wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrangle & Analyze Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shakhawat Hassan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import tweepy\n",
    "from tweepy import OAuthHandler\n",
    "import json\n",
    "from timeit import default_timer as timer\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import urllib.request\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gather Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read twitter-archieve.csv\n",
    "df1 = pd.read_csv('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read image-predictions.tsv\n",
    "df2 = pd.read_csv('image-predictions.tsv', sep = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read jsoon file\n",
    "df3 = pd.read_json('tweet-json.txt', lines =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Query Twitter API for each tweet in the Twitter archive and save JSON in a text file\n",
    "# These are hidden to comply with Twitter's API terms and conditions\n",
    "consumer_key = 'HIDDEN'\n",
    "consumer_secret = 'HIDDEN'\n",
    "access_token = 'HIDDEN'\n",
    "access_secret = 'HIDDEN'\n",
    "\n",
    "auth = OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "api = tweepy.API(auth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assess Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      "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": [
    "df1.info() #inspecting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "df2.info() #inspectiing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
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       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
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       "      <td>Shetland_sheepdog</td>\n",
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       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
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       "      <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",
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       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
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       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
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       "      <td>bloodhound</td>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
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       "    <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",
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      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspecting data\n",
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 31 columns):\n",
      "contributors                     0 non-null float64\n",
      "coordinates                      0 non-null float64\n",
      "created_at                       2354 non-null datetime64[ns]\n",
      "display_text_range               2354 non-null object\n",
      "entities                         2354 non-null object\n",
      "extended_entities                2073 non-null object\n",
      "favorite_count                   2354 non-null int64\n",
      "favorited                        2354 non-null bool\n",
      "full_text                        2354 non-null object\n",
      "geo                              0 non-null float64\n",
      "id                               2354 non-null int64\n",
      "id_str                           2354 non-null int64\n",
      "in_reply_to_screen_name          78 non-null object\n",
      "in_reply_to_status_id            78 non-null float64\n",
      "in_reply_to_status_id_str        78 non-null float64\n",
      "in_reply_to_user_id              78 non-null float64\n",
      "in_reply_to_user_id_str          78 non-null float64\n",
      "is_quote_status                  2354 non-null bool\n",
      "lang                             2354 non-null object\n",
      "place                            1 non-null object\n",
      "possibly_sensitive               2211 non-null float64\n",
      "possibly_sensitive_appealable    2211 non-null float64\n",
      "quoted_status                    28 non-null object\n",
      "quoted_status_id                 29 non-null float64\n",
      "quoted_status_id_str             29 non-null float64\n",
      "retweet_count                    2354 non-null int64\n",
      "retweeted                        2354 non-null bool\n",
      "retweeted_status                 179 non-null object\n",
      "source                           2354 non-null object\n",
      "truncated                        2354 non-null bool\n",
      "user                             2354 non-null object\n",
      "dtypes: bool(4), datetime64[ns](1), float64(11), int64(4), object(11)\n",
      "memory usage: 505.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df3.info() #inspecting df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <td>33819</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6514</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03</td>\n",
       "      <td>[0, 121]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 891815175371796480, 'id_str'...</td>\n",
       "      <td>25461</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4328</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51</td>\n",
       "      <td>[0, 79]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 891689552724799489, 'id_str'...</td>\n",
       "      <td>42908</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8964</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24</td>\n",
       "      <td>[0, 138]</td>\n",
       "      <td>{'hashtags': [{'text': 'BarkWeek', 'indices': ...</td>\n",
       "      <td>{'media': [{'id': 891327551943041024, 'id_str'...</td>\n",
       "      <td>41048</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9774</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   contributors  coordinates          created_at display_text_range  \\\n",
       "0           NaN          NaN 2017-08-01 16:23:56            [0, 85]   \n",
       "1           NaN          NaN 2017-08-01 00:17:27           [0, 138]   \n",
       "2           NaN          NaN 2017-07-31 00:18:03           [0, 121]   \n",
       "3           NaN          NaN 2017-07-30 15:58:51            [0, 79]   \n",
       "4           NaN          NaN 2017-07-29 16:00:24           [0, 138]   \n",
       "\n",
       "                                            entities  \\\n",
       "0  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "3  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "4  {'hashtags': [{'text': 'BarkWeek', 'indices': ...   \n",
       "\n",
       "                                   extended_entities  favorite_count  \\\n",
       "0  {'media': [{'id': 892420639486877696, 'id_str'...           39467   \n",
       "1  {'media': [{'id': 892177413194625024, 'id_str'...           33819   \n",
       "2  {'media': [{'id': 891815175371796480, 'id_str'...           25461   \n",
       "3  {'media': [{'id': 891689552724799489, 'id_str'...           42908   \n",
       "4  {'media': [{'id': 891327551943041024, 'id_str'...           41048   \n",
       "\n",
       "   favorited                                          full_text  geo  \\\n",
       "0      False  This is Phineas. He's a mystical boy. Only eve...  NaN   \n",
       "1      False  This is Tilly. She's just checking pup on you....  NaN   \n",
       "2      False  This is Archie. He is a rare Norwegian Pouncin...  NaN   \n",
       "3      False  This is Darla. She commenced a snooze mid meal...  NaN   \n",
       "4      False  This is Franklin. He would like you to stop ca...  NaN   \n",
       "\n",
       "                         ...                          \\\n",
       "0                        ...                           \n",
       "1                        ...                           \n",
       "2                        ...                           \n",
       "3                        ...                           \n",
       "4                        ...                           \n",
       "\n",
       "   possibly_sensitive_appealable  quoted_status quoted_status_id  \\\n",
       "0                            0.0            NaN              NaN   \n",
       "1                            0.0            NaN              NaN   \n",
       "2                            0.0            NaN              NaN   \n",
       "3                            0.0            NaN              NaN   \n",
       "4                            0.0            NaN              NaN   \n",
       "\n",
       "   quoted_status_id_str  retweet_count  retweeted  retweeted_status  \\\n",
       "0                   NaN           8853      False               NaN   \n",
       "1                   NaN           6514      False               NaN   \n",
       "2                   NaN           4328      False               NaN   \n",
       "3                   NaN           8964      False               NaN   \n",
       "4                   NaN           9774      False               NaN   \n",
       "\n",
       "                                              source truncated  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "\n",
       "                                                user  \n",
       "0  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "1  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "2  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "3  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "4  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>contributors</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>geo</th>\n",
       "      <th>id</th>\n",
       "      <th>id_str</th>\n",
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       "      <th>in_reply_to_status_id_str</th>\n",
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       "      <th>in_reply_to_user_id_str</th>\n",
       "      <th>possibly_sensitive</th>\n",
       "      <th>possibly_sensitive_appealable</th>\n",
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       "      <th>retweet_count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2354.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.354000e+03</td>\n",
       "      <td>2.354000e+03</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>2211.0</td>\n",
       "      <td>2211.0</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>2354.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8080.968564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.426978e+17</td>\n",
       "      <td>7.426978e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.162686e+17</td>\n",
       "      <td>8.162686e+17</td>\n",
       "      <td>3164.797366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11814.771334</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.852812e+16</td>\n",
       "      <td>6.852812e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.164161e+16</td>\n",
       "      <td>6.164161e+16</td>\n",
       "      <td>5284.770364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.721083e+17</td>\n",
       "      <td>6.721083e+17</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1415.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.783975e+17</td>\n",
       "      <td>6.783975e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.888183e+17</td>\n",
       "      <td>7.888183e+17</td>\n",
       "      <td>624.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3603.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.194596e+17</td>\n",
       "      <td>7.194596e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.340867e+17</td>\n",
       "      <td>8.340867e+17</td>\n",
       "      <td>1473.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10122.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.993058e+17</td>\n",
