wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import requests\n",
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gathering Data source 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193 NaN                    NaN                    \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "\n",
       "                                                                               source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                            text  \\\n",
       "0  This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "\n",
       "   retweeted_status_id  retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0 NaN                  NaN                        NaN                         \n",
       "\n",
       "                                                     expanded_urls  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator     name doggo floofer pupper puppo  \n",
       "0  13                10                  Phineas  None  None    None   None  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DOWNLOADED MANUALLY AND UPLOADED\n",
    "archive_df=pd.read_csv('twitter-archive-enhanced.csv')\n",
    "archive_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gathering Data source 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog      p2   p2_conf  \\\n",
       "0  1        Welsh_springer_spaniel  0.465074  True    collie  0.156665   \n",
       "\n",
       "   p2_dog                 p3   p3_conf  p3_dog  \n",
       "0  True    Shetland_sheepdog  0.061428  True    "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DOWNLOADING PROGRAMATICALLY\n",
    "if not os.path.exists('downloaded_programmatically'):\n",
    "    os.makedirs('downloaded_programmatically')\n",
    "url= 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "response= requests.get(url)\n",
    "with open (os.path.join('downloaded_programmatically', url.split('/')[-1]), mode='wb') as file:\n",
    "    file.write(response.content)\n",
    "os.listdir('downloaded_programmatically')\n",
    "image_predictions_df=pd.read_csv(os.path.join('downloaded_programmatically', url.split('/')[-1]), sep='\\t')\n",
    "image_predictions_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gathering Data source 3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "from tweepy import OAuthHandler\n",
    "import json\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "# 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, wait_on_rate_limit=True)\n",
    "\n",
    "# NOTE TO STUDENT WITH MOBILE VERIFICATION ISSUES:\n",
    "# df_1 is a DataFrame with the twitter_archive_enhanced.csv file. You may have to\n",
    "# change line 17 to match the name of your DataFrame with twitter_archive_enhanced.csv\n",
    "# NOTE TO REVIEWER: this student had mobile verification issues so the following\n",
    "# Twitter API code was sent to this student from a Udacity instructor\n",
    "# Tweet IDs for which to gather additional data via Twitter's API\n",
    "tweet_ids = df_1.tweet_id.values\n",
    "len(tweet_ids)\n",
    "\n",
    "# Query Twitter's API for JSON data for each tweet ID in the Twitter archive\n",
    "count = 0\n",
    "fails_dict = {}\n",
    "start = timer()\n",
    "# Save each tweet's returned JSON as a new line in a .txt file\n",
    "with open('tweet_json.txt', 'w') as outfile:\n",
    "    # This loop will likely take 20-30 minutes to run because of Twitter's rate limit\n",
    "    for tweet_id in tweet_ids:\n",
    "        count += 1\n",
    "        print(str(count) + \": \" + str(tweet_id))\n",
    "        try:\n",
    "            tweet = api.get_status(tweet_id, tweet_mode='extended')\n",
    "            print(\"Success\")\n",
    "            json.dump(tweet._json, outfile)\n",
    "            outfile.write('\\n')\n",
    "        except tweepy.TweepError as e:\n",
    "            print(\"Fail\")\n",
    "            fails_dict[tweet_id] = e\n",
    "            pass\n",
    "end = timer()\n",
    "print(end - start)\n",
    "print(fails_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>tweet_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39467</td>\n",
       "      <td>8853</td>\n",
       "      <td>892420643555336193</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   favorite_count  retweet_count            tweet_id\n",
       "0  39467           8853           892420643555336193"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating dataframe\n",
    "df_list=[]\n",
    "with open('tweet-json.txt', encoding='utf-8') as file:\n",
    "#     print(file.readline())\n",
    "    for line in file:\n",
    "        tweet=json.loads(line)\n",
    "        tweet_id=tweet['id']\n",
    "        retweet_count=tweet['retweet_count']\n",
    "        fav_count = tweet['favorite_count']\n",
    "        df_list.append({'tweet_id': tweet_id,\n",
    "                        'retweet_count': retweet_count,\n",
    "                        'favorite_count': fav_count})\n",
    "api_df= pd.DataFrame(df_list)\n",
    "api_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## assesing data##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "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": [
    "archive_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      "tweet_id    2075 non-null 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": [
    "image_predictions_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8e8b5c7588>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e8b6296a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_predictions_df.p1_conf.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2075.000000\n",
       "mean        0.594548\n",
       "std         0.271174\n",
       "min         0.044333\n",
       "25%         0.364412\n",
       "50%         0.588230\n",
       "75%         0.843855\n",
       "max         1.000000\n",
       "Name: p1_conf, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions_df.p1_conf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Welsh_springer_spaniel', 'redbone', 'German_shepherd',\n",
       "       'Rhodesian_ridgeback', 'miniature_pinscher', 'Bernese_mountain_dog',\n",
       "       'box_turtle', 'chow', 'shopping_cart', 'miniature_poodle',\n",
       "       'golden_retriever', 'Gordon_setter', 'Walker_hound', 'pug',\n",
       "       'bloodhound', 'Lhasa', 'English_setter', 'hen', 'desktop_computer',\n",
       "       'Italian_greyhound', 'Maltese_dog', 'three-toed_sloth', 'ox',\n",
       "       'malamute', 'guinea_pig', 'soft-coated_wheaten_terrier',\n",
       "       'Chihuahua', 'black-and-tan_coonhound', 'coho', 'toy_terrier',\n",
       "       'Blenheim_spaniel', 'Pembroke', 'llama', 'Chesapeake_Bay_retriever',\n",
       "       'curly-coated_retriever', 'dalmatian', 'Ibizan_hound',\n",
       "       'Border_collie', 'Labrador_retriever', 'seat_belt', 'snail',\n",
       "       'miniature_schnauzer', 'Airedale', 'triceratops', 'swab', 'hay',\n",
       "       'hyena', 'jigsaw_puzzle', 'West_Highland_white_terrier',\n",
       "       'toy_poodle', 'giant_schnauzer', 'vizsla', 'vacuum', 'Rottweiler',\n",
       "       'Siberian_husky', 'teddy', 'papillon', 'Saint_Bernard', 'porcupine',\n",
       "       'goose', 'Tibetan_terrier', 'borzoi', 'beagle', 'hare',\n",
       "       'Yorkshire_terrier', 'Pomeranian', 'electric_fan', 'web_site',\n",
       "       'ibex', 'kuvasz', 'fire_engine', 'lorikeet',\n",
       "       'flat-coated_retriever', 'toyshop', 'common_iguana',\n",
       "       'Norwegian_elkhound', 'frilled_lizard', 'leatherback_turtle',\n",
       "       'hamster', 'Angora', 'Arctic_fox', 'trombone', 'canoe',\n",
       "       'king_penguin', 'shopping_basket', 'standard_poodle',\n",
       "       'Staffordshire_bullterrier', 'basenji', 'Lakeland_terrier',\n",
       "       'American_Staffordshire_terrier', 'bearskin', 'Shih-Tzu', 'bustard',\n",
       "       'crash_helmet', 'French_bulldog', 'Pekinese', 'komondor',\n",
       "       'ski_mask', 'malinois', 'kelpie', 'Brittany_spaniel',\n",
       "       'cocker_spaniel', 'shower_curtain', 'basset', 'jellyfish',\n",
       "       'doormat', 'Arabian_camel', 'lynx', 'hog', 'comic_book', 'minivan',\n",
       "       'seashore', 'cuirass', 'Brabancon_griffon', 'candle', 'Eskimo_dog',\n",
       "       'weasel', 'Christmas_stocking', 'washbasin', 'car_mirror',\n",
       "       'piggy_bank', 'pot', 'boathouse', 'mud_turtle',\n",
       "       'German_short-haired_pointer', 'Shetland_sheepdog', 'Irish_terrier',\n",
       "       'cairn', 'platypus', 'English_springer', 'whippet',\n",
       "       'ping-pong_ball', 'sea_urchin', 'bow_tie', 'window_shade',\n",
       "       \"jack-o'-lantern\", 'sorrel', 'Sussex_spaniel', 'peacock', 'axolotl',\n",
       "       'wool', 'banana', 'Dandie_Dinmont', 'Norwich_terrier',\n",
       "       'wood_rabbit', 'dhole', 'keeshond', 'Norfolk_terrier', 'lacewing',\n",
       "       'dingo', 'brown_bear', 'Old_English_sheepdog', 'scorpion',\n",
       "       'flamingo', 'microphone', 'Samoyed', 'pitcher',\n",
       "       'African_hunting_dog', 'refrigerator', 'picket_fence', 'tub',\n",
       "       'zebra', 'hermit_crab', 'swing', 'Doberman', 'park_bench',\n",
       "       'feather_boa', 'Loafer', 'stone_wall', 'ice_bear', 'prayer_rug',\n",
       "       'chimpanzee', 'china_cabinet', 'bee_eater', 'tennis_ball', 'carton',\n",
       "       'killer_whale', 'ostrich', 'terrapin', 'Siamese_cat', 'gondola',\n",
       "       'Great_Pyrenees', 'microwave', 'starfish', 'sandbar', 'tusker',\n",
       "       'motor_scooter', 'ram', 'leaf_beetle', 'wombat', 'schipperke',\n",
       "       'Newfoundland', 'bull_mastiff', 'water_bottle', 'suit',\n",
       "       'toilet_seat', 'collie', 'robin', 'Cardigan',\n",
       "       'Greater_Swiss_Mountain_dog', 'slug', 'toilet_tissue',\n",
       "       'acorn_squash', 'soccer_ball', 'African_crocodile', 'tick',\n",
       "       'ocarina', 'boxer', 'street_sign', 'bow', 'stove', 'paper_towel',\n",
       "       'upright', 'dough', 'Scottish_deerhound', 'bath_towel',\n",
       "       'standard_schnauzer', 'walking_stick', 'Irish_water_spaniel',\n",
       "       'bubble', 'Boston_bull', 'book_jacket', 'rain_barrel',\n",
       "       'black-footed_ferret', 'guenon', 'Japanese_spaniel',\n",
       "       'water_buffalo', 'patio', 'cowboy_hat', 'dogsled', 'maze', 'harp',\n",
