Aorta. Ev...   \n",
       "669993076832759809  This is Zoey. Her dreams of becoming a hippo b...   \n",
       "\n",
       "                    rating_numerator  rating_denominator    name  \\\n",
       "tweet_id                                                           \n",
       "890240255349198849                14                  10  CASSIE   \n",
       "889665388333682689                13                  10    NONE   \n",
       "889531135344209921                13                  10  STUART   \n",
       "886366144734445568                12                  10  ROSCOE   \n",
       "884162670584377345                12                  10    YOGI   \n",
       "...                              ...                 ...     ...   \n",
       "672481316919734272                12                  10    JAZZ   \n",
       "672254177670729728                11                  10    ROLF   \n",
       "672205392827572224                 9                  10    OPAL   \n",
       "672160042234327040                 8                  10   BUBBA   \n",
       "669993076832759809                 9                  10    ZOEY   \n",
       "\n",
       "                    retweet_count  favorite_count dogtionary  \n",
       "tweet_id                                                      \n",
       "890240255349198849         6428.0         29052.0      doggo  \n",
       "889665388333682689         8795.0         43747.0      puppo  \n",
       "889531135344209921         1985.0         13853.0      puppo  \n",
       "886366144734445568         2781.0         19266.0     pupper  \n",
       "884162670584377345         2631.0         18613.0      doggo  \n",
       "...                           ...             ...        ...  \n",
       "672481316919734272            NaN             NaN     pupper  \n",
       "672254177670729728            NaN             NaN     pupper  \n",
       "672205392827572224            NaN             NaN     pupper  \n",
       "672160042234327040            NaN             NaN     pupper  \n",
       "669993076832759809            NaN             NaN     pupper  \n",
       "\n",
       "[399 rows x 9 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive[twitter_archive['dogtionary'].notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"dogtionary\" columns are now one column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write Cleaned Data to CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cleaned dataset will be written to a CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive.to_csv('twitter_archive_master.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will visualize the cleaned dataset and examine the following:\n",
    "1. How did the monthly number of tweets on WeRateDogs change over time?\n",
    "2. What is the relationship between retweets and favorites?\n",
    "3. How did retweet and favorite counts vary by dog breed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.3.3-cp39-cp39-win_amd64.whl (8.5 MB)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\bsandham001\\anaconda3\\envs\\dlp\\lib\\site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\bsandham001\\anaconda3\\envs\\dlp\\lib\\site-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\bsandham001\\anaconda3\\envs\\dlp\\lib\\site-packages (from matplotlib) (1.19.2)\n",
      "Collecting cycler>=0.10\n",
      "  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
      "Requirement already satisfied: six in c:\\users\\bsandham001\\anaconda3\\envs\\dlp\\lib\\site-packages (from cycler>=0.10->matplotlib) (1.15.0)\n",
      "Collecting kiwisolver>=1.0.1\n",
      "  Downloading kiwisolver-1.3.1-cp39-cp39-win_amd64.whl (51 kB)\n",
      "Collecting pillow>=6.2.0\n",
      "  Downloading Pillow-8.1.0-cp39-cp39-win_amd64.whl (2.2 MB)\n",
      "Installing collected packages: pillow, kiwisolver, cycler, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.3.1 matplotlib-3.3.3 pillow-8.1.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2356 entries, 2017-08-01 16:23:56+00:00 to 2015-11-15 22:32:08+00:00\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   tweet_id            2356 non-null   int64  \n",
      " 1   source              2356 non-null   object \n",
      " 2   text                2356 non-null   object \n",
      " 3   rating_numerator    2356 non-null   int64  \n",
      " 4   rating_denominator  2356 non-null   int64  \n",
      " 5   name                2286 non-null   object \n",
      " 6   retweet_count       877 non-null    float64\n",
      " 7   favorite_count      877 non-null    float64\n",
      " 8   dogtionary          399 non-null    object \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 184.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# import archive master as df\n",
    "df = pd.read_csv('twitter_archive_master.csv')\n",
    "df['timestamp'] = pd.to_datetime(df['timestamp'])\n",
    "df.set_index('timestamp', inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tweets Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>monthly_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-30 00:00:00+00:00</th>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 00:00:00+00:00</th>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31 00:00:00+00:00</th>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29 00:00:00+00:00</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31 00:00:00+00:00</th>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           monthly_count\n",
       "timestamp                               \n",
       "2015-11-30 00:00:00+00:00            302\n",
       "2015-12-31 00:00:00+00:00            388\n",
       "2016-01-31 00:00:00+00:00            194\n",
       "2016-02-29 00:00:00+00:00            125\n",
       "2016-03-31 00:00:00+00:00            137"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create copy of df\n",
    "monthly_tweets = df.groupby(pd.Grouper(level='timestamp', freq=\"M\")).count().reset_index()\n",
    "monthly_tweets = monthly_tweets[['timestamp','tweet_id']]\n",
    "monthly_tweets.rename(columns={'tweet_id':'monthly_count'},inplace=True)\n",
    "monthly_tweets.set_index('timestamp',inplace=True)\n",
    "monthly_tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot tweets by month\n",
    "monthly_tweets.plot(figsize=(10,8), \n",
    "                   title='@WeRateDogs Tweet Count by Month',\n",
    "                   xlabel='Date',\n",
    "                   ylabel='Count',\n",
    "                   legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_34a30_row21_col0{\n",
       "            background-color:  yellow;\n",