       "      <td>7.993058e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.664587e+17</td>\n",
       "      <td>8.664587e+17</td>\n",
       "      <td>3652.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132810.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.860534e+17</td>\n",
       "      <td>8.860534e+17</td>\n",
       "      <td>79515.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       contributors  coordinates  favorite_count  geo            id  \\\n",
       "count           0.0          0.0     2354.000000  0.0  2.354000e+03   \n",
       "mean            NaN          NaN     8080.968564  NaN  7.426978e+17   \n",
       "std             NaN          NaN    11814.771334  NaN  6.852812e+16   \n",
       "min             NaN          NaN        0.000000  NaN  6.660209e+17   \n",
       "25%             NaN          NaN     1415.000000  NaN  6.783975e+17   \n",
       "50%             NaN          NaN     3603.500000  NaN  7.194596e+17   \n",
       "75%             NaN          NaN    10122.250000  NaN  7.993058e+17   \n",
       "max             NaN          NaN   132810.000000  NaN  8.924206e+17   \n",
       "\n",
       "             id_str  in_reply_to_status_id  in_reply_to_status_id_str  \\\n",
       "count  2.354000e+03           7.800000e+01               7.800000e+01   \n",
       "mean   7.426978e+17           7.455079e+17               7.455079e+17   \n",
       "std    6.852812e+16           7.582492e+16               7.582492e+16   \n",
       "min    6.660209e+17           6.658147e+17               6.658147e+17   \n",
       "25%    6.783975e+17           6.757419e+17               6.757419e+17   \n",
       "50%    7.194596e+17           7.038708e+17               7.038708e+17   \n",
       "75%    7.993058e+17           8.257804e+17               8.257804e+17   \n",
       "max    8.924206e+17           8.862664e+17               8.862664e+17   \n",
       "\n",
       "       in_reply_to_user_id  in_reply_to_user_id_str  possibly_sensitive  \\\n",
       "count         7.800000e+01             7.800000e+01              2211.0   \n",
       "mean          2.014171e+16             2.014171e+16                 0.0   \n",
       "std           1.252797e+17             1.252797e+17                 0.0   \n",
       "min           1.185634e+07             1.185634e+07                 0.0   \n",
       "25%           3.086374e+08             3.086374e+08                 0.0   \n",
       "50%           4.196984e+09             4.196984e+09                 0.0   \n",
       "75%           4.196984e+09             4.196984e+09                 0.0   \n",
       "max           8.405479e+17             8.405479e+17                 0.0   \n",
       "\n",
       "       possibly_sensitive_appealable  quoted_status_id  quoted_status_id_str  \\\n",
       "count                         2211.0      2.900000e+01          2.900000e+01   \n",
       "mean                             0.0      8.162686e+17          8.162686e+17   \n",
       "std                              0.0      6.164161e+16          6.164161e+16   \n",
       "min                              0.0      6.721083e+17          6.721083e+17   \n",
       "25%                              0.0      7.888183e+17          7.888183e+17   \n",
       "50%                              0.0      8.340867e+17          8.340867e+17   \n",
       "75%                              0.0      8.664587e+17          8.664587e+17   \n",
       "max                              0.0      8.860534e+17          8.860534e+17   \n",
       "\n",
       "       retweet_count  \n",
       "count    2354.000000  \n",
       "mean     3164.797366  \n",
       "std      5284.770364  \n",
       "min         0.000000  \n",
       "25%       624.500000  \n",
       "50%      1473.500000  \n",
       "75%      3652.000000  \n",
       "max     79515.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17                 tweet_id\n",
       "42    in_reply_to_status_id\n",
       "44      in_reply_to_user_id\n",
       "57                   source\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_columns = pd.Series(list(df1) + list (df2)+ list(df3))\n",
    "all_columns[all_columns.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality\n",
    "- Drop unnecessary columns in df1\n",
    "- Drop unnecessary columns in df2\n",
    "- Extract the columns in df3 which are needed ('id', 'retweet_count', 'favorite_count')\n",
    "- Change 'id' name to 'tweet_id' in df3\n",
    "- Change 'tweet_id' data type to string in df1\n",
    "- Change 'tweet_id' data type to  string in df2\n",
    "- Change 'id' data type to string in df3\n",
    "- Remove \"_\" and capitalize the first letter for p1, p2, and p3 in df2\n",
    "- Change string to datetime for timestamp in df1\n",
    "- Capitalize first letter in 'names' in df1\n",
    "\n",
    "Tidiness\n",
    "- Merge 3 dogs stage into one single column\n",
    "- Merge 3 columns into one column by 'tweet_id'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop unnecessary columns in df1_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',\n",
       "       'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',\n",
       "       'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean = df1.drop(['in_reply_to_status_id', 'in_reply_to_user_id',\n",
    "       'source', 'retweeted_status_id', 'retweeted_status_user_id',\n",
    "       'retweeted_status_timestamp', 'expanded_urls'], axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'text', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop unnecessary columns in df2_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_clean = df2.drop(['jpg_url', 'img_num'], axis= 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'p1', 'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3',\n",
       "       'p3_conf', 'p3_dog'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Extract the columns in df3 which are needed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extracting columns which are needed\n",
    "df3_clean = pd.DataFrame(df3, columns = ['id', 'retweet_count', 'favorite_count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'retweet_count', 'favorite_count'], dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "change 'id' name to 'tweet_id' in df3_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_clean.rename(columns = {'id': 'tweet_id'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'retweet_count', 'favorite_count'], dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3_clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "change tweet_id data type by .astype in df1_clean, df2_clean, and df3_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean.tweet_id = df1_clean.tweet_id.astype(str)\n",
    "df2_clean.tweet_id = df2_clean.tweet_id.astype(str)\n",
    "df3_clean.tweet_id = df3_clean.tweet_id.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    892420643555336193\n",
       "1    892177421306343426\n",
       "2    891815181378084864\n",
       "3    891689557279858688\n",
       "4    891327558926688256\n",
       "Name: tweet_id, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean.tweet_id.head()\n",
    "df2_clean.tweet_id.head()\n",
    "df3_clean.tweet_id.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Remove _ and capitalize the first letter for p1, p2, and p3 in df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_clean = df2_clean.replace('_', ' ', regex = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_clean['p1'] = df2_clean['p1'].str.title()\n",
    "df2_clean['p2'] = df2_clean['p2'].str.title()\n",
    "df2_clean['p3'] = df2_clean['p3'].str.title()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</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>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>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>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>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>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>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>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>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>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>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",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>666063827256086533</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.775930</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan Mastiff</td>\n",
       "      <td>0.093718</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>0.072427</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>666071193221509120</td>\n",
       "      <td>Gordon Setter</td>\n",
       "      <td>0.503672</td>\n",
       "      <td>True</td>\n",
       "      <td>Yorkshire Terrier</td>\n",
       "      <td>0.174201</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.109454</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>666073100786774016</td>\n",
       "      <td>Walker Hound</td>\n",
       "      <td>0.260857</td>\n",
       "      <td>True</td>\n",
       "      <td>English Foxhound</td>\n",
       "      <td>0.175382</td>\n",
       "      <td>True</td>\n",
       "      <td>Ibizan Hound</td>\n",
       "      <td>0.097471</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>Pug</td>\n",
       "      <td>0.489814</td>\n",
       "      <td>True</td>\n",
       "      <td>Bull Mastiff</td>\n",
       "      <td>0.404722</td>\n",
       "      <td>True</td>\n",
       "      <td>French Bulldog</td>\n",
       "      <td>0.048960</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>666094000022159362</td>\n",
       "      <td>Bloodhound</td>\n",
       "      <td>0.195217</td>\n",
       "      <td>True</td>\n",
       "      <td>German Shepherd</td>\n",
       "      <td>0.078260</td>\n",
       "      <td>True</td>\n",
       "      <td>Malinois</td>\n",
       "      <td>0.075628</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>666099513787052032</td>\n",
       "      <td>Lhasa</td>\n",
       "      <td>0.582330</td>\n",
       "      <td>True</td>\n",
       "      <td>Shih-Tzu</td>\n",
       "      <td>0.166192</td>\n",
       "      <td>True</td>\n",
       "      <td>Dandie Dinmont</td>\n",
       "      <td>0.089688</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>666102155909144576</td>\n",
       "      <td>English Setter</td>\n",
       "      <td>0.298617</td>\n",
       "      <td>True</td>\n",
       "      <td>Newfoundland</td>\n",
       "      <td>0.149842</td>\n",
       "      <td>True</td>\n",
       "      <td>Borzoi</td>\n",
       "      <td>0.133649</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>Hen</td>\n",
       "      <td>0.965932</td>\n",
       "      <td>False</td>\n",
       "      <td>Cock</td>\n",
       "      <td>0.033919</td>\n",
       "      <td>False</td>\n",
       "      <td>Partridge</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>666268910803644416</td>\n",
       "      <td>Desktop Computer</td>\n",
       "      <td>0.086502</td>\n",
       "      <td>False</td>\n",
       "      <td>Desk</td>\n",
       "      <td>0.085547</td>\n",
       "      <td>False</td>\n",
       "      <td>Bookcase</td>\n",
       "      <td>0.079480</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>666273097616637952</td>\n",
       "      <td>Italian Greyhound</td>\n",
       "      <td>0.176053</td>\n",
       "      <td>True</td>\n",
       "      <td>Toy Terrier</td>\n",
       "      <td>0.111884</td>\n",
       "      <td>True</td>\n",
       "      <td>Basenji</td>\n",
       "      <td>0.111152</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>Maltese Dog</td>\n",
       "      <td>0.857531</td>\n",
       "      <td>True</td>\n",
       "      <td>Toy Poodle</td>\n",
       "      <td>0.063064</td>\n",
       "      <td>True</td>\n",
       "      <td>Miniature Poodle</td>\n",