       "       'panpipe', 'cash_machine', 'mailbox', 'wallaby', 'EntleBucher',\n",
       "       'earthstar', 'pillow', 'bluetick', 'space_heater', 'carousel',\n",
       "       'Irish_setter', 'birdhouse', 'snorkel', 'bald_eagle', 'koala',\n",
       "       'Leonberg', 'cheetah', 'minibus', 'Weimaraner', 'clog',\n",
       "       'dishwasher', 'white_wolf', 'sliding_door', 'damselfly',\n",
       "       'Great_Dane', 'Tibetan_mastiff', 'cheeseburger', 'fiddler_crab',\n",
       "       'bannister', 'crane', 'Scotch_terrier', 'snowmobile', 'badger',\n",
       "       'bighorn', 'geyser', 'barrow', 'bison', 'Mexican_hairless',\n",
       "       'ice_lolly', 'sea_lion', 'dining_table', 'groenendael',\n",
       "       'Australian_terrier', 'beaver', 'briard', 'Appenzeller', 'grey_fox',\n",
       "       'mousetrap', 'hippopotamus', 'Border_terrier', 'hummingbird',\n",
       "       'tailed_frog', 'otter', 'Egyptian_cat', 'four-poster', 'wild_boar',\n",
       "       'bathtub', 'agama', 'muzzle', 'hotdog', 'bib', 'espresso',\n",
       "       'timber_wolf', 'meerkat', 'nail', 'hammer', 'home_theater', 'alp',\n",
       "       'bonnet', 'handkerchief', 'hand_blower', 'polecat', 'lakeside',\n",
       "       'studio_couch', 'cup', 'cliff', 'Bedlington_terrier', 'lawn_mower',\n",
       "       'balloon', 'sunglasses', 'rapeseed', 'traffic_light', 'coil',\n",
       "       'binoculars', 'paddle', 'tiger_shark', 'sulphur-crested_cockatoo',\n",
       "       'wire-haired_fox_terrier', 'Saluki', 'American_black_bear',\n",
       "       'rotisserie', 'conch', 'skunk', 'bookshop', 'radio_telescope',\n",
       "       'cougar', 'African_grey', 'coral_reef', 'lion', 'maillot',\n",
       "       'Madagascar_cat', 'tabby', 'silky_terrier', 'giant_panda',\n",
       "       'long-horned_beetle', 'Afghan_hound', 'clumber', 'sundial',\n",
       "       'padlock', 'pool_table', 'quilt', 'beach_wagon', 'remote_control',\n",
       "       'bakery', 'pedestal', 'gas_pump', 'bookcase', 'shield', 'loupe',\n",
       "       'restaurant', 'prison', 'school_bus', 'cowboy_boot', 'jersey',\n",
       "       'wooden_spoon', 'leopard', 'mortarboard', 'teapot',\n",
       "       'military_uniform', 'washer', 'coffee_mug', 'fountain',\n",
       "       'pencil_box', 'barbell', 'grille', 'revolver', 'envelope',\n",
       "       'syringe', 'marmot', 'pole', 'laptop', 'basketball', 'tricycle',\n",
       "       'convertible', 'limousine', 'orange'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions_df.p1.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None     2259\n",
       "doggo      97\n",
       "Name: doggo, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df.doggo.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assesing data ##\n",
    "\n",
    "\n",
    "archive_df  table:\n",
    "- Dog stage classification was in different columns, which considered as a tidiness issue, each variable like (dog stage) should be in a separate column.\n",
    "- Timestamp column is object, which is invalid for time analysis. (quality issue)\n",
    "- Missing names. (quality issue)\n",
    "- Inaccurate data like (55 dog named 'a', 8 named 'the' and 7 named 'an') (quality issue)\n",
    "- Some dog ratings have overestimating numbers (higher than 20  to hundreds!, in contrary there are low ones (lower than 7!) (quality issue)\n",
    "- rating_denominator have sometimes different  numbers other than 10. (quality issue)\n",
    "\n",
    "Image prediction data:\n",
    "- columns undefined.(quality  issue)\n",
    "- Irrelevant stuff in classification prediction columns.(content issue)\n",
    "\n",
    "\n",
    "api_df:\n",
    "- retweet counts and favourite counts are columns related to archive table (tideness issue)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "exploring data\n",
    "- first prediction is more reliable than second and third.\n",
    "- in first prediction, below 0.25 belongs to stuff other than dog types.\n",
    "\n",
    "plan:\n",
    "- first: restricting data to original tweets.\n",
    "- drop from archive data id`s that has no  images in image prediction table.\n",
    "- confirm that the id numbers and counts are the same for tables.\n",
    "- continue cleaning.\n",
    "\n",
    "notes:\n",
    "- no null values in image prediction, no duplicated rows.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making copies\n",
    "archive_df_c=archive_df.copy()\n",
    "image_predictions_df_c=image_predictions_df.copy()\n",
    "api_df_c=api_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tideness #1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: \n",
    "Doge stage must be in one column\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "doggo      97 \n",
       "floofer    10 \n",
       "pupper     257\n",
       "puppo      30 \n",
       "dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first, further investigation needed for if there are dogs with two or more types:\n",
    "(archive_df_c.loc[:, 'doggo':\"puppo\"] != 'None').sum() \n",
    "#found that sum of them is higher than the number \n",
    "# of records so it must be rows with two or more classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_df_c.doggo=archive_df_c.doggo.replace('None', '')\n",
    "archive_df_c.floofer=archive_df_c.floofer.replace('None', '')\n",
    "archive_df_c.pupper=archive_df_c.pupper.replace('None', '')\n",
    "archive_df_c.puppo=archive_df_c.puppo.replace('None', '')\n",
    "# combining in one column\n",
    "archive_df_c['dog_stage']=archive_df_c.doggo+archive_df_c.floofer+archive_df_c.pupper+archive_df_c.puppo\n",
    "\n",
    "# check douple types\n",
    "archive_df_c['dog_stage'].value_counts()\n",
    "\n",
    "# adding (-) to them\n",
    "archive_df_c.loc[archive_df_c.dog_stage=='doggopupper', 'dog_stage']='doggo-pupper'\n",
    "archive_df_c.loc[archive_df_c.dog_stage=='doggofloofer', 'dog_stage']='doggo-floofer'\n",
    "archive_df_c.loc[archive_df_c.dog_stage=='doggopuppo', 'dog_stage']='doggo-puppo'\n",
    "\n",
    "#drop columns\n",
    "archive_df_c.drop(['doggo', 'floofer', 'pupper', 'puppo'], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "\n",
       "   rating_denominator     name dog_stage  \n",
       "0                  10  Phineas            "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df_c.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(quality#1, accuracy) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "modify the douple- stage tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200    At first I thought this was a shy doggo, but it's actually a Rare Canadian Floofer Owl. Amateurs would confuse the two. 11/10 only send dogs https://t.co/TXdT3tmuYk\n",
      "Name: text, dtype: object\n",
      "191    Here's a puppo participating in the #ScienceMarch. Cleverly disguising her own doggo agenda. 13/10 would keep the planet habitable for https://t.co/cMhq16isel\n",
      "Name: text, dtype: object\n",
      "460     This is Dido. She's playing the lead role in \"Pupper Stops to Catch Snow Before Resuming Shadow Box with Dried Apple.\" 13/10 (IG: didodoggo) https://t.co/m7isZrOBX7\n",
      "531     Here we have Burke (pupper) and Dexter (doggo). Pupper wants to be exactly like doggo. Both 12/10 would pet at same time https://t.co/ANBpEYHaho                    \n",
      "565     Like doggo, like pupper version 2. Both 11/10 https://t.co/9IxWAXFqze                                                                                               \n",
      "575     This is Bones. He's being haunted by another doggo of roughly the same size. 12/10 deep breaths pupper everything's fine https://t.co/55Dqe0SJNj                    \n",
      "705     This is Pinot. He's a sophisticated doggo. You can tell by the hat. Also pointier than your average pupper. Still 10/10 would pet cautiously https://t.co/f2wmLZTPHd\n",
      "733     Pupper butt 1, Doggo 0. Both 12/10 https://t.co/WQvcPEpH2u                                                                                                          \n",
      "778     RT @dog_rates: Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda                                                                           \n",
      "822     RT @dog_rates: This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC                                                          \n",
      "889     Meet Maggie &amp; Lila. Maggie is the doggo, Lila is the pupper. They are sisters. Both 12/10 would pet at the same time https://t.co/MYwR4DQKll                    \n",
      "956     Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8                            \n",
      "1063    This is just downright precious af. 12/10 for both pupper and doggo https://t.co/o5J479bZUC                                                                         \n",
      "1113    Like father (doggo), like son (pupper). Both 12/10 https://t.co/pG2inLaOda                                                                                          \n",
      "Name: text, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#investigating the douple type dogs by viewing the text:\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "print(archive_df_c.query('dog_stage == \"doggo-floofer\"').text)\n",
    "print(archive_df_c.query('dog_stage == \"doggo-puppo\"').text)\n",
    "print(archive_df_c.query('dog_stage == \"doggo-pupper\"').text)\n",
    "# according to the tweet text, some errors must be modified.  \n",
    "archive_df_c.dog_stage=archive_df_c.dog_stage.replace('doggo-floofer', 'floofer')\n",
    "archive_df_c.dog_stage=archive_df_c.dog_stage.replace('doggo-puppo', 'puppo')\n",
    "# but the doggo-pupper mix was due to existence of both in image, so it is not error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                1976\n",
       "pupper          245 \n",
       "doggo           83  \n",
       "puppo           30  \n",
       "doggo-pupper    12  \n",
       "floofer         10  \n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df_c.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality#2, inconsistent "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Replacing the empty cells by NaN, to avoid different representations in table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing empty cells with pandas NaN\n",