       "        }</style><table id=\"T_34a30_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >monthly_count</th>    </tr>    <tr>        <th class=\"index_name level0\" >timestamp</th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_34a30_level0_row0\" class=\"row_heading level0 row0\" >2015-11-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row0_col0\" class=\"data row0 col0\" >nan%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row1\" class=\"row_heading level0 row1\" >2015-12-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row1_col0\" class=\"data row1 col0\" >28.48%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row2\" class=\"row_heading level0 row2\" >2016-01-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row2_col0\" class=\"data row2 col0\" >-50.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row3\" class=\"row_heading level0 row3\" >2016-02-29 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row3_col0\" class=\"data row3 col0\" >-35.57%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row4\" class=\"row_heading level0 row4\" >2016-03-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row4_col0\" class=\"data row4 col0\" >9.60%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row5\" class=\"row_heading level0 row5\" >2016-04-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row5_col0\" class=\"data row5 col0\" >-56.20%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row6\" class=\"row_heading level0 row6\" >2016-05-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row6_col0\" class=\"data row6 col0\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row7\" class=\"row_heading level0 row7\" >2016-06-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row7_col0\" class=\"data row7 col0\" >61.67%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row8\" class=\"row_heading level0 row8\" >2016-07-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row8_col0\" class=\"data row8 col0\" >8.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row9\" class=\"row_heading level0 row9\" >2016-08-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row9_col0\" class=\"data row9 col0\" >-28.57%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row10\" class=\"row_heading level0 row10\" >2016-09-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row10_col0\" class=\"data row10 col0\" >12.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row11\" class=\"row_heading level0 row11\" >2016-10-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row11_col0\" class=\"data row11 col0\" >4.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row12\" class=\"row_heading level0 row12\" >2016-11-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row12_col0\" class=\"data row12 col0\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row13\" class=\"row_heading level0 row13\" >2016-12-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row13_col0\" class=\"data row13 col0\" >-20.45%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row14\" class=\"row_heading level0 row14\" >2017-01-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row14_col0\" class=\"data row14 col0\" >34.29%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row15\" class=\"row_heading level0 row15\" >2017-02-28 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row15_col0\" class=\"data row15 col0\" >-6.38%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row16\" class=\"row_heading level0 row16\" >2017-03-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row16_col0\" class=\"data row16 col0\" >-22.73%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row17\" class=\"row_heading level0 row17\" >2017-04-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row17_col0\" class=\"data row17 col0\" >-11.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row18\" class=\"row_heading level0 row18\" >2017-05-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row18_col0\" class=\"data row18 col0\" >-6.67%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row19\" class=\"row_heading level0 row19\" >2017-06-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row19_col0\" class=\"data row19 col0\" >5.36%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row20\" class=\"row_heading level0 row20\" >2017-07-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row20_col0\" class=\"data row20 col0\" >-5.08%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_34a30_level0_row21\" class=\"row_heading level0 row21\" >2017-08-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_34a30_row21_col0\" class=\"data row21 col0\" >-96.43%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x120aa8f23d0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pct_changes = monthly_tweets.pct_change()\n",
    "pct_changes.style.format(\"{:.2%}\").highlight_min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over the course of the period, the tweet count declined significantly, this will be further examined in the act_report."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retweet and Favorites by Dog Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Groupby name and select records with RTs and favorite count information\n",
    "popular_names = df[df[['retweet_count','favorite_count']].isna().any(1) == False] # select records where RTs and favorites not empty\n",
    "popular_names = popular_names.groupby('name')[['retweet_count','favorite_count']].sum() # group by name\n",
    "popular_names = popular_names.drop('NONE') # remove records with no dog name\n",
    "# popular_names.sort_values(['retweet_count','favorite_count'], inplace=True, ascending=False) # sort values\n",
    "# popular_names = popular_names.iloc[:20,:]\n",
    "# popular_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y=0.370242x+-816.319020\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = popular_names.plot.scatter(x='retweet_count', \n",
    "                                y='favorite_count',\n",
    "                                title='Favorites and Retweets by Dog Name',\n",
    "                                xlabel='Favorites',\n",
    "                                ylabel='Retweets',\n",
    "                                xlim=(0,120000),\n",
    "                               ylim=(0,200000),\n",
    "                               figsize=(10,8))\n",
    "\n",
    "ax.ticklabel_format(style='plain') # change scientific formatting to plain\n",
    "ax.get_yaxis().set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}')) # format y-axis labels\n",
    "ax.get_xaxis().set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}')) # format x-axis labels\n",
    "\n",
    "RTs = popular_names['retweet_count']\n",
    "fav = popular_names['favorite_count']\n",
    "plt.plot(fav,RTs,'o')\n",
    "\n",
    "# calc the trendline\n",
    "z = numpy.polyfit(fav, RTs, 1)\n",
    "p = numpy.poly1d(z)\n",
    "plt.plot(fav,p(fav),\"r--\")\n",
    "# the line equation:\n",
    "print(\"y={:.6f}x+{:.6f}\".format(z[0],z[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a positive relationship between the number of favorites and retweets."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "DLP (venv)",
   "language": "python",
   "name": "dlp"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}