       "      <td>0.025581</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>Three-Toed Sloth</td>\n",
       "      <td>0.914671</td>\n",
       "      <td>False</td>\n",
       "      <td>Otter</td>\n",
       "      <td>0.015250</td>\n",
       "      <td>False</td>\n",
       "      <td>Great Grey Owl</td>\n",
       "      <td>0.013207</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>666337882303524864</td>\n",
       "      <td>Ox</td>\n",
       "      <td>0.416669</td>\n",
       "      <td>False</td>\n",
       "      <td>Newfoundland</td>\n",
       "      <td>0.278407</td>\n",
       "      <td>True</td>\n",
       "      <td>Groenendael</td>\n",
       "      <td>0.102643</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>666345417576210432</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.858744</td>\n",
       "      <td>True</td>\n",
       "      <td>Chesapeake Bay Retriever</td>\n",
       "      <td>0.054787</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>0.014241</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>666353288456101888</td>\n",
       "      <td>Malamute</td>\n",
       "      <td>0.336874</td>\n",
       "      <td>True</td>\n",
       "      <td>Siberian Husky</td>\n",
       "      <td>0.147655</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo Dog</td>\n",
       "      <td>0.093412</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>666362758909284353</td>\n",
       "      <td>Guinea Pig</td>\n",
       "      <td>0.996496</td>\n",
       "      <td>False</td>\n",
       "      <td>Skunk</td>\n",
       "      <td>0.002402</td>\n",
       "      <td>False</td>\n",
       "      <td>Hamster</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>666373753744588802</td>\n",
       "      <td>Soft-Coated Wheaten Terrier</td>\n",
       "      <td>0.326467</td>\n",
       "      <td>True</td>\n",
       "      <td>Afghan Hound</td>\n",
       "      <td>0.259551</td>\n",
       "      <td>True</td>\n",
       "      <td>Briard</td>\n",
       "      <td>0.206803</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>666396247373291520</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.978108</td>\n",
       "      <td>True</td>\n",
       "      <td>Toy Terrier</td>\n",
       "      <td>0.009397</td>\n",
       "      <td>True</td>\n",
       "      <td>Papillon</td>\n",
       "      <td>0.004577</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>666407126856765440</td>\n",
       "      <td>Black-And-Tan Coonhound</td>\n",
       "      <td>0.529139</td>\n",
       "      <td>True</td>\n",
       "      <td>Bloodhound</td>\n",
       "      <td>0.244220</td>\n",
       "      <td>True</td>\n",
       "      <td>Flat-Coated Retriever</td>\n",
       "      <td>0.173810</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>666411507551481857</td>\n",
       "      <td>Coho</td>\n",
       "      <td>0.404640</td>\n",
       "      <td>False</td>\n",
       "      <td>Barracouta</td>\n",
       "      <td>0.271485</td>\n",
       "      <td>False</td>\n",
       "      <td>Gar</td>\n",
       "      <td>0.189945</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>667192066997374976</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.283640</td>\n",
       "      <td>True</td>\n",
       "      <td>Miniature Pinscher</td>\n",
       "      <td>0.148112</td>\n",
       "      <td>True</td>\n",
       "      <td>Black-And-Tan Coonhound</td>\n",
       "      <td>0.095585</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>667200525029539841</td>\n",
       "      <td>Siberian Husky</td>\n",
       "      <td>0.694904</td>\n",
       "      <td>True</td>\n",
       "      <td>Malamute</td>\n",
       "      <td>0.232006</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo Dog</td>\n",
       "      <td>0.050635</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>667211855547486208</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.462556</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>0.454937</td>\n",
       "      <td>True</td>\n",
       "      <td>Kuvasz</td>\n",
       "      <td>0.024193</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>667369227918143488</td>\n",
       "      <td>Teddy</td>\n",
       "      <td>0.709545</td>\n",
       "      <td>False</td>\n",
       "      <td>Bath Towel</td>\n",
       "      <td>0.127285</td>\n",
       "      <td>False</td>\n",
       "      <td>Christmas Stocking</td>\n",
       "      <td>0.028568</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>667393430834667520</td>\n",
       "      <td>Papillon</td>\n",
       "      <td>0.557009</td>\n",
       "      <td>True</td>\n",
       "      <td>Border Collie</td>\n",
       "      <td>0.271963</td>\n",
       "      <td>True</td>\n",
       "      <td>Collie</td>\n",
       "      <td>0.073473</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>667405339315146752</td>\n",
       "      <td>Saint Bernard</td>\n",
       "      <td>0.381377</td>\n",
       "      <td>True</td>\n",
       "      <td>Leonberg</td>\n",
       "      <td>0.127998</td>\n",
       "      <td>True</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.069357</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>667435689202614272</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.999091</td>\n",
       "      <td>True</td>\n",
       "      <td>Miniature Pinscher</td>\n",
       "      <td>0.000450</td>\n",
       "      <td>True</td>\n",
       "      <td>Black-And-Tan Coonhound</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>667437278097252352</td>\n",
       "      <td>Porcupine</td>\n",
       "      <td>0.989154</td>\n",
       "      <td>False</td>\n",
       "      <td>Bath Towel</td>\n",
       "      <td>0.006300</td>\n",
       "      <td>False</td>\n",
       "      <td>Badger</td>\n",
       "      <td>0.000966</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>667443425659232256</td>\n",
       "      <td>Goose</td>\n",
       "      <td>0.980815</td>\n",
       "      <td>False</td>\n",
       "      <td>Drake</td>\n",
       "      <td>0.006918</td>\n",
       "      <td>False</td>\n",
       "      <td>Hen</td>\n",
       "      <td>0.005255</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>667453023279554560</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>0.825670</td>\n",
       "      <td>True</td>\n",
       "      <td>French Bulldog</td>\n",
       "      <td>0.056639</td>\n",
       "      <td>True</td>\n",
       "      <td>Staffordshire Bullterrier</td>\n",
       "      <td>0.054018</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>667455448082227200</td>\n",
       "      <td>Tibetan Terrier</td>\n",
       "      <td>0.676376</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish Terrier</td>\n",
       "      <td>0.054933</td>\n",
       "      <td>True</td>\n",
       "      <td>Yorkshire Terrier</td>\n",
       "      <td>0.040576</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>667470559035432960</td>\n",
       "      <td>Toy Poodle</td>\n",
       "      <td>0.304175</td>\n",
       "      <td>True</td>\n",
       "      <td>Pug</td>\n",
       "      <td>0.223427</td>\n",
       "      <td>True</td>\n",
       "      <td>Lakeland Terrier</td>\n",
       "      <td>0.073316</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>667491009379606528</td>\n",
       "      <td>Borzoi</td>\n",
       "      <td>0.852088</td>\n",
       "      <td>True</td>\n",
       "      <td>Ice Bear</td>\n",
       "      <td>0.132264</td>\n",
       "      <td>False</td>\n",
       "      <td>Weasel</td>\n",
       "      <td>0.005730</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>667495797102141441</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.143957</td>\n",
       "      <td>True</td>\n",
       "      <td>Christmas Stocking</td>\n",
       "      <td>0.118651</td>\n",
       "      <td>False</td>\n",
       "      <td>Ski Mask</td>\n",
       "      <td>0.092482</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>667502640335572993</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>0.996709</td>\n",
       "      <td>True</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.001688</td>\n",
       "      <td>True</td>\n",
       "      <td>Beagle</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>667509364010450944</td>\n",
       "      <td>Beagle</td>\n",
       "      <td>0.636169</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>0.119256</td>\n",
       "      <td>True</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.082549</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>667517642048163840</td>\n",
       "      <td>Italian Greyhound</td>\n",
       "      <td>0.125176</td>\n",
       "      <td>True</td>\n",
       "      <td>Standard Poodle</td>\n",
       "      <td>0.084572</td>\n",
       "      <td>True</td>\n",
       "      <td>Cocker Spaniel</td>\n",
       "      <td>0.081347</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>667524857454854144</td>\n",
       "      <td>Hare</td>\n",
       "      <td>0.447893</td>\n",
       "      <td>False</td>\n",
       "      <td>Dhole</td>\n",
       "      <td>0.092435</td>\n",
       "      <td>False</td>\n",
       "      <td>Chesapeake Bay Retriever</td>\n",
       "      <td>0.088122</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>667530908589760512</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.633037</td>\n",
       "      <td>True</td>\n",
       "      <td>Kuvasz</td>\n",
       "      <td>0.146391</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>0.046184</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>667534815156183040</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.435254</td>\n",
       "      <td>True</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.307407</td>\n",
       "      <td>True</td>\n",
       "      <td>Cocker Spaniel</td>\n",
       "      <td>0.033158</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>667538891197542400</td>\n",
       "      <td>Yorkshire Terrier</td>\n",
       "      <td>0.618957</td>\n",
       "      <td>True</td>\n",
       "      <td>Silky Terrier</td>\n",
       "      <td>0.300313</td>\n",
       "      <td>True</td>\n",
       "      <td>Australian Terrier</td>\n",
       "      <td>0.053412</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>667544320556335104</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.412893</td>\n",
       "      <td>True</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.312958</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.071960</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>667546741521195010</td>\n",
       "      <td>Toy Poodle</td>\n",
       "      <td>0.787424</td>\n",
       "      <td>True</td>\n",
       "      <td>Miniature Poodle</td>\n",
       "      <td>0.202225</td>\n",
       "      <td>True</td>\n",
       "      <td>Teddy</td>\n",
       "      <td>0.004047</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>667549055577362432</td>\n",
       "      <td>Electric Fan</td>\n",
       "      <td>0.984377</td>\n",
       "      <td>False</td>\n",
       "      <td>Spotlight</td>\n",
       "      <td>0.007737</td>\n",
       "      <td>False</td>\n",
       "      <td>Lampshade</td>\n",
       "      <td>0.001901</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>667550882905632768</td>\n",
       "      <td>Web Site</td>\n",
       "      <td>0.998258</td>\n",
       "      <td>False</td>\n",
       "      <td>Dishwasher</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>False</td>\n",
       "      <td>Oscilloscope</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>667550904950915073</td>\n",
       "      <td>Web Site</td>\n",
       "      <td>0.999335</td>\n",
       "      <td>False</td>\n",
       "      <td>Vizsla</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>True</td>\n",