    "archive_df_c=archive_df_c.replace('', np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper          245\n",
       "doggo           83 \n",
       "puppo           30 \n",
       "doggo-pupper    12 \n",
       "floofer         10 \n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df_c.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality#3 (accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "    Modifying the three table to be the same id`s number and relevent content, by\n",
    "First: drop from archieve table tweets with no images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2356, 14)\n"
     ]
    }
   ],
   "source": [
    "print(archive_df_c.shape)\n",
    "# creating a list of tweets with images.\n",
    "tweets_with_image = list(image_predictions_df_c.tweet_id.unique())\n",
    "# confirming that all tweet with images exists in archive table\n",
    "len(tweets_with_image)== archive_df_c.tweet_id.isin(tweets_with_image).sum()\n",
    "# clean\n",
    "archive_df_c=archive_df_c[archive_df_c.tweet_id.isin(tweets_with_image)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2075, 14)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_df_c.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality#4 (accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "second: Dropping from archieve table retweets & replies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2075, 14)\n",
      "(1994, 14)\n",
      "(1971, 14)\n"
     ]
    }
   ],
   "source": [
    "retweet=archive_df_c.query('retweeted_status_id != \"NaN\"')\n",
    "reply=archive_df_c.query('in_reply_to_status_id != \"NaN\"')\n",
    "## testing original\n",
    "print(archive_df_c.shape)\n",
    "\n",
    "archive_df_c.drop(retweet.index, inplace=True)\n",
    "## testing first drop\n",
    "print(archive_df_c.shape)\n",
    "\n",
    "archive_df_c.drop(reply.index, inplace=True)\n",
    "## testing second drop\n",
    "print(archive_df_c.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Third: removing retweets from image table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2075, 12)\n"
     ]
    }
   ],
   "source": [
    "lst=list(archive_df_c.tweet_id.unique())\n",
    "print(image_predictions_df_c.shape)\n",
    "image_predictions_df_c=image_predictions_df_c[image_predictions_df_c.tweet_id.isin(lst)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1971, 12)\n"
     ]
    }
   ],
   "source": [
    "print(image_predictions_df_c.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Fourth: removing unneeded id`s from api table depending on the pre-cleaned archive table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2354, 3)\n"
     ]
    }
   ],
   "source": [
    "print(api_df_c.shape)\n",
    "api_df_c=api_df_c[api_df_c.tweet_id.isin(lst)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1971, 3)\n"
     ]
    }
   ],
   "source": [
    "print(api_df_c.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tideness #2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define \n",
    "    Merging all three table by\n",
    "First: merging tables Api and Archive because Api table columns belongs to tweets id (same observational unit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_arch_api=pd.merge(archive_df_c, api_df_c, on='tweet_id', how='outer')\n",
    "# df_new = df.join(neighborhood_dummies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1971 entries, 0 to 1970\n",
      "Data columns (total 16 columns):\n",
      "tweet_id                      1971 non-null int64\n",
      "in_reply_to_status_id         0 non-null float64\n",
      "in_reply_to_user_id           0 non-null float64\n",
      "timestamp                     1971 non-null object\n",
      "source                        1971 non-null object\n",
      "text                          1971 non-null object\n",
      "retweeted_status_id           0 non-null float64\n",
      "retweeted_status_user_id      0 non-null float64\n",
      "retweeted_status_timestamp    0 non-null object\n",
      "expanded_urls                 1971 non-null object\n",
      "rating_numerator              1971 non-null int64\n",
      "rating_denominator            1971 non-null int64\n",
      "name                          1971 non-null object\n",
      "dog_stage                     303 non-null object\n",
      "favorite_count                1971 non-null int64\n",
      "retweet_count                 1971 non-null int64\n",
      "dtypes: float64(4), int64(5), object(7)\n",
      "memory usage: 261.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_arch_api.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Second: Merging image table to them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all=pd.merge(df_arch_api, image_predictions_df_c, on='tweet_id', how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1971 entries, 0 to 1970\n",
      "Data columns (total 27 columns):\n",
      "tweet_id                      1971 non-null int64\n",
      "in_reply_to_status_id         0 non-null float64\n",
      "in_reply_to_user_id           0 non-null float64\n",
      "timestamp                     1971 non-null object\n",
      "source                        1971 non-null object\n",
      "text                          1971 non-null object\n",
      "retweeted_status_id           0 non-null float64\n",
      "retweeted_status_user_id      0 non-null float64\n",
      "retweeted_status_timestamp    0 non-null object\n",
      "expanded_urls                 1971 non-null object\n",
      "rating_numerator              1971 non-null int64\n",
      "rating_denominator            1971 non-null int64\n",
      "name                          1971 non-null object\n",
      "dog_stage                     303 non-null object\n",
      "favorite_count                1971 non-null int64\n",
      "retweet_count                 1971 non-null int64\n",
      "jpg_url                       1971 non-null object\n",
      "img_num                       1971 non-null int64\n",
      "p1                            1971 non-null object\n",
      "p1_conf                       1971 non-null float64\n",
      "p1_dog                        1971 non-null bool\n",
      "p2                            1971 non-null object\n",
      "p2_conf                       1971 non-null float64\n",
      "p2_dog                        1971 non-null bool\n",
      "p3                            1971 non-null object\n",
      "p3_conf                       1971 non-null float64\n",
      "p3_dog                        1971 non-null bool\n",
      "dtypes: bool(3), float64(7), int64(6), object(11)\n",
      "memory usage: 390.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_all.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality #5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Drop unneeded columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all=df_all.drop(['in_reply_to_status_id', 'in_reply_to_user_id', 'retweeted_status_id',\n",
    "                               'retweeted_status_user_id', 'retweeted_status_timestamp'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1971 entries, 0 to 1970\n",
      "Data columns (total 22 columns):\n",
      "tweet_id              1971 non-null int64\n",
      "timestamp             1971 non-null object\n",
      "source                1971 non-null object\n",
      "text                  1971 non-null object\n",
      "expanded_urls         1971 non-null object\n",
      "rating_numerator      1971 non-null int64\n",
      "rating_denominator    1971 non-null int64\n",
      "name                  1971 non-null object\n",
      "dog_stage             303 non-null object\n",
      "favorite_count        1971 non-null int64\n",
      "retweet_count         1971 non-null int64\n",
      "jpg_url               1971 non-null object\n",
      "img_num               1971 non-null int64\n",
      "p1                    1971 non-null object\n",
      "p1_conf               1971 non-null float64\n",
      "p1_dog                1971 non-null bool\n",
      "p2                    1971 non-null object\n",
      "p2_conf               1971 non-null float64\n",
      "p2_dog                1971 non-null bool\n",
      "p3                    1971 non-null object\n",
      "p3_conf               1971 non-null float64\n",
      "p3_dog                1971 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(6), object(10)\n",
      "memory usage: 313.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_all.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality #7 (invalid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "Converting timestamp to day time to  be used in analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n",
      "datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "df_all.timestamp.isnull().sum()\n",
    "print(df_all.timestamp.dtype)\n",
    "df_all.timestamp=pd.to_datetime(df_all.timestamp)\n",
    "print(df_all.timestamp.dtype)\n",
    "df_all.timestamp= df_all.timestamp.dt.date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39467</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.07611</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id   timestamp  \\\n",
       "0  892420643555336193  2017-08-01   \n",
       "\n",
       "                                                                               source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                            text  \\\n",
       "0  This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "\n",
       "                                                     expanded_urls  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator     name dog_stage  favorite_count  \\\n",
       "0  13                10                  Phineas  NaN       39467            \n",
       "\n",
       "   ...    img_num      p1   p1_conf p1_dog     p2   p2_conf p2_dog      p3  \\\n",
       "0  ...    1        orange  0.097049  False  bagel  0.085851  False  banana   \n",
       "\n",
       "   p3_conf p3_dog  \n",
       "0  0.07611  False  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality #8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    " Renaming some columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',\n",
      "       'rating_numerator', 'rating_denominator', 'name', 'dog_stage',\n",
      "       'favorite_count', 'retweet_count', 'jpg_url', 'img_num', 'p1',\n",