       "      <td>Collie</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>667724302356258817</td>\n",
       "      <td>Ibex</td>\n",
       "      <td>0.619098</td>\n",
       "      <td>False</td>\n",
       "      <td>Bighorn</td>\n",
       "      <td>0.125119</td>\n",
       "      <td>False</td>\n",
       "      <td>Ram</td>\n",
       "      <td>0.074673</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>667728196545200128</td>\n",
       "      <td>Kuvasz</td>\n",
       "      <td>0.360159</td>\n",
       "      <td>True</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.293744</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>0.270673</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>667766675769573376</td>\n",
       "      <td>Fire Engine</td>\n",
       "      <td>0.883493</td>\n",
       "      <td>False</td>\n",
       "      <td>Tow Truck</td>\n",
       "      <td>0.074734</td>\n",
       "      <td>False</td>\n",
       "      <td>Jeep</td>\n",
       "      <td>0.012773</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>667773195014021121</td>\n",
       "      <td>West Highland White Terrier</td>\n",
       "      <td>0.360465</td>\n",
       "      <td>True</td>\n",
       "      <td>Pug</td>\n",
       "      <td>0.093494</td>\n",
       "      <td>True</td>\n",
       "      <td>Ice Bear</td>\n",
       "      <td>0.069038</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id                           p1   p1_conf  p1_dog  \\\n",
       "0   666020888022790149       Welsh Springer Spaniel  0.465074    True   \n",
       "1   666029285002620928                      Redbone  0.506826    True   \n",
       "2   666033412701032449              German Shepherd  0.596461    True   \n",
       "3   666044226329800704          Rhodesian Ridgeback  0.408143    True   \n",
       "4   666049248165822465           Miniature Pinscher  0.560311    True   \n",
       "5   666050758794694657         Bernese Mountain Dog  0.651137    True   \n",
       "6   666051853826850816                   Box Turtle  0.933012   False   \n",
       "7   666055525042405380                         Chow  0.692517    True   \n",
       "8   666057090499244032                Shopping Cart  0.962465   False   \n",
       "9   666058600524156928             Miniature Poodle  0.201493    True   \n",
       "10  666063827256086533             Golden Retriever  0.775930    True   \n",
       "11  666071193221509120                Gordon Setter  0.503672    True   \n",
       "12  666073100786774016                 Walker Hound  0.260857    True   \n",
       "13  666082916733198337                          Pug  0.489814    True   \n",
       "14  666094000022159362                   Bloodhound  0.195217    True   \n",
       "15  666099513787052032                        Lhasa  0.582330    True   \n",
       "16  666102155909144576               English Setter  0.298617    True   \n",
       "17  666104133288665088                          Hen  0.965932   False   \n",
       "18  666268910803644416             Desktop Computer  0.086502   False   \n",
       "19  666273097616637952            Italian Greyhound  0.176053    True   \n",
       "20  666287406224695296                  Maltese Dog  0.857531    True   \n",
       "21  666293911632134144             Three-Toed Sloth  0.914671   False   \n",
       "22  666337882303524864                           Ox  0.416669   False   \n",
       "23  666345417576210432             Golden Retriever  0.858744    True   \n",
       "24  666353288456101888                     Malamute  0.336874    True   \n",
       "25  666362758909284353                   Guinea Pig  0.996496   False   \n",
       "26  666373753744588802  Soft-Coated Wheaten Terrier  0.326467    True   \n",
       "27  666396247373291520                    Chihuahua  0.978108    True   \n",
       "28  666407126856765440      Black-And-Tan Coonhound  0.529139    True   \n",
       "29  666411507551481857                         Coho  0.404640   False   \n",
       "..                 ...                          ...       ...     ...   \n",
       "70  667192066997374976                   Rottweiler  0.283640    True   \n",
       "71  667200525029539841               Siberian Husky  0.694904    True   \n",
       "72  667211855547486208             Golden Retriever  0.462556    True   \n",
       "73  667369227918143488                        Teddy  0.709545   False   \n",
       "74  667393430834667520                     Papillon  0.557009    True   \n",
       "75  667405339315146752                Saint Bernard  0.381377    True   \n",
       "76  667435689202614272                   Rottweiler  0.999091    True   \n",
       "77  667437278097252352                    Porcupine  0.989154   False   \n",
       "78  667443425659232256                        Goose  0.980815   False   \n",
       "79  667453023279554560           Labrador Retriever  0.825670    True   \n",
       "80  667455448082227200              Tibetan Terrier  0.676376    True   \n",
       "81  667470559035432960                   Toy Poodle  0.304175    True   \n",
       "82  667491009379606528                       Borzoi  0.852088    True   \n",
       "83  667495797102141441                    Chihuahua  0.143957    True   \n",
       "84  667502640335572993           Labrador Retriever  0.996709    True   \n",
       "85  667509364010450944                       Beagle  0.636169    True   \n",
       "86  667517642048163840            Italian Greyhound  0.125176    True   \n",
       "87  667524857454854144                         Hare  0.447893   False   \n",
       "88  667530908589760512             Golden Retriever  0.633037    True   \n",
       "89  667534815156183040                     Pembroke  0.435254    True   \n",
       "90  667538891197542400            Yorkshire Terrier  0.618957    True   \n",
       "91  667544320556335104                   Pomeranian  0.412893    True   \n",
       "92  667546741521195010                   Toy Poodle  0.787424    True   \n",
       "93  667549055577362432                 Electric Fan  0.984377   False   \n",
       "94  667550882905632768                     Web Site  0.998258   False   \n",
       "95  667550904950915073                     Web Site  0.999335   False   \n",
       "96  667724302356258817                         Ibex  0.619098   False   \n",
       "97  667728196545200128                       Kuvasz  0.360159    True   \n",
       "98  667766675769573376                  Fire Engine  0.883493   False   \n",
       "99  667773195014021121  West Highland White Terrier  0.360465    True   \n",
       "\n",
       "                          p2   p2_conf  p2_dog                           p3  \\\n",
       "0                     Collie  0.156665    True            Shetland Sheepdog   \n",
       "1         Miniature Pinscher  0.074192    True          Rhodesian Ridgeback   \n",
       "2                   Malinois  0.138584    True                   Bloodhound   \n",
       "3                    Redbone  0.360687    True           Miniature Pinscher   \n",
       "4                 Rottweiler  0.243682    True                     Doberman   \n",
       "5           English Springer  0.263788    True   Greater Swiss Mountain Dog   \n",
       "6                 Mud Turtle  0.045885   False                     Terrapin   \n",
       "7            Tibetan Mastiff  0.058279    True                     Fur Coat   \n",
       "8            Shopping Basket  0.014594   False             Golden Retriever   \n",
       "9                   Komondor  0.192305    True  Soft-Coated Wheaten Terrier   \n",
       "10           Tibetan Mastiff  0.093718    True           Labrador Retriever   \n",
       "11         Yorkshire Terrier  0.174201    True                     Pekinese   \n",
       "12          English Foxhound  0.175382    True                 Ibizan Hound   \n",
       "13              Bull Mastiff  0.404722    True               French Bulldog   \n",
       "14           German Shepherd  0.078260    True                     Malinois   \n",
       "15                  Shih-Tzu  0.166192    True               Dandie Dinmont   \n",
       "16              Newfoundland  0.149842    True                       Borzoi   \n",
       "17                      Cock  0.033919   False                    Partridge   \n",
       "18                      Desk  0.085547   False                     Bookcase   \n",
       "19               Toy Terrier  0.111884    True                      Basenji   \n",
       "20                Toy Poodle  0.063064    True             Miniature Poodle   \n",
       "21                     Otter  0.015250   False               Great Grey Owl   \n",
       "22              Newfoundland  0.278407    True                  Groenendael   \n",
       "23  Chesapeake Bay Retriever  0.054787    True           Labrador Retriever   \n",
       "24            Siberian Husky  0.147655    True                   Eskimo Dog   \n",
       "25                     Skunk  0.002402   False                      Hamster   \n",
       "26              Afghan Hound  0.259551    True                       Briard   \n",
       "27               Toy Terrier  0.009397    True                     Papillon   \n",
       "28                Bloodhound  0.244220    True        Flat-Coated Retriever   \n",
       "29                Barracouta  0.271485   False                          Gar   \n",
       "..                       ...       ...     ...                          ...   \n",
       "70        Miniature Pinscher  0.148112    True      Black-And-Tan Coonhound   \n",
       "71                  Malamute  0.232006    True                   Eskimo Dog   \n",
       "72        Labrador Retriever  0.454937    True                       Kuvasz   \n",
       "73                Bath Towel  0.127285   False           Christmas Stocking   \n",
       "74             Border Collie  0.271963    True                       Collie   \n",
       "75                  Leonberg  0.127998    True             Golden Retriever   \n",
       "76        Miniature Pinscher  0.000450    True      Black-And-Tan Coonhound   \n",
       "77                Bath Towel  0.006300   False                       Badger   \n",
       "78                     Drake  0.006918   False                          Hen   \n",
       "79            French Bulldog  0.056639    True    Staffordshire Bullterrier   \n",
       "80             Irish Terrier  0.054933    True            Yorkshire Terrier   \n",
       "81                       Pug  0.223427    True             Lakeland Terrier   \n",
       "82                  Ice Bear  0.132264   False                       Weasel   \n",
       "83        Christmas Stocking  0.118651   False                     Ski Mask   \n",
       "84          Golden Retriever  0.001688    True                       Beagle   \n",
       "85        Labrador Retriever  0.119256    True             Golden Retriever   \n",
       "86           Standard Poodle  0.084572    True               Cocker Spaniel   \n",
       "87                     Dhole  0.092435   False     Chesapeake Bay Retriever   \n",
       "88                    Kuvasz  0.146391    True           Labrador Retriever   \n",
       "89                  Cardigan  0.307407    True               Cocker Spaniel   \n",
       "90             Silky Terrier  0.300313    True           Australian Terrier   \n",
       "91                  Pembroke  0.312958    True                    Chihuahua   \n",
       "92          Miniature Poodle  0.202225    True                        Teddy   \n",
       "93                 Spotlight  0.007737   False                    Lampshade   \n",
       "94                Dishwasher  0.000201   False                 Oscilloscope   \n",
       "95                    Vizsla  0.000081    True                       Collie   \n",
       "96                   Bighorn  0.125119   False                          Ram   \n",
       "97          Golden Retriever  0.293744    True           Labrador Retriever   \n",
       "98                 Tow Truck  0.074734   False                         Jeep   \n",
       "99                       Pug  0.093494    True                     Ice Bear   \n",
       "\n",
       "     p3_conf  p3_dog  \n",
       "0   0.061428    True  \n",
       "1   0.072010    True  \n",
       "2   0.116197    True  \n",
       "3   0.222752    True  \n",
       "4   0.154629    True  \n",
       "5   0.016199    True  \n",
       "6   0.017885   False  \n",
       "7   0.054449   False  \n",
       "8   0.007959    True  \n",
       "9   0.082086    True  \n",
       "10  0.072427    True  \n",
       "11  0.109454    True  \n",
       "12  0.097471    True  \n",
       "13  0.048960    True  \n",
       "14  0.075628    True  \n",
       "15  0.089688    True  \n",
       "16  0.133649    True  \n",
       "17  0.000052   False  \n",
       "18  0.079480   False  \n",
       "19  0.111152    True  \n",
       "20  0.025581    True  \n",
       "21  0.013207   False  \n",
       "22  0.102643    True  \n",
       "23  0.014241    True  \n",
       "24  0.093412    True  \n",
       "25  0.000461   False  \n",
       "26  0.206803    True  \n",
       "27  0.004577    True  \n",
       "28  0.173810    True  \n",
       "29  0.189945   False  \n",
       "..       ...     ...  \n",
       "70  0.095585    True  \n",
       "71  0.050635    True  \n",
       "72  0.024193    True  \n",
       "73  0.028568   False  \n",
       "74  0.073473    True  \n",
       "75  0.069357    True  \n",
       "76  0.000157    True  \n",
       "77  0.000966   False  \n",
       "78  0.005255   False  \n",
       "79  0.054018    True  \n",
       "80  0.040576    True  \n",
       "81  0.073316    True  \n",
       "82  0.005730   False  \n",
       "83  0.092482   False  \n",
       "84  0.000712    True  \n",
       "85  0.082549    True  \n",
       "86  0.081347    True  \n",
       "87  0.088122    True  \n",
       "88  0.046184    True  \n",
       "89  0.033158    True  \n",
       "90  0.053412    True  \n",
       "91  0.071960    True  \n",
       "92  0.004047   False  \n",
       "93  0.001901   False  \n",
       "94  0.000142   False  \n",
       "95  0.000069    True  \n",
       "96  0.074673   False  \n",
       "97  0.270673    True  \n",
       "98  0.012773   False  \n",
       "99  0.069038   False  \n",
       "\n",
       "[100 rows x 10 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_clean.head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Change string to datetime for timestamp in df1_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to datetime\n",
    "df1_clean.timestamp = pd.to_datetime(df1_clean.timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 10 columns):\n",
      "tweet_id              2356 non-null object\n",
      "timestamp             2356 non-null datetime64[ns]\n",
      "text                  2356 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: datetime64[ns](1), int64(2), object(7)\n",
      "memory usage: 184.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df1_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Uppercase the first letter in 'names' and dogs' stages by .title and  in df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean['name'] = df1_clean['name'].str.title()\n",
    "df1_clean['doggo'] = df1_clean['doggo'].str.title()\n",
    "df1_clean['floofer'] = df1_clean['floofer'].str.title()\n",
    "df1_clean['pupper'] = df1_clean['pupper'].str.title()\n",
    "df1_clean['puppo'] = df1_clean['puppo'].str.title()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Phineas\n",
       "1          Tilly\n",
       "2         Archie\n",
       "3          Darla\n",
       "4       Franklin\n",
       "5           None\n",
       "6            Jax\n",
       "7           None\n",
       "8           Zoey\n",
       "9         Cassie\n",
       "10          Koda\n",
       "11         Bruno\n",
       "12          None\n",
       "13           Ted\n",
       "14        Stuart\n",
       "15        Oliver\n",
       "16           Jim\n",
       "17          Zeke\n",
       "18       Ralphus\n",
       "19        Canela\n",
       "20        Gerald\n",
       "21       Jeffrey\n",
       "22          Such\n",
       "23        Canela\n",
       "24          None\n",
       "25          None\n",
       "26          Maya\n",
       "27        Mingus\n",
       "28         Derek\n",
       "29        Roscoe\n",
       "          ...   \n",
       "2326       Quite\n",
       "2327           A\n",
       "2328        None\n",
       "2329        None\n",
       "2330        None\n",
       "2331        None\n",
       "2332        None\n",
       "2333          An\n",
       "2334           A\n",
       "2335          An\n",
       "2336        None\n",
       "2337        None\n",
       "2338        None\n",
       "2339        None\n",
       "2340        None\n",
       "2341        None\n",
       "2342        None\n",
       "2343        None\n",
       "2344        None\n",
       "2345         The\n",
       "2346         The\n",
       "2347           A\n",
       "2348           A\n",
       "2349          An\n",
       "2350           A\n",
       "2351        None\n",
       "2352           A\n",
       "2353           A\n",
       "2354           A\n",
       "2355        None\n",
       "Name: name, Length: 2356, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean['name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidiness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "- Merge all three dogs' stages into one single column and then drop the empty rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean.replace('None', np.nan, inplace = True)\n",
    "df1_clean['stage'] = df1_clean[['doggo', 'floofer', 'pupper', 'puppo']].fillna('').sum(axis=1).astype(str)\n",
    "#drop the \n",
    "df1_clean = df1_clean.drop(['doggo', 'floofer', 'pupper', 'puppo'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_clean['name'] = df1_clean['name'].str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>890240255349198849</td>\n",
       "      <td>2017-07-26 15:59:51</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>Doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>889665388333682689</td>\n",
       "      <td>2017-07-25 01:55:32</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>NaN</td>\n",
       "      <td>Puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>889531135344209921</td>\n",
       "      <td>2017-07-24 17:02:04</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>Puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>886366144734445568</td>\n",
       "      <td>2017-07-15 23:25:31</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>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>884162670584377345</td>\n",
       "      <td>2017-07-09 21:29:42</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>Doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>883360690899218434</td>\n",
       "      <td>2017-07-07 16:22:55</td>\n",
       "      <td>Meet Grizzwald. He may be the floofiest floofe...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Grizzwald</td>\n",
       "      <td>Floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>882762694511734784</td>\n",
       "      <td>2017-07-06 00:46:41</td>\n",
       "      <td>This is Gus. He's quite the cheeky pupper. Alr...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Gus</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>881536004380872706</td>\n",
       "      <td>2017-07-02 15:32:16</td>\n",
       "      <td>Here is a pupper approaching maximum borkdrive...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>878776093423087618</td>\n",
       "      <td>2017-06-25 00:45:22</td>\n",
       "      <td>This is Snoopy. He's a proud #PrideMonthPuppo....</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Snoopy</td>\n",
       "      <td>Puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>877611172832227328</td>\n",
       "      <td>2017-06-21 19:36:23</td>\n",
       "      <td>RT @rachel2195: @dog_rates the boyfriend and h...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>876838120628539392</td>\n",
       "      <td>2017-06-19 16:24:33</td>\n",
       "      <td>This is Ginger. She's having a ruff Monday. To...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Ginger</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>874296783580663808</td>\n",
       "      <td>2017-06-12 16:06:11</td>\n",
       "      <td>This is Jed. He may be the fanciest pupper in ...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Jed</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>874012996292530176</td>\n",
       "      <td>2017-06-11 21:18:31</td>\n",
       "      <td>This is Sebastian. He can't see all the colors...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Sebastian</td>\n",
       "      <td>Puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>873337748698140672</td>\n",
       "      <td>2017-06-10 00:35:19</td>\n",
       "      <td>RT @dog_rates: This is Sierra. She's one preci...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Sierra</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>873213775632977920</td>\n",
       "      <td>2017-06-09 16:22:42</td>\n",
       "      <td>This is Sierra. She's one precious pupper. Abs...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Sierra</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>872967104147763200</td>\n",
       "      <td>2017-06-09 00:02:31</td>\n",
       "      <td>Here's a very large dog. He has a date later. ...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>871762521631449091</td>\n",
       "      <td>2017-06-05 16:15:56</td>\n",
       "      <td>This is Rover. As part of pupper protocol he h...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Rover</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>871515927908634625</td>\n",
       "      <td>2017-06-04 23:56:03</td>\n",
       "      <td>This is Napolean. He's a Raggedy East Nicaragu...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Napolean</td>\n",
       "      <td>Doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>871102520638267392</td>\n",
       "      <td>2017-06-03 20:33:19</td>\n",
       "      <td>Never doubt a doggo 14/10 https://t.co/AbBLh2FZCH</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>869596645499047938</td>\n",
       "      <td>2017-05-30 16:49:31</td>\n",
       "      <td>This is Scout. He just graduated. Officially a...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Scout</td>\n",
       "      <td>Doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>867421006826221569</td>\n",
       "      <td>2017-05-24 16:44:18</td>\n",
       "      <td>This is Shikha. She just watched you drop a sk...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Shikha</td>\n",
       "      <td>Puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>866816280283807744</td>\n",
       "      <td>2017-05-23 00:41:20</td>\n",
       "      <td>RT @dog_rates: This is Jamesy. He gives a kiss...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Jamesy</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>866450705531457537</td>\n",
       "      <td>2017-05-22 00:28:40</td>\n",
       "      <td>This is Jamesy. He gives a kiss to every other...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Jamesy</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>866094527597207552</td>\n",
       "      <td>2017-05-21 00:53:21</td>\n",
       "      <td>RT @dog_rates: Here's a pupper before and afte...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>859607811541651456</td>\n",
       "      <td>2017-05-03 03:17:27</td>\n",