      "       'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf',\n",
      "       'p3_dog'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_all.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_headers=['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',\n",
    "             'rating_numerator', 'rating_denominator', 'name', 'dog_stage',\n",
    "             'favorite_count', 'retweet_count', 'jpg_url', 'img_num', \n",
    "             'first_prediction_type', 'first_prediction_confidence', 'first_prediction_result',\n",
    "             'second_prediction_type','second_prediction_confidence', 'second_prediction_result',\n",
    "             'third_prediction_type', 'third_prediction_confidence', 'third_prediction_result']\n",
    "df_all.columns=new_headers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',\n",
       "       'rating_numerator', 'rating_denominator', 'name', 'dog_stage',\n",
       "       'favorite_count', 'retweet_count', 'jpg_url', 'img_num',\n",
       "       'first_prediction_type', 'first_prediction_confidence',\n",
       "       'first_prediction_result', 'second_prediction_type',\n",
       "       'second_prediction_confidence', 'second_prediction_result',\n",
       "       'third_prediction_type', 'third_prediction_confidence',\n",
       "       'third_prediction_result'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality #9\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    " modify some wrong ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      446\n",
       "10      417\n",
       "11      392\n",
       "13      253\n",
       "9       150\n",
       "8       95 \n",
       "7       52 \n",
       "5       33 \n",
       "14      33 \n",
       "6       32 \n",
       "3       19 \n",
       "4       16 \n",
       "2       9  \n",
       "1       5  \n",
       "1776    1  \n",
       "24      1  \n",
       "26      1  \n",
       "420     1  \n",
       "27      1  \n",
       "44      1  \n",
       "45      1  \n",
       "50      1  \n",
       "60      1  \n",
       "75      1  \n",
       "80      1  \n",
       "84      1  \n",
       "88      1  \n",
       "99      1  \n",
       "121     1  \n",
       "144     1  \n",
       "165     1  \n",
       "204     1  \n",
       "0       1  \n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.rating_numerator.value_counts()\n",
    "#it is found that most common\n",
    "# and reasonable rates between 6 and 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS</td>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>Who keeps sending in pictures without dogs in them? This needs to stop. 5/10 for the mediocre road https://t.co/ELqelxWMrC</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>This is Wesley. He's clearly trespassing. Seems rather h*ckin violent too. Weaponized forehead. 3/10 wouldn't let in https://t.co/pL7wbMRW7M</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>This is Fido. He can tell the weather. Not good at fetch tho. Never comes when called. 4/10 would probably still pet https://t.co/4gOv2Q3iKP</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE</td>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>Here's another picture without a dog in it. Idk why you guys keep sending these. 4/10 just because that's a neat rug https://t.co/mOmnL19Wsl</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh</td>\n",
       "      <td>1776</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>This is Alexanderson. He's got a weird ass birth mark. Dreadful at fetch. Won't eat kibble. 3/10 wtf @Target https://t.co/FmxOpf2Sgl</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>This is Benedict. He's a feisty pup. Needs a brushing. Portable af. Looks very angry actually. 4/10 might not pet https://t.co/3oeFfHjv0Z</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>What hooligan sent in pictures w/out a dog in them? Churlish af. 3/10 just bc that's a neat fluffy bean bag chair https://t.co/wcwoGOkZvz</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp;amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>This is Keurig. He's a rare dog. Laughs like an idiot tho. Head is basically a weapon. Poorly maintained goatee 4/10 https://t.co/xOrUyj7K30</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>This is Elliot. He's blocking the roadway. Downright rude as hell. Doesn't care that you're already late. 3/10 https://t.co/FMUxir5pYu</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045</th>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>This is Dave. He's a tropical pup. Short lil legs (dachshund mix?) Excels underwater, but refuses to eat kibble 5/10 https://t.co/ZJnCxlIf62</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>This is Charl. He's a bully. Chucks that dumbbell around like its nothing. Sharp neck. Exceptionally unfluffy. 3/10 https://t.co/VfLoDZecJ7</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>This may be the greatest video I've ever been sent. 4/10 for Charles the puppy, 13/10 overall. (Vid by @stevenxx_) https://t.co/uaJmNgXR2P</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Scary dog here. Too many legs. Extra tail. Not soft, let alone fluffy. Won't bark. Moves sideways. Has weapon. 2/10 https://t.co/XOPXCSXiUT</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>Flamboyant pup here. Probably poisonous. Won't eat kibble. Doesn't bark. Slow af. Petting doesn't look fun. 1/10 https://t.co/jxukeh2BeO</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714</th>\n",
       "      <td>This dude slaps your girl's ass what do you do?\\n5/10 https://t.co/6dioUL6gcP</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>Vibrant dog here. Fabulous tail. Only 2 legs tho. Has wings but can barely fly (lame). Rather elusive. 5/10 okay pup https://t.co/cixC0M3P1e</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>This is Randall. He's from Chernobyl. Built playground himself. Has been stuck up there quite a while. 5/10 good dog https://t.co/pzrvc7wKGd</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1758</th>\n",
       "      <td>This is Tommy. He's a cool dog. Hard not to step on. Won't let go of seashell. Not fast by any means. 3/10 https://t.co/0gY6XTOpn3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1761</th>\n",
       "      <td>Awesome dog here. Not sure where it is tho. Spectacular camouflage. Enjoys leaves. Not very soft. 5/10 still petable https://t.co/rOTOteKx4q</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1774</th>\n",
       "      <td>This is a brave dog. Excellent free climber. Trying to get closer to God. Not very loyal though. Doesn't bark. 5/10 https://t.co/ODnILTr4QM</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>Two gorgeous pups here. Both have cute fake horns(adorable). Barn in the back looks on fire. 5/10 would pet rly well https://t.co/w5oYFXi0uh</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>This is Bernie. He's taking his Halloween costume very seriously. Wants to be baked. 3/10 not a good idea Bernie smh https://t.co/1zBp1moFlX</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1806</th>\n",
       "      <td>Unique dog here. Oddly shaped tail. Long pink front legs. I don't think dogs breath underwater sos. 4/10 bad owner https://t.co/0EJXxE9UxW</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>Fascinating dog here. Loves beach. Oddly long nose for dog. Massive ass paws. Hard to cuddle w. 3/10 would still pet https://t.co/IiSdmhkC5N</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825</th>\n",
       "      <td>Meet Zeek. He is a grey Cumulonimbus. Zeek is hungry. Someone should feed Zeek asap. 5/10 absolutely terrifying https://t.co/fvVNScw8VH</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855</th>\n",
       "      <td>This lil pup is Oliver. Hops around. Has wings but doesn't fly (lame). Annoying chirp. Won't catch tennis balls 2/10 https://t.co/DnhUw0aBM2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857</th>\n",
       "      <td>This dog resembles a baked potato. Bed looks uncomfortable. No tail. Comes with butter tho. 3/10 petting still fun https://t.co/x89NSCEZCq</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>Wow. Armored dog here. Ready for battle. Face looks dangerous. Not very loyal. Lil dog on back havin a blast. 5/10 https://t.co/SyMoWrp368</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>This is Tedrick. He lives on the edge. Needs someone to hit the gas tho. Other than that he's a baller. 10&amp;amp;2/10 https://t.co/LvP1TTYSCN</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>Never seen dog like this. Breathes heavy. Tilts head in a pattern. No bark. Shitty at fetch. Not even cordless. 1/10 https://t.co/i9iSGNn3fx</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>These are strange dogs. All have toupees. Long neck for dogs. In a shed of sorts? Work in groups? 4/10 still petable https://t.co/PZxSarAfSN</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>My goodness. Very rare dog here. Large. Tail dangerous. Kinda fat. Only eats leaves. Doesn't come when called 3/10 https://t.co/xYGdBrMS9h</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>Unfamiliar with this breed. Ears pointy af. Won't let go of seashell. Won't eat kibble. Not very fast. Bad dog 2/10 https://t.co/EIn5kElY1S</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>This is Josep. He is a Rye Manganese mix. Can drive w eyes closed. Very irresponsible. Menace on the roadways. 5/10 https://t.co/XNGeDwrtYH</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>Cool dog. Enjoys couch. Low monotone bark. Very nice kicks. Pisses milk (must be rare). Can't go down stairs. 4/10 https://t.co/vXMKrJC81s</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                           text  \\\n",
       "39    This is Bella. She hopes her smile made you smile. If not, she is also offering you her favorite monkey. 13.5/10 https://t.co/qjrljjt948                                    \n",
       "232   When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag                                                           \n",
       "320   The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd                                                                         \n",
       "382   Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx   \n",