       "      <td>Sorry for the lack of posts today. I came home...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>858843525470990336</td>\n",
       "      <td>2017-05-01 00:40:27</td>\n",
       "      <td>I have stumbled puppon a doggo painting party....</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>855851453814013952</td>\n",
       "      <td>2017-04-22 18:31:02</td>\n",
       "      <td>Here's a puppo participating in the #ScienceMa...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DoggoPuppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>854120357044912130</td>\n",
       "      <td>2017-04-17 23:52:16</td>\n",
       "      <td>Sometimes you guys remind me just how impactfu...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>854010172552949760</td>\n",
       "      <td>2017-04-17 16:34:26</td>\n",
       "      <td>At first I thought this was a shy doggo, but i...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DoggoFloofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>851953902622658560</td>\n",
       "      <td>2017-04-12 00:23:33</td>\n",
       "      <td>RT @dog_rates: This is Astrid. She's a guide d...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Astrid</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>675113801096802304</td>\n",
       "      <td>2015-12-11 00:44:07</td>\n",
       "      <td>Meet Zuzu. He just graduated college. Astute p...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Zuzu</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>675006312288268288</td>\n",
       "      <td>2015-12-10 17:37:00</td>\n",
       "      <td>Say hello to Mollie. This pic was taken after ...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Mollie</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>674774481756377088</td>\n",
       "      <td>2015-12-10 02:15:47</td>\n",
       "      <td>This is Superpup. His head isn't proportional ...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Superpup</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>674737130913071104</td>\n",
       "      <td>2015-12-09 23:47:22</td>\n",
       "      <td>Meet Rufio. He is unaware of the pink legless ...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Rufio</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>674638615994089473</td>\n",
       "      <td>2015-12-09 17:15:54</td>\n",
       "      <td>This pupper is fed up with being tickled. 12/1...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907</th>\n",
       "      <td>674447403907457024</td>\n",
       "      <td>2015-12-09 04:36:06</td>\n",
       "      <td>This pupper just wants a belly rub. This puppe...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>674318007229923329</td>\n",
       "      <td>2015-12-08 20:01:55</td>\n",
       "      <td>This is Lennon. He's in quite the predicament....</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Lennon</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>674262580978937856</td>\n",
       "      <td>2015-12-08 16:21:41</td>\n",
       "      <td>This is Gus. He's super stoked about being an ...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Gus</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>674038233588723717</td>\n",
       "      <td>2015-12-08 01:30:12</td>\n",
       "      <td>This is Kaiya. She's an aspiring shoe model. 1...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Kaiya</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>673956914389192708</td>\n",
       "      <td>2015-12-07 20:07:04</td>\n",
       "      <td>This is one esteemed pupper. Just graduated co...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>One</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>673919437611909120</td>\n",
       "      <td>2015-12-07 17:38:09</td>\n",
       "      <td>This is Obie. He is on guard watching for evil...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Obie</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>673707060090052608</td>\n",
       "      <td>2015-12-07 03:34:14</td>\n",
       "      <td>This is Raymond. He's absolutely terrified of ...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Raymond</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>673697980713705472</td>\n",
       "      <td>2015-12-07 02:58:09</td>\n",
       "      <td>This is Pickles. She's a tiny pointy pupper. A...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Pickles</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>673656262056419329</td>\n",
       "      <td>2015-12-07 00:12:23</td>\n",
       "      <td>This is Albert AKA King Banana Peel. He's a ki...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>673612854080196609</td>\n",
       "      <td>2015-12-06 21:19:54</td>\n",
       "      <td>This is Jeffri. He's a speckled ice pupper. Ve...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Jeffri</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>673363615379013632</td>\n",
       "      <td>2015-12-06 04:49:31</td>\n",
       "      <td>This little pupper can't wait for Christmas. H...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>673342308415348736</td>\n",
       "      <td>2015-12-06 03:24:51</td>\n",
       "      <td>This is Django. He's a skilled assassin pupper...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Django</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>673295268553605120</td>\n",
       "      <td>2015-12-06 00:17:55</td>\n",
       "      <td>Meet Eve. She's a raging alcoholic 8/10 (would...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Eve</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>673148804208660480</td>\n",
       "      <td>2015-12-05 14:35:56</td>\n",
       "      <td>This is Fletcher. He's had a ruff night. No mo...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>Fletcher</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>672988786805112832</td>\n",
       "      <td>2015-12-05 04:00:04</td>\n",
       "      <td>This is Schnozz. He's had a blurred tail since...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Schnozz</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>672975131468300288</td>\n",
       "      <td>2015-12-05 03:05:49</td>\n",
       "      <td>This is Chuckles. He is one skeptical pupper. ...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Chuckles</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>672970152493887488</td>\n",
       "      <td>2015-12-05 02:46:02</td>\n",
       "      <td>This is Chet. He's having a hard time. Really ...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Chet</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>672898206762672129</td>\n",
       "      <td>2015-12-04 22:00:08</td>\n",
       "      <td>This is Cheryl AKA Queen Pupper of the Skies. ...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Cheryl</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>672622327801233409</td>\n",
       "      <td>2015-12-04 03:43:54</td>\n",
       "      <td>This lil pupper is sad because we haven't foun...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>672614745925664768</td>\n",
       "      <td>2015-12-04 03:13:46</td>\n",
       "      <td>This is Norman. Doesn't bark much. Very docile...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>Norman</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>672594978741354496</td>\n",
       "      <td>2015-12-04 01:55:13</td>\n",
       "      <td>Meet Scott. Just trying to catch his train to ...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Scott</td>\n",
       "      <td>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>672481316919734272</td>\n",
       "      <td>2015-12-03 18:23:34</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>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>672254177670729728</td>\n",
       "      <td>2015-12-03 03:21:00</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>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>672205392827572224</td>\n",
       "      <td>2015-12-03 00:07:09</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>Pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>672160042234327040</td>\n",
       "      <td>2015-12-02 21:06:56</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>Pupper</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>380 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id           timestamp  \\\n",
       "9     890240255349198849 2017-07-26 15:59:51   \n",
       "12    889665388333682689 2017-07-25 01:55:32   \n",
       "14    889531135344209921 2017-07-24 17:02:04   \n",
       "29    886366144734445568 2017-07-15 23:25:31   \n",
       "43    884162670584377345 2017-07-09 21:29:42   \n",
       "46    883360690899218434 2017-07-07 16:22:55   \n",
       "49    882762694511734784 2017-07-06 00:46:41   \n",
       "56    881536004380872706 2017-07-02 15:32:16   \n",
       "71    878776093423087618 2017-06-25 00:45:22   \n",
       "78    877611172832227328 2017-06-21 19:36:23   \n",
       "82    876838120628539392 2017-06-19 16:24:33   \n",
       "92    874296783580663808 2017-06-12 16:06:11   \n",
       "94    874012996292530176 2017-06-11 21:18:31   \n",
       "97    873337748698140672 2017-06-10 00:35:19   \n",
       "98    873213775632977920 2017-06-09 16:22:42   \n",
       "99    872967104147763200 2017-06-09 00:02:31   \n",
       "107   871762521631449091 2017-06-05 16:15:56   \n",
       "108   871515927908634625 2017-06-04 23:56:03   \n",
       "110   871102520638267392 2017-06-03 20:33:19   \n",
       "121   869596645499047938 2017-05-30 16:49:31   \n",
       "129   867421006826221569 2017-05-24 16:44:18   \n",
       "132   866816280283807744 2017-05-23 00:41:20   \n",
       "135   866450705531457537 2017-05-22 00:28:40   \n",
       "137   866094527597207552 2017-05-21 00:53:21   \n",
       "168   859607811541651456 2017-05-03 03:17:27   \n",
       "172   858843525470990336 2017-05-01 00:40:27   \n",
       "191   855851453814013952 2017-04-22 18:31:02   \n",
       "199   854120357044912130 2017-04-17 23:52:16   \n",
       "200   854010172552949760 2017-04-17 16:34:26   \n",
       "211   851953902622658560 2017-04-12 00:23:33   \n",
       "...                  ...                 ...   \n",
       "1875  675113801096802304 2015-12-11 00:44:07   \n",
       "1880  675006312288268288 2015-12-10 17:37:00   \n",
       "1889  674774481756377088 2015-12-10 02:15:47   \n",
       "1897  674737130913071104 2015-12-09 23:47:22   \n",
       "1903  674638615994089473 2015-12-09 17:15:54   \n",
       "1907  674447403907457024 2015-12-09 04:36:06   \n",
       "1915  674318007229923329 2015-12-08 20:01:55   \n",
       "1921  674262580978937856 2015-12-08 16:21:41   \n",
       "1930  674038233588723717 2015-12-08 01:30:12   \n",
       "1936  673956914389192708 2015-12-07 20:07:04   \n",
       "1937  673919437611909120 2015-12-07 17:38:09   \n",
       "1945  673707060090052608 2015-12-07 03:34:14   \n",
       "1948  673697980713705472 2015-12-07 02:58:09   \n",
       "1954  673656262056419329 2015-12-07 00:12:23   \n",
       "1956  673612854080196609 2015-12-06 21:19:54   \n",
       "1960  673363615379013632 2015-12-06 04:49:31   \n",
       "1967  673342308415348736 2015-12-06 03:24:51   \n",
       "1970  673295268553605120 2015-12-06 00:17:55   \n",
       "1974  673148804208660480 2015-12-05 14:35:56   \n",
       "1977  672988786805112832 2015-12-05 04:00:04   \n",
       "1980  672975131468300288 2015-12-05 03:05:49   \n",
       "1981  672970152493887488 2015-12-05 02:46:02   \n",
       "1985  672898206762672129 2015-12-04 22:00:08   \n",
       "1991  672622327801233409 2015-12-04 03:43:54   \n",
       "1992  672614745925664768 2015-12-04 03:13:46   \n",
       "1995  672594978741354496 2015-12-04 01:55:13   \n",
       "2002  672481316919734272 2015-12-03 18:23:34   \n",
       "2009  672254177670729728 2015-12-03 03:21:00   \n",
       "2015  672205392827572224 2015-12-03 00:07:09   \n",