       "499   This is Logan, the Chow who lived. He solemnly swears he's up to lots of good. H*ckin magical af 9.75/10 https://t.co/yBO5wuqaPS                                            \n",
       "526   Who keeps sending in pictures without dogs in them? This needs to stop. 5/10 for the mediocre road https://t.co/ELqelxWMrC                                                  \n",
       "549   This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq           \n",
       "550   This is Wesley. He's clearly trespassing. Seems rather h*ckin violent too. Weaponized forehead. 3/10 wouldn't let in https://t.co/pL7wbMRW7M                                \n",
       "644   This is Fido. He can tell the weather. Not good at fetch tho. Never comes when called. 4/10 would probably still pet https://t.co/4gOv2Q3iKP                                \n",
       "658   Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE                                                                                              \n",
       "665   Here's another picture without a dog in it. Idk why you guys keep sending these. 4/10 just because that's a neat rug https://t.co/mOmnL19Wsl                                \n",
       "700   Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8                                    \n",
       "722   This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh                                                                                              \n",
       "744   Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R                                  \n",
       "842   Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv                                                   \n",
       "884   Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a                                                                                                            \n",
       "908   This is Alexanderson. He's got a weird ass birth mark. Dreadful at fetch. Won't eat kibble. 3/10 wtf @Target https://t.co/FmxOpf2Sgl                                        \n",
       "918   This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq                                                       \n",
       "932   This is Benedict. He's a feisty pup. Needs a brushing. Portable af. Looks very angry actually. 4/10 might not pet https://t.co/3oeFfHjv0Z                                   \n",
       "939   Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1                                                                               \n",
       "958   What hooligan sent in pictures w/out a dog in them? Churlish af. 3/10 just bc that's a neat fluffy bean bag chair https://t.co/wcwoGOkZvz                                   \n",
       "963   Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12                                                                \n",
       "981   From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK                          \n",
       "1004  This is Keurig. He's a rare dog. Laughs like an idiot tho. Head is basically a weapon. Poorly maintained goatee 4/10 https://t.co/xOrUyj7K30                                \n",
       "1014  This is Elliot. He's blocking the roadway. Downright rude as hell. Doesn't care that you're already late. 3/10 https://t.co/FMUxir5pYu                                      \n",
       "1045  Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa                                                                                          \n",
       "1089  This is Dave. He's a tropical pup. Short lil legs (dachshund mix?) Excels underwater, but refuses to eat kibble 5/10 https://t.co/ZJnCxlIf62                                \n",
       "1096  This is Charl. He's a bully. Chucks that dumbbell around like its nothing. Sharp neck. Exceptionally unfluffy. 3/10 https://t.co/VfLoDZecJ7                                 \n",
       "1120  Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ                                                                                \n",
       "1141  This may be the greatest video I've ever been sent. 4/10 for Charles the puppy, 13/10 overall. (Vid by @stevenxx_) https://t.co/uaJmNgXR2P                                  \n",
       "...                                                                                                                                          ...                                  \n",
       "1701  Scary dog here. Too many legs. Extra tail. Not soft, let alone fluffy. Won't bark. Moves sideways. Has weapon. 2/10 https://t.co/XOPXCSXiUT                                 \n",
       "1713  Flamboyant pup here. Probably poisonous. Won't eat kibble. Doesn't bark. Slow af. Petting doesn't look fun. 1/10 https://t.co/jxukeh2BeO                                    \n",
       "1714  This dude slaps your girl's ass what do you do?\\n5/10 https://t.co/6dioUL6gcP                                                                                               \n",
       "1731  Vibrant dog here. Fabulous tail. Only 2 legs tho. Has wings but can barely fly (lame). Rather elusive. 5/10 okay pup https://t.co/cixC0M3P1e                                \n",
       "1756  This is Randall. He's from Chernobyl. Built playground himself. Has been stuck up there quite a while. 5/10 good dog https://t.co/pzrvc7wKGd                                \n",
       "1758  This is Tommy. He's a cool dog. Hard not to step on. Won't let go of seashell. Not fast by any means. 3/10 https://t.co/0gY6XTOpn3                                          \n",
       "1761  Awesome dog here. Not sure where it is tho. Spectacular camouflage. Enjoys leaves. Not very soft. 5/10 still petable https://t.co/rOTOteKx4q                                \n",
       "1774  This is a brave dog. Excellent free climber. Trying to get closer to God. Not very loyal though. Doesn't bark. 5/10 https://t.co/ODnILTr4QM                                 \n",
       "1801  Two gorgeous pups here. Both have cute fake horns(adorable). Barn in the back looks on fire. 5/10 would pet rly well https://t.co/w5oYFXi0uh                                \n",
       "1803  This is Bernie. He's taking his Halloween costume very seriously. Wants to be baked. 3/10 not a good idea Bernie smh https://t.co/1zBp1moFlX                                \n",
       "1806  Unique dog here. Oddly shaped tail. Long pink front legs. I don't think dogs breath underwater sos. 4/10 bad owner https://t.co/0EJXxE9UxW                                  \n",
       "1821  Fascinating dog here. Loves beach. Oddly long nose for dog. Massive ass paws. Hard to cuddle w. 3/10 would still pet https://t.co/IiSdmhkC5N                                \n",
       "1825  Meet Zeek. He is a grey Cumulonimbus. Zeek is hungry. Someone should feed Zeek asap. 5/10 absolutely terrifying https://t.co/fvVNScw8VH                                     \n",
       "1840  Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m                                     \n",
       "1855  This lil pup is Oliver. Hops around. Has wings but doesn't fly (lame). Annoying chirp. Won't catch tennis balls 2/10 https://t.co/DnhUw0aBM2                                \n",
       "1857  This dog resembles a baked potato. Bed looks uncomfortable. No tail. Comes with butter tho. 3/10 petting still fun https://t.co/x89NSCEZCq                                  \n",
       "1860  Wow. Armored dog here. Ready for battle. Face looks dangerous. Not very loyal. Lil dog on back havin a blast. 5/10 https://t.co/SyMoWrp368                                  \n",
       "1864  This is Tedrick. He lives on the edge. Needs someone to hit the gas tho. Other than that he's a baller. 10&amp;2/10 https://t.co/LvP1TTYSCN                                 \n",
       "1877  Never seen dog like this. Breathes heavy. Tilts head in a pattern. No bark. Shitty at fetch. Not even cordless. 1/10 https://t.co/i9iSGNn3fx                                \n",
       "1904  These are strange dogs. All have toupees. Long neck for dogs. In a shed of sorts? Work in groups? 4/10 still petable https://t.co/PZxSarAfSN                                \n",
       "1920  My goodness. Very rare dog here. Large. Tail dangerous. Kinda fat. Only eats leaves. Doesn't come when called 3/10 https://t.co/xYGdBrMS9h                                  \n",
       "1925  Unfamiliar with this breed. Ears pointy af. Won't let go of seashell. Won't eat kibble. Not very fast. Bad dog 2/10 https://t.co/EIn5kElY1S                                 \n",
       "1927  This is Josep. He is a Rye Manganese mix. Can drive w eyes closed. Very irresponsible. Menace on the roadways. 5/10 https://t.co/XNGeDwrtYH                                 \n",
       "1931  Cool dog. Enjoys couch. Low monotone bark. Very nice kicks. Pisses milk (must be rare). Can't go down stairs. 4/10 https://t.co/vXMKrJC81s                                  \n",
       "1941  This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t                                \n",
       "1949  This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0                                  \n",
       "1950  This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv                                    \n",
       "1953  Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX                                      \n",
       "1964  This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc                                  \n",
       "1966  Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq                                                    \n",
       "\n",
       "      rating_numerator  rating_denominator  \n",
       "39    5                 10                  \n",
       "232   0                 10                  \n",
       "320   84                70                  \n",
       "382   24                7                   \n",
       "499   75                10                  \n",
       "526   5                 10                  \n",
       "549   27                10                  \n",
       "550   3                 10                  \n",
       "644   4                 10                  \n",
       "658   165               150                 \n",
       "665   4                 10                  \n",
       "700   5                 10                  \n",
       "722   1776              10                  \n",
       "744   4                 10                  \n",
       "842   204               170                 \n",
       "884   4                 20                  \n",
       "908   3                 10                  \n",
       "918   50                50                  \n",
       "932   4                 10                  \n",
       "939   99                90                  \n",
       "958   3                 10                  \n",
       "963   80                80                  \n",
       "981   45                50                  \n",
       "1004  4                 10                  \n",
       "1014  3                 10                  \n",
       "1045  60                50                  \n",
       "1089  5                 10                  \n",
       "1096  3                 10                  \n",
       "1120  44                40                  \n",
       "1141  4                 10                  \n",
       "...  ..                 ..                  \n",
       "1701  2                 10                  \n",
       "1713  1                 10                  \n",
       "1714  5                 10                  \n",
       "1731  5                 10                  \n",
       "1756  5                 10                  \n",
       "1758  3                 10                  \n",
       "1761  5                 10                  \n",
       "1774  5                 10                  \n",
       "1801  5                 10                  \n",
       "1803  3                 10                  \n",
       "1806  4                 10                  \n",
       "1821  3                 10                  \n",
       "1825  5                 10                  \n",
       "1840  4                 10                  \n",
       "1855  2                 10                  \n",
       "1857  3                 10                  \n",
       "1860  5                 10                  \n",
       "1864  2                 10                  \n",
       "1877  1                 10                  \n",
       "1904  4                 10                  \n",
       "1920  3                 10                  \n",
       "1925  2                 10                  \n",
       "1927  5                 10                  \n",
       "1931  4                 10                  \n",
       "1941  2                 10                  \n",
       "1949  3                 10                  \n",
       "1950  1                 2                   \n",
       "1953  1                 10                  \n",
       "1964  2                 10                  \n",
       "1966  5                 10                  \n",
       "\n",
       "[101 rows x 3 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.query('rating_numerator>15 or rating_numerator<6')[['text', 'rating_numerator', 'rating_denominator']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all.at[39,'rating_numerator']=14 #extracted wrong\n",
    "df_all.at[175,'rating_numerator']=11  #he was kidding, he put the right rating after that.\n",
    "df_all.at[499,'rating_numerator']=10 #extracted wrong\n",
    "df_all.at[320,'rating_numerator']=12 #sum ratings for dogs, average taken."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>Who keeps sending in pictures without dogs in them? This needs to stop. 5/10 for the mediocre road https://t.co/ELqelxWMrC</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>This is Wesley. He's clearly trespassing. Seems rather h*ckin violent too. Weaponized forehead. 3/10 wouldn't let in https://t.co/pL7wbMRW7M</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>This is Fido. He can tell the weather. Not good at fetch tho. Never comes when called. 4/10 would probably still pet https://t.co/4gOv2Q3iKP</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE</td>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>Here's another picture without a dog in it. Idk why you guys keep sending these. 4/10 just because that's a neat rug https://t.co/mOmnL19Wsl</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh</td>\n",
       "      <td>1776</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>This is Alexanderson. He's got a weird ass birth mark. Dreadful at fetch. Won't eat kibble. 3/10 wtf @Target https://t.co/FmxOpf2Sgl</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>This is Benedict. He's a feisty pup. Needs a brushing. Portable af. Looks very angry actually. 4/10 might not pet https://t.co/3oeFfHjv0Z</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>What hooligan sent in pictures w/out a dog in them? Churlish af. 3/10 just bc that's a neat fluffy bean bag chair https://t.co/wcwoGOkZvz</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp;amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>This is Keurig. He's a rare dog. Laughs like an idiot tho. Head is basically a weapon. Poorly maintained goatee 4/10 https://t.co/xOrUyj7K30</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1014</th>\n",
       "      <td>This is Elliot. He's blocking the roadway. Downright rude as hell. Doesn't care that you're already late. 3/10 https://t.co/FMUxir5pYu</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045</th>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>This is Dave. He's a tropical pup. Short lil legs (dachshund mix?) Excels underwater, but refuses to eat kibble 5/10 https://t.co/ZJnCxlIf62</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>This is Charl. He's a bully. Chucks that dumbbell around like its nothing. Sharp neck. Exceptionally unfluffy. 3/10 https://t.co/VfLoDZecJ7</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>This may be the greatest video I've ever been sent. 4/10 for Charles the puppy, 13/10 overall. (Vid by @stevenxx_) https://t.co/uaJmNgXR2P</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1155</th>\n",
       "      <td>Meet Phil. He's big af. Currently destroying this nice family home. Completely uncalled for. 3/10 not a good pupper https://t.co/fShNNhBWYx</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>When bae says they can't go out but you see them with someone else that same night. 5/10 &amp;amp; 10/10 for heartbroken pup https://t.co/aenk0KpoWM</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>Army of water dogs here. None of them know where they're going. Have no real purpose. Aggressive barks. 5/10 for all https://t.co/A88x73TwMN</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Scary dog here. Too many legs. Extra tail. Not soft, let alone fluffy. Won't bark. Moves sideways. Has weapon. 2/10 https://t.co/XOPXCSXiUT</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1713</th>\n",
       "      <td>Flamboyant pup here. Probably poisonous. Won't eat kibble. Doesn't bark. Slow af. Petting doesn't look fun. 1/10 https://t.co/jxukeh2BeO</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1714</th>\n",
       "      <td>This dude slaps your girl's ass what do you do?\\n5/10 https://t.co/6dioUL6gcP</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>Vibrant dog here. Fabulous tail. Only 2 legs tho. Has wings but can barely fly (lame). Rather elusive. 5/10 okay pup https://t.co/cixC0M3P1e</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>This is Randall. He's from Chernobyl. Built playground himself. Has been stuck up there quite a while. 5/10 good dog https://t.co/pzrvc7wKGd</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1758</th>\n",
       "      <td>This is Tommy. He's a cool dog. Hard not to step on. Won't let go of seashell. Not fast by any means. 3/10 https://t.co/0gY6XTOpn3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1761</th>\n",
       "      <td>Awesome dog here. Not sure where it is tho. Spectacular camouflage. Enjoys leaves. Not very soft. 5/10 still petable https://t.co/rOTOteKx4q</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1774</th>\n",
       "      <td>This is a brave dog. Excellent free climber. Trying to get closer to God. Not very loyal though. Doesn't bark. 5/10 https://t.co/ODnILTr4QM</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>Two gorgeous pups here. Both have cute fake horns(adorable). Barn in the back looks on fire. 5/10 would pet rly well https://t.co/w5oYFXi0uh</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>This is Bernie. He's taking his Halloween costume very seriously. Wants to be baked. 3/10 not a good idea Bernie smh https://t.co/1zBp1moFlX</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1806</th>\n",
       "      <td>Unique dog here. Oddly shaped tail. Long pink front legs. I don't think dogs breath underwater sos. 4/10 bad owner https://t.co/0EJXxE9UxW</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>Fascinating dog here. Loves beach. Oddly long nose for dog. Massive ass paws. Hard to cuddle w. 3/10 would still pet https://t.co/IiSdmhkC5N</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825</th>\n",
       "      <td>Meet Zeek. He is a grey Cumulonimbus. Zeek is hungry. Someone should feed Zeek asap. 5/10 absolutely terrifying https://t.co/fvVNScw8VH</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1855</th>\n",
       "      <td>This lil pup is Oliver. Hops around. Has wings but doesn't fly (lame). Annoying chirp. Won't catch tennis balls 2/10 https://t.co/DnhUw0aBM2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857</th>\n",
       "      <td>This dog resembles a baked potato. Bed looks uncomfortable. No tail. Comes with butter tho. 3/10 petting still fun https://t.co/x89NSCEZCq</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1860</th>\n",
       "      <td>Wow. Armored dog here. Ready for battle. Face looks dangerous. Not very loyal. Lil dog on back havin a blast. 5/10 https://t.co/SyMoWrp368</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>This is Tedrick. He lives on the edge. Needs someone to hit the gas tho. Other than that he's a baller. 10&amp;amp;2/10 https://t.co/LvP1TTYSCN</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>Never seen dog like this. Breathes heavy. Tilts head in a pattern. No bark. Shitty at fetch. Not even cordless. 1/10 https://t.co/i9iSGNn3fx</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1904</th>\n",