       "2017  672160042234327040 2015-12-02 21:06:56   \n",
       "\n",
       "                                                   text  rating_numerator  \\\n",
       "9     This is Cassie. She is a college pup. Studying...                14   \n",
       "12    Here's a puppo that seems to be on the fence a...                13   \n",
       "14    This is Stuart. He's sporting his favorite fan...                13   \n",
       "29    This is Roscoe. Another pupper fallen victim t...                12   \n",
       "43    Meet Yogi. He doesn't have any important dog m...                12   \n",
       "46    Meet Grizzwald. He may be the floofiest floofe...                13   \n",
       "49    This is Gus. He's quite the cheeky pupper. Alr...                12   \n",
       "56    Here is a pupper approaching maximum borkdrive...                14   \n",
       "71    This is Snoopy. He's a proud #PrideMonthPuppo....                13   \n",
       "78    RT @rachel2195: @dog_rates the boyfriend and h...                14   \n",
       "82    This is Ginger. She's having a ruff Monday. To...                12   \n",
       "92    This is Jed. He may be the fanciest pupper in ...                13   \n",
       "94    This is Sebastian. He can't see all the colors...                13   \n",
       "97    RT @dog_rates: This is Sierra. She's one preci...                12   \n",
       "98    This is Sierra. She's one precious pupper. Abs...                12   \n",
       "99    Here's a very large dog. He has a date later. ...                12   \n",
       "107   This is Rover. As part of pupper protocol he h...                12   \n",
       "108   This is Napolean. He's a Raggedy East Nicaragu...                12   \n",
       "110   Never doubt a doggo 14/10 https://t.co/AbBLh2FZCH                14   \n",
       "121   This is Scout. He just graduated. Officially a...                12   \n",
       "129   This is Shikha. She just watched you drop a sk...                12   \n",
       "132   RT @dog_rates: This is Jamesy. He gives a kiss...                13   \n",
       "135   This is Jamesy. He gives a kiss to every other...                13   \n",
       "137   RT @dog_rates: Here's a pupper before and afte...                12   \n",
       "168   Sorry for the lack of posts today. I came home...                13   \n",
       "172   I have stumbled puppon a doggo painting party....                13   \n",
       "191   Here's a puppo participating in the #ScienceMa...                13   \n",
       "199   Sometimes you guys remind me just how impactfu...                14   \n",
       "200   At first I thought this was a shy doggo, but i...                11   \n",
       "211   RT @dog_rates: This is Astrid. She's a guide d...                13   \n",
       "...                                                 ...               ...   \n",
       "1875  Meet Zuzu. He just graduated college. Astute p...                10   \n",
       "1880  Say hello to Mollie. This pic was taken after ...                10   \n",
       "1889  This is Superpup. His head isn't proportional ...                11   \n",
       "1897  Meet Rufio. He is unaware of the pink legless ...                10   \n",
       "1903  This pupper is fed up with being tickled. 12/1...                12   \n",
       "1907  This pupper just wants a belly rub. This puppe...                10   \n",
       "1915  This is Lennon. He's in quite the predicament....                 8   \n",
       "1921  This is Gus. He's super stoked about being an ...                 9   \n",
       "1930  This is Kaiya. She's an aspiring shoe model. 1...                12   \n",
       "1936  This is one esteemed pupper. Just graduated co...                10   \n",
       "1937  This is Obie. He is on guard watching for evil...                11   \n",
       "1945  This is Raymond. He's absolutely terrified of ...                10   \n",
       "1948  This is Pickles. She's a tiny pointy pupper. A...                 8   \n",
       "1954  This is Albert AKA King Banana Peel. He's a ki...                10   \n",
       "1956  This is Jeffri. He's a speckled ice pupper. Ve...                 7   \n",
       "1960  This little pupper can't wait for Christmas. H...                11   \n",
       "1967  This is Django. He's a skilled assassin pupper...                10   \n",
       "1970  Meet Eve. She's a raging alcoholic 8/10 (would...                 8   \n",
       "1974  This is Fletcher. He's had a ruff night. No mo...                 8   \n",
       "1977  This is Schnozz. He's had a blurred tail since...                10   \n",
       "1980  This is Chuckles. He is one skeptical pupper. ...                10   \n",
       "1981  This is Chet. He's having a hard time. Really ...                 7   \n",
       "1985  This is Cheryl AKA Queen Pupper of the Skies. ...                11   \n",
       "1991  This lil pupper is sad because we haven't foun...                12   \n",
       "1992  This is Norman. Doesn't bark much. Very docile...                 6   \n",
       "1995  Meet Scott. Just trying to catch his train to ...                 9   \n",
       "2002  Say hello to Jazz. She should be on the cover ...                12   \n",
       "2009  This is Rolf. He's having the time of his life...                11   \n",
       "2015  This is Opal. He's a Royal John Coctostan. Rea...                 9   \n",
       "2017  This is Bubba. He's a Titted Peebles Aorta. Ev...                 8   \n",
       "\n",
       "      rating_denominator       name         stage  \n",
       "9                     10     Cassie         Doggo  \n",
       "12                    10        NaN         Puppo  \n",
       "14                    10     Stuart         Puppo  \n",
       "29                    10     Roscoe        Pupper  \n",
       "43                    10       Yogi         Doggo  \n",
       "46                    10  Grizzwald       Floofer  \n",
       "49                    10        Gus        Pupper  \n",
       "56                    10          A        Pupper  \n",
       "71                    10     Snoopy         Puppo  \n",
       "78                    10        NaN        Pupper  \n",
       "82                    10     Ginger        Pupper  \n",
       "92                    10        Jed        Pupper  \n",
       "94                    10  Sebastian         Puppo  \n",
       "97                    10     Sierra        Pupper  \n",
       "98                    10     Sierra        Pupper  \n",
       "99                    10        NaN         Doggo  \n",
       "107                   10      Rover        Pupper  \n",
       "108                   10   Napolean         Doggo  \n",
       "110                   10        NaN         Doggo  \n",
       "121                   10      Scout         Doggo  \n",
       "129                   10     Shikha         Puppo  \n",
       "132                   10     Jamesy        Pupper  \n",
       "135                   10     Jamesy        Pupper  \n",
       "137                   10        NaN        Pupper  \n",
       "168                   10        NaN         Puppo  \n",
       "172                   10        NaN         Doggo  \n",
       "191                   10        NaN    DoggoPuppo  \n",
       "199                   10        NaN        Pupper  \n",
       "200                   10        NaN  DoggoFloofer  \n",
       "211                   10     Astrid         Doggo  \n",
       "...                  ...        ...           ...  \n",
       "1875                  10       Zuzu        Pupper  \n",
       "1880                  10     Mollie        Pupper  \n",
       "1889                  10   Superpup        Pupper  \n",
       "1897                  10      Rufio        Pupper  \n",
       "1903                  10        NaN        Pupper  \n",
       "1907                  10        NaN        Pupper  \n",
       "1915                  10     Lennon        Pupper  \n",
       "1921                  10        Gus        Pupper  \n",
       "1930                  10      Kaiya        Pupper  \n",
       "1936                  10        One        Pupper  \n",
       "1937                  10       Obie        Pupper  \n",
       "1945                  10    Raymond        Pupper  \n",
       "1948                  10    Pickles        Pupper  \n",
       "1954                  10     Albert        Pupper  \n",
       "1956                  10     Jeffri        Pupper  \n",
       "1960                  10        NaN        Pupper  \n",
       "1967                  10     Django        Pupper  \n",
       "1970                  10        Eve        Pupper  \n",
       "1974                  10   Fletcher        Pupper  \n",
       "1977                  10    Schnozz        Pupper  \n",
       "1980                  10   Chuckles        Pupper  \n",
       "1981                  10       Chet        Pupper  \n",
       "1985                  10     Cheryl        Pupper  \n",
       "1991                  10        NaN        Pupper  \n",
       "1992                  10     Norman        Pupper  \n",
       "1995                  10      Scott        Pupper  \n",
       "2002                  10       Jazz        Pupper  \n",
       "2009                  10       Rolf        Pupper  \n",
       "2015                  10       Opal        Pupper  \n",
       "2017                  10      Bubba        Pupper  \n",
       "\n",
       "[380 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean['stage'].replace('', np.nan, inplace=True)\n",
    "df1_clean.dropna(subset=['name'])\n",
    "df1_clean.dropna(subset=['stage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method IndexOpsMixin.value_counts of 0          NaN\n",
       "1          NaN\n",
       "2          NaN\n",
       "3          NaN\n",
       "4          NaN\n",
       "5          NaN\n",
       "6          NaN\n",
       "7          NaN\n",
       "8          NaN\n",
       "9        Doggo\n",
       "10         NaN\n",
       "11         NaN\n",
       "12       Puppo\n",
       "13         NaN\n",
       "14       Puppo\n",
       "15         NaN\n",
       "16         NaN\n",
       "17         NaN\n",
       "18         NaN\n",
       "19         NaN\n",
       "20         NaN\n",
       "21         NaN\n",
       "22         NaN\n",
       "23         NaN\n",
       "24         NaN\n",
       "25         NaN\n",
       "26         NaN\n",
       "27         NaN\n",
       "28         NaN\n",
       "29      Pupper\n",
       "         ...  \n",
       "2326       NaN\n",
       "2327       NaN\n",
       "2328       NaN\n",
       "2329       NaN\n",
       "2330       NaN\n",
       "2331       NaN\n",
       "2332       NaN\n",
       "2333       NaN\n",
       "2334       NaN\n",
       "2335       NaN\n",
       "2336       NaN\n",
       "2337       NaN\n",
       "2338       NaN\n",
       "2339       NaN\n",
       "2340       NaN\n",
       "2341       NaN\n",
       "2342       NaN\n",
       "2343       NaN\n",
       "2344       NaN\n",
       "2345       NaN\n",
       "2346       NaN\n",
       "2347       NaN\n",
       "2348       NaN\n",
       "2349       NaN\n",
       "2350       NaN\n",
       "2351       NaN\n",
       "2352       NaN\n",
       "2353       NaN\n",
       "2354       NaN\n",
       "2355       NaN\n",
       "Name: stage, Length: 2356, dtype: object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean['stage'].value_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Replace the names 'DoggoPupper\" to \"Doggo\", 'DoggoPuppo', 'Puppo', 'DoggoFloofer', 'Floofer'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "##DoggoPupper should be doggo\n",
    "\n",
    "df1_clean.stage= df1_clean.stage.replace('DoggoPupper', 'Doggo')\n",
    "df1_clean.stage= df1_clean.stage.replace('DoggoPuppo', 'Puppo')\n",
    "df1_clean.stage= df1_clean.stage.replace('DoggoFloofer', 'Floofer')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pupper     245\n",
       "Doggo       95\n",
       "Puppo       30\n",
       "Floofer     10\n",
       "Name: stage, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean['stage'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           55\n",
       "Charlie     12\n",
       "Oliver      11\n",
       "Cooper      11\n",
       "Lucy        11\n",
       "Lola        10\n",
       "Tucker      10\n",
       "Penny       10\n",
       "Bo           9\n",
       "Winston      9\n",
       "Sadie        8\n",
       "The          8\n",
       "Bailey       7\n",
       "An           7\n",
       "Daisy        7\n",
       "Buddy        7\n",
       "Toby         7\n",
       "Jack         6\n",
       "Bella        6\n",
       "Rusty        6\n",
       "Stanley      6\n",
       "Milo         6\n",
       "Jax          6\n",
       "Koda         6\n",
       "Scout        6\n",
       "Leo          6\n",
       "Oscar        6\n",
       "Dave         6\n",
       "Chester      5\n",
       "Sammy        5\n",
       "            ..\n",
       "Ron          1\n",
       "Mya          1\n",
       "Bayley       1\n",
       "Kobe         1\n",
       "Loomis       1\n",
       "Edmund       1\n",
       "Old          1\n",
       "Tess         1\n",
       "Bubba        1\n",
       "Eriq         1\n",
       "Pupcasso     1\n",
       "Sobe         1\n",
       "Gustaf       1\n",
       "Lupe         1\n",
       "Creg         1\n",
       "Reptar       1\n",
       "Clyde        1\n",
       "Newt         1\n",
       "Ferg         1\n",
       "Billy        1\n",
       "Cilantro     1\n",
       "Remy         1\n",
       "Miguel       1\n",
       "Snoop        1\n",
       "Puff         1\n",
       "Alf          1\n",
       "Dobby        1\n",
       "Bradlay      1\n",
       "Aldrick      1\n",
       "Pancake      1\n",
       "Name: name, Length: 956, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_clean['name'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Merge all three columns into a single column by 'tweeter_id'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_n = pd.merge(df1_clean, df2_clean, on = 'tweet_id', how ='inner')\n",
    "df_new = pd.merge(df_n, df3_clean, on = 'tweet_id', how ='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'text', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'stage', 'p1', 'p1_conf', 'p1_dog', 'p2',\n",
       "       'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog', 'retweet_count',\n",
       "       'favorite_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2073 entries, 0 to 2072\n",
      "Data columns (total 18 columns):\n",
      "tweet_id              2073 non-null object\n",
      "timestamp             2073 non-null datetime64[ns]\n",
      "text                  2073 non-null object\n",
      "rating_numerator      2073 non-null int64\n",
      "rating_denominator    2073 non-null int64\n",
      "name                  1496 non-null object\n",
      "stage                 320 non-null object\n",
      "p1                    2073 non-null object\n",
      "p1_conf               2073 non-null float64\n",
      "p1_dog                2073 non-null bool\n",
      "p2                    2073 non-null object\n",
      "p2_conf               2073 non-null float64\n",
      "p2_dog                2073 non-null bool\n",
      "p3                    2073 non-null object\n",
      "p3_conf               2073 non-null float64\n",
      "p3_dog                2073 non-null bool\n",
      "retweet_count         2073 non-null int64\n",
      "favorite_count        2073 non-null int64\n",
      "dtypes: bool(3), datetime64[ns](1), float64(3), int64(4), object(7)\n",
      "memory usage: 265.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data cleaning has been done! And the data type looks fine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize and Analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2073.000000</td>\n",
       "      <td>2073.000000</td>\n",
       "      <td>2073.000000</td>\n",
       "      <td>2.073000e+03</td>\n",
       "      <td>2.073000e+03</td>\n",
       "      <td>2073.000000</td>\n",
       "      <td>2073.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.265798</td>\n",
       "      <td>10.511819</td>\n",
       "      <td>0.594532</td>\n",
       "      <td>1.346665e-01</td>\n",
       "      <td>6.034005e-02</td>\n",
       "      <td>2976.089243</td>\n",
       "      <td>8556.718283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>40.699924</td>\n",
       "      <td>7.180517</td>\n",
       "      <td>0.271234</td>\n",
       "      <td>1.006830e-01</td>\n",
       "      <td>5.092769e-02</td>\n",
       "      <td>5054.897526</td>\n",
       "      <td>12098.640994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.364095</td>\n",
       "      <td>5.390140e-02</td>\n",
       "      <td>1.619920e-02</td>\n",
       "      <td>634.000000</td>\n",
       "      <td>1674.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.588230</td>\n",
       "      <td>1.186220e-01</td>\n",
       "      <td>4.947150e-02</td>\n",
       "      <td>1408.000000</td>\n",
       "      <td>3864.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.843911</td>\n",
       "      <td>1.955730e-01</td>\n",
       "      <td>9.193000e-02</td>\n",
       "      <td>3443.000000</td>\n",
       "      <td>10937.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "      <td>79515.000000</td>\n",
       "      <td>132810.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rating_numerator  rating_denominator      p1_conf       p2_conf  \\\n",
       "count       2073.000000         2073.000000  2073.000000  2.073000e+03   \n",
       "mean          12.265798           10.511819     0.594532  1.346665e-01   \n",
       "std           40.699924            7.180517     0.271234  1.006830e-01   \n",
       "min            0.000000            2.000000     0.044333  1.011300e-08   \n",
       "25%           10.000000           10.000000     0.364095  5.390140e-02   \n",
       "50%           11.000000           10.000000     0.588230  1.186220e-01   \n",
       "75%           12.000000           10.000000     0.843911  1.955730e-01   \n",
       "max         1776.000000          170.000000     1.000000  4.880140e-01   \n",
       "\n",
       "            p3_conf  retweet_count  favorite_count  \n",
       "count  2.073000e+03    2073.000000     2073.000000  \n",
       "mean   6.034005e-02    2976.089243     8556.718283  \n",
       "std    5.092769e-02    5054.897526    12098.640994  \n",
       "min    1.740170e-10      16.000000        0.000000  \n",
       "25%    1.619920e-02     634.000000     1674.000000  \n",
       "50%    4.947150e-02    1408.000000     3864.000000  \n",
       "75%    9.193000e-02    3443.000000    10937.000000  \n",
       "max    2.734190e-01   79515.000000   132810.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- At 75 percentile, most dogs get at the scale of 12 on rating numerator.\n",
    "- At 75 percentile, most dogs get at the scale of 11 on rating denominator.\n",
    "- There are more favorite counts than retweet counts.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most Popular Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A          55\n",
       "Charlie    11\n",
       "Penny      10\n",
       "Lucy       10\n",
       "Tucker     10\n",
       "Cooper     10\n",
       "Oliver     10\n",
       "Winston     8\n",
       "Sadie       8\n",
       "Lola        8\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_names = df_new['name'].value_counts().nlargest(10)\n",
    "common_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 10 dog names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f579cafb550>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f579cae89e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "common_names.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unrecorded names with 'A' dogs' have the highest number of names among all other names."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dog Stages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pupper     210\n",
       "Doggo       78\n",
       "Puppo       24\n",
       "Floofer      8\n",
       "Name: stage, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dog_stages = df_new['stage'].value_counts()\n",
    "dog_stages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pupper stage has the highest number of dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f579cb7e828>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f579cca2e48>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dog_stages.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pupper stage has the highest number of dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2073 entries, 0 to 2072\n",
      "Data columns (total 18 columns):\n",
      "tweet_id              2073 non-null object\n",
      "timestamp             2073 non-null datetime64[ns]\n",
      "text                  2073 non-null object\n",
      "rating_numerator      2073 non-null int64\n",
      "rating_denominator    2073 non-null int64\n",
      "name                  1496 non-null object\n",
      "stage                 320 non-null object\n",
      "p1                    2073 non-null object\n",
      "p1_conf               2073 non-null float64\n",
      "p1_dog                2073 non-null bool\n",
      "p2                    2073 non-null object\n",
      "p2_conf               2073 non-null float64\n",
      "p2_dog                2073 non-null bool\n",
      "p3                    2073 non-null object\n",
      "p3_conf               2073 non-null float64\n",
      "p3_dog                2073 non-null bool\n",
      "retweet_count         2073 non-null int64\n",
      "favorite_count        2073 non-null int64\n",
      "dtypes: bool(3), datetime64[ns](1), float64(3), int64(4), object(7)\n",
      "memory usage: 265.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Favorite Tweets vs Retweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     2073.000000\n",
       "mean      2976.089243\n",
       "std       5054.897526\n",
       "min         16.000000\n",
       "25%        634.000000\n",
       "50%       1408.000000\n",
       "75%       3443.000000\n",
       "max      79515.000000\n",
       "Name: retweet_count, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['retweet_count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      2073.000000\n",
       "mean       8556.718283\n",
       "std       12098.640994\n",
       "min           0.000000\n",
       "25%        1674.000000\n",
       "50%        3864.000000\n",
       "75%       10937.000000\n",
       "max      132810.000000\n",
       "Name: favorite_count, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['favorite_count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f579caee400>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_new.plot(x = 'retweet_count', y= 'favorite_count' , kind = 'scatter', figsize= (20, 20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a more positive correlation towards the favorite count side."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Favorite tweets vs Timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f579d290668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df_new.plot(x = 'timestamp', y= 'favorite_count' , kind = 'line', figsize = (20,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As time goes on, more people are liking a tweet than retweeting that tweet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sources\n",
    "https://stackoverflow.com/questions/39662149/pandas-extract-date-and-time-from-timestamp\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.cat.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}