       "      <td>These are strange dogs. All have toupees. Long neck for dogs. In a shed of sorts? Work in groups? 4/10 still petable https://t.co/PZxSarAfSN</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>My goodness. Very rare dog here. Large. Tail dangerous. Kinda fat. Only eats leaves. Doesn't come when called 3/10 https://t.co/xYGdBrMS9h</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>Unfamiliar with this breed. Ears pointy af. Won't let go of seashell. Won't eat kibble. Not very fast. Bad dog 2/10 https://t.co/EIn5kElY1S</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>This is Josep. He is a Rye Manganese mix. Can drive w eyes closed. Very irresponsible. Menace on the roadways. 5/10 https://t.co/XNGeDwrtYH</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>Cool dog. Enjoys couch. Low monotone bark. Very nice kicks. Pisses milk (must be rare). Can't go down stairs. 4/10 https://t.co/vXMKrJC81s</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                           text  \\\n",
       "232   When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag                                                           \n",
       "382   Meet Sam. She smiles 24/7 &amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx   \n",
       "526   Who keeps sending in pictures without dogs in them? This needs to stop. 5/10 for the mediocre road https://t.co/ELqelxWMrC                                                  \n",
       "549   This is Sophie. She's a Jubilant Bush Pupper. Super h*ckin rare. Appears at random just to smile at the locals. 11.27/10 would smile back https://t.co/QFaUiIHxHq           \n",
       "550   This is Wesley. He's clearly trespassing. Seems rather h*ckin violent too. Weaponized forehead. 3/10 wouldn't let in https://t.co/pL7wbMRW7M                                \n",
       "644   This is Fido. He can tell the weather. Not good at fetch tho. Never comes when called. 4/10 would probably still pet https://t.co/4gOv2Q3iKP                                \n",
       "658   Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE                                                                                              \n",
       "665   Here's another picture without a dog in it. Idk why you guys keep sending these. 4/10 just because that's a neat rug https://t.co/mOmnL19Wsl                                \n",
       "700   Please stop sending it pictures that don't even have a doggo or pupper in them. Churlish af. 5/10 neat couch tho https://t.co/u2c9c7qSg8                                    \n",
       "722   This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh                                                                                              \n",
       "744   Viewer discretion is advised. This is a terrible attack in progress. Not even in water (tragic af). 4/10 bad sherk https://t.co/L3U0j14N5R                                  \n",
       "842   Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv                                                   \n",
       "884   Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a                                                                                                            \n",
       "908   This is Alexanderson. He's got a weird ass birth mark. Dreadful at fetch. Won't eat kibble. 3/10 wtf @Target https://t.co/FmxOpf2Sgl                                        \n",
       "918   This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq                                                       \n",
       "932   This is Benedict. He's a feisty pup. Needs a brushing. Portable af. Looks very angry actually. 4/10 might not pet https://t.co/3oeFfHjv0Z                                   \n",
       "939   Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1                                                                               \n",
       "958   What hooligan sent in pictures w/out a dog in them? Churlish af. 3/10 just bc that's a neat fluffy bean bag chair https://t.co/wcwoGOkZvz                                   \n",
       "963   Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12                                                                \n",
       "981   From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK                          \n",
       "1004  This is Keurig. He's a rare dog. Laughs like an idiot tho. Head is basically a weapon. Poorly maintained goatee 4/10 https://t.co/xOrUyj7K30                                \n",
       "1014  This is Elliot. He's blocking the roadway. Downright rude as hell. Doesn't care that you're already late. 3/10 https://t.co/FMUxir5pYu                                      \n",
       "1045  Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa                                                                                          \n",
       "1089  This is Dave. He's a tropical pup. Short lil legs (dachshund mix?) Excels underwater, but refuses to eat kibble 5/10 https://t.co/ZJnCxlIf62                                \n",
       "1096  This is Charl. He's a bully. Chucks that dumbbell around like its nothing. Sharp neck. Exceptionally unfluffy. 3/10 https://t.co/VfLoDZecJ7                                 \n",
       "1120  Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ                                                                                \n",
       "1141  This may be the greatest video I've ever been sent. 4/10 for Charles the puppy, 13/10 overall. (Vid by @stevenxx_) https://t.co/uaJmNgXR2P                                  \n",
       "1155  Meet Phil. He's big af. Currently destroying this nice family home. Completely uncalled for. 3/10 not a good pupper https://t.co/fShNNhBWYx                                 \n",
       "1179  When bae says they can't go out but you see them with someone else that same night. 5/10 &amp; 10/10 for heartbroken pup https://t.co/aenk0KpoWM                            \n",
       "1246  Army of water dogs here. None of them know where they're going. Have no real purpose. Aggressive barks. 5/10 for all https://t.co/A88x73TwMN                                \n",
       "...                                                                                                                                            ...                                \n",
       "1701  Scary dog here. Too many legs. Extra tail. Not soft, let alone fluffy. Won't bark. Moves sideways. Has weapon. 2/10 https://t.co/XOPXCSXiUT                                 \n",
       "1713  Flamboyant pup here. Probably poisonous. Won't eat kibble. Doesn't bark. Slow af. Petting doesn't look fun. 1/10 https://t.co/jxukeh2BeO                                    \n",
       "1714  This dude slaps your girl's ass what do you do?\\n5/10 https://t.co/6dioUL6gcP                                                                                               \n",
       "1731  Vibrant dog here. Fabulous tail. Only 2 legs tho. Has wings but can barely fly (lame). Rather elusive. 5/10 okay pup https://t.co/cixC0M3P1e                                \n",
       "1756  This is Randall. He's from Chernobyl. Built playground himself. Has been stuck up there quite a while. 5/10 good dog https://t.co/pzrvc7wKGd                                \n",
       "1758  This is Tommy. He's a cool dog. Hard not to step on. Won't let go of seashell. Not fast by any means. 3/10 https://t.co/0gY6XTOpn3                                          \n",
       "1761  Awesome dog here. Not sure where it is tho. Spectacular camouflage. Enjoys leaves. Not very soft. 5/10 still petable https://t.co/rOTOteKx4q                                \n",
       "1774  This is a brave dog. Excellent free climber. Trying to get closer to God. Not very loyal though. Doesn't bark. 5/10 https://t.co/ODnILTr4QM                                 \n",
       "1801  Two gorgeous pups here. Both have cute fake horns(adorable). Barn in the back looks on fire. 5/10 would pet rly well https://t.co/w5oYFXi0uh                                \n",
       "1803  This is Bernie. He's taking his Halloween costume very seriously. Wants to be baked. 3/10 not a good idea Bernie smh https://t.co/1zBp1moFlX                                \n",
       "1806  Unique dog here. Oddly shaped tail. Long pink front legs. I don't think dogs breath underwater sos. 4/10 bad owner https://t.co/0EJXxE9UxW                                  \n",
       "1821  Fascinating dog here. Loves beach. Oddly long nose for dog. Massive ass paws. Hard to cuddle w. 3/10 would still pet https://t.co/IiSdmhkC5N                                \n",
       "1825  Meet Zeek. He is a grey Cumulonimbus. Zeek is hungry. Someone should feed Zeek asap. 5/10 absolutely terrifying https://t.co/fvVNScw8VH                                     \n",
       "1840  Here is a mother dog caring for her pups. Snazzy red mohawk. Doesn't wag tail. Pups look confused. Overall 4/10 https://t.co/YOHe6lf09m                                     \n",
       "1855  This lil pup is Oliver. Hops around. Has wings but doesn't fly (lame). Annoying chirp. Won't catch tennis balls 2/10 https://t.co/DnhUw0aBM2                                \n",
       "1857  This dog resembles a baked potato. Bed looks uncomfortable. No tail. Comes with butter tho. 3/10 petting still fun https://t.co/x89NSCEZCq                                  \n",
       "1860  Wow. Armored dog here. Ready for battle. Face looks dangerous. Not very loyal. Lil dog on back havin a blast. 5/10 https://t.co/SyMoWrp368                                  \n",
       "1864  This is Tedrick. He lives on the edge. Needs someone to hit the gas tho. Other than that he's a baller. 10&amp;2/10 https://t.co/LvP1TTYSCN                                 \n",
       "1877  Never seen dog like this. Breathes heavy. Tilts head in a pattern. No bark. Shitty at fetch. Not even cordless. 1/10 https://t.co/i9iSGNn3fx                                \n",
       "1904  These are strange dogs. All have toupees. Long neck for dogs. In a shed of sorts? Work in groups? 4/10 still petable https://t.co/PZxSarAfSN                                \n",
       "1920  My goodness. Very rare dog here. Large. Tail dangerous. Kinda fat. Only eats leaves. Doesn't come when called 3/10 https://t.co/xYGdBrMS9h                                  \n",
       "1925  Unfamiliar with this breed. Ears pointy af. Won't let go of seashell. Won't eat kibble. Not very fast. Bad dog 2/10 https://t.co/EIn5kElY1S                                 \n",
       "1927  This is Josep. He is a Rye Manganese mix. Can drive w eyes closed. Very irresponsible. Menace on the roadways. 5/10 https://t.co/XNGeDwrtYH                                 \n",
       "1931  Cool dog. Enjoys couch. Low monotone bark. Very nice kicks. Pisses milk (must be rare). Can't go down stairs. 4/10 https://t.co/vXMKrJC81s                                  \n",
       "1941  This is quite the dog. Gets really excited when not in water. Not very soft tho. Bad at fetch. Can't do tricks. 2/10 https://t.co/aMCTNWO94t                                \n",
       "1949  This is a funny dog. Weird toes. Won't come down. Loves branch. Refuses to eat his food. Hard to cuddle with. 3/10 https://t.co/IIXis0zta0                                  \n",
       "1950  This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv                                    \n",
       "1953  Not familiar with this breed. No tail (weird). Only 2 legs. Doesn't bark. Surprisingly quick. Shits eggs. 1/10 https://t.co/Asgdc6kuLX                                      \n",
       "1964  This is an odd dog. Hard on the outside but loving on the inside. Petting still fun. Doesn't play catch well. 2/10 https://t.co/v5A4vzSDdc                                  \n",
       "1966  Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq                                                    \n",
       "\n",
       "      rating_numerator  rating_denominator  \n",
       "232   0                 10                  \n",
       "382   24                7                   \n",
       "526   5                 10                  \n",
       "549   27                10                  \n",
       "550   3                 10                  \n",
       "644   4                 10                  \n",
       "658   165               150                 \n",
       "665   4                 10                  \n",
       "700   5                 10                  \n",
       "722   1776              10                  \n",
       "744   4                 10                  \n",
       "842   204               170                 \n",
       "884   4                 20                  \n",
       "908   3                 10                  \n",
       "918   50                50                  \n",
       "932   4                 10                  \n",
       "939   99                90                  \n",
       "958   3                 10                  \n",
       "963   80                80                  \n",
       "981   45                50                  \n",
       "1004  4                 10                  \n",
       "1014  3                 10                  \n",
       "1045  60                50                  \n",
       "1089  5                 10                  \n",
       "1096  3                 10                  \n",
       "1120  44                40                  \n",
       "1141  4                 10                  \n",
       "1155  3                 10                  \n",
       "1179  5                 10                  \n",
       "1246  5                 10                  \n",
       "...  ..                 ..                  \n",
       "1701  2                 10                  \n",
       "1713  1                 10                  \n",
       "1714  5                 10                  \n",
       "1731  5                 10                  \n",
       "1756  5                 10                  \n",
       "1758  3                 10                  \n",
       "1761  5                 10                  \n",
       "1774  5                 10                  \n",
       "1801  5                 10                  \n",
       "1803  3                 10                  \n",
       "1806  4                 10                  \n",
       "1821  3                 10                  \n",
       "1825  5                 10                  \n",
       "1840  4                 10                  \n",
       "1855  2                 10                  \n",
       "1857  3                 10                  \n",
       "1860  5                 10                  \n",
       "1864  2                 10                  \n",
       "1877  1                 10                  \n",
       "1904  4                 10                  \n",
       "1920  3                 10                  \n",
       "1925  2                 10                  \n",
       "1927  5                 10                  \n",
       "1931  4                 10                  \n",
       "1941  2                 10                  \n",
       "1949  3                 10                  \n",
       "1950  1                 2                   \n",
       "1953  1                 10                  \n",
       "1964  2                 10                  \n",
       "1966  5                 10                  \n",
       "\n",
       "[98 rows x 3 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all.query('rating_numerator>15 or rating_numerator<6')[['text', 'rating_numerator', 'rating_denominator']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all.to_csv('twitter_archive_master.csv', index= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>first_prediction_type</th>\n",
       "      <th>first_prediction_confidence</th>\n",
       "      <th>first_prediction_result</th>\n",
       "      <th>second_prediction_type</th>\n",
       "      <th>second_prediction_confidence</th>\n",
       "      <th>second_prediction_result</th>\n",
       "      <th>third_prediction_type</th>\n",
       "      <th>third_prediction_confidence</th>\n",
       "      <th>third_prediction_result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39467</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.07611</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  timestamp  \\\n",
       "0  892420643555336193 2017-08-01   \n",
       "\n",
       "                                                                               source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                            text  \\\n",
       "0  This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "\n",
       "                                                     expanded_urls  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator     name dog_stage  favorite_count  \\\n",
       "0  13                10                  Phineas  NaN       39467            \n",
       "\n",
       "            ...            img_num first_prediction_type  \\\n",
       "0           ...            1        orange                 \n",
       "\n",
       "   first_prediction_confidence first_prediction_result  \\\n",
       "0  0.097049                     False                    \n",
       "\n",
       "   second_prediction_type  second_prediction_confidence  \\\n",
       "0  bagel                   0.085851                       \n",
       "\n",
       "  second_prediction_result  third_prediction_type  \\\n",
       "0  False                    banana                  \n",
       "\n",
       "   third_prediction_confidence third_prediction_result  \n",
       "0  0.07611                      False                   \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master= pd.read_csv('twitter_archive_master.csv', parse_dates=['timestamp'])\n",
    "df_master.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4941296c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# asking abount most common dog stage:\n",
    "df_master.dog_stage.value_counts().plot(kind='bar', color='blue', figsize=(3,3));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f493e552f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_master.groupby('dog_stage').favorite_count.mean().plot(kind='bar', color= 'blue', figsize=(3,3));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49402fb780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_master.groupby('dog_stage').retweet_count.mean().plot(kind='bar', color='blue',figsize=(3,3));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v19nYB1g7cUv5iNgqIvbOzEurVydJksZeL302Pgb8T8fr+0qbJEnSjHrqIJqZE1cQJTMfpre+HpIkST2FjRsj4i0R8bjyeCtw43xmGhF/HBFrI+LaiDgzIh4fEbtGxKURcUNEfCYiNivjbl5eryvDl3RM552l/fqIOKCjfXlpWxcRx82nVkmSND+9hI03AL8K/ABYD+wNHD3XGUbEzsBbgGWZuQfN/VYOA04ATszMpcCdNNf3oPy8MzOfDpxYxiMidi/vexawHPhoRGwSEZsAHwEOpLna6eFlXEmSNAS9XK789sw8LDN3zMwnZ+arM/P2ec53U2CLiNiU5i6ytwL7AmeX4atoTrEFOLi8pgzfLyKitH86Mx/IzJuAdcBe5bEuM2/MzAeBT5dxJUnSEMzlFvPzkpk/AP4W+D5NyLgbuBy4q+MGb+uBncvznYGby3sfKuNv39k+6T1TtT9GRBwdEWsiYs2GDRvmv3CSJOkxBh42ImI7mj0NuwI/D2xJc8hjsolOqd2uVppzaH9sY+bJmbksM5ctWrRoptIlSdIcDDxsAC8BbsrMDZn5U+BzNH1Cti2HVQAWA7eU5+uBXQDK8G2AOzrbJ71nqnZJkjQEvdz19ckRcUpEnFte7x4RR830vml8H9gnIp5Q+l7sB3wLuBB4VRnnCOAL5fnq8poy/CvlVNzVwGHlbJVdgaXAN4DLgKXl7JbNaDqRrp5HvZIkaR562bNxOnAezSEPgG8Db5vrDMuVR88GrgCuKTWcDLwDODYi1tH0yZi4/8opwPal/VjguDKdtcBZNEHlS8Axmbmx9Ot4U6n5OuCsMq4kSRqCXi7OtUNmnhUR74Smk2ZEbJzpTdPJzBXAiknNN9KcSTJ53J8Ah04xneOB47u0nwOcM58aJUlSf/SyZ+O+iNie0smy3Cvl7qpVSZKk1uhlz8axNH0enhYRXwUWMcWeBkmSpMl6CRtrgd8AnkFzWun1DOcsFkmSNIZ6CQ2XZOZDmbk2M68tp6teUrswSZLUDlPu2YiIp9BceXOLiHgej1wsa2uaS4xLkiTNaLrDKAcAr6O5KNYHO9rvBf68Yk2SJKlFpgwbmbkKWBURr8zMzw6wJkmS1CLTHUb53cz8J2BJRBw7eXhmfrDL2yRJkh5lusMoW5afTxxEIZIkqZ2mO4zy8YjYBLgnM08cYE1Sq6yMlazIyRfMlaSFY9pTXzNzI3DQgGqRJEkt1MtFvb4WER8GPgPcN9GYmVdUq0qSJLVGL2HjV8vPd3e0JbBv/8uRJEltM2PYyMwXD6IQSZLUTjNerjwitomID0bEmvL4QERsM4jiJEnS+Ovl3iin0lw19LfL4x7gtJpFSZLqWBkrh12CFqBe+mw8LTNf2fF6ZURcWasgSZLULr3s2fhxRLxw4kVEvAD4cb2SJElSm/SyZ+ONNPdImeincSdwRL2SJElSm/QSNq7JzOdExNYAmXlP5ZokSVKL9HIY5aaIOBn4ZZqOopIkST3rJWw8A/h34Bia4PHhzj4ckiRJ05kxbGTmjzPzrMz838DzgK2B/1e9MkmS1Aq97NkgIn4jIj4KXAE8nuZ6G5IkSTOasYNoRNwEXAmcBbw9M++b4S2SJEk/08vZKM/xDBRJkjRXvYSNByPiGOBZNIdQAMjM369WlSRJC8jKWMmKXDHsMqrppc/Gp4CnAAfQdAxdjKfASpKkHvUSNp6emX8F3JeZq4DfBH6pblmSJKktegkbPy0/74qIPYBtgCXVKpIkSa3SS5+NkyNiO+AvgdXAE4G/qlqVJElqjSnDRkS8NTP/HrguM+8ELgZ2G1hlkiSpFaY7jHJk+fmhQRQiSZLaabrDKNdFxHeBRRFxdUd7AJmZz65amSRJaoUpw0ZmHh4RTwHOAw4aXEmSJKlNpj0bJTN/mJnPyczvTX4MqkA1F3uRJGlc9XQjNkmSpLkybEiSpKqmDBsR8any862DK0eSJLXNdHs2nh8RTwV+PyK2i4gndT4GVaAkSRpv0536+g/Al2gu5HU5zSmvExIv8CVJknow5Z6NzDwpM58JnJqZu2Xmrh0Pg4YkSerJjPdGycw3RsRzgF8rTRdn5tXTvUeSJGnCjGejRMRbgDOAHcvjjIh4c+3CJElSO/Ry19fXA3tn5n0AEXECcAneM0WSJPWgl+tsBLCx4/VGHt1ZVJIkaUq97Nk4Dbg0Iv6lvD4EOKVeSZIkqU166SD6wYi4CHghzR6NIzPzm7ULkyRJ7dDLng0y8wrgisq1SJKkFvLeKJIkqSrDhiRJqmrasBERm0TEvw+qGEmS1D7Tho3M3AjcHxHbDKgeSZLUMr0cRvkJcE1EnBIRJ0085jPTiNg2Is6OiP+KiOsi4lfK3WTPj4gbys/tyrhR5rkuIq6OiD07pnNEGf+GiDiio/35EXFNec9JEeF1QSRJGpJewsYXgb8CLqa5++vEYz7+HvhSZv4i8BzgOuA44ILMXApcUF4DHAgsLY+jgY8BlNvcrwD2BvYCVkwElDLO0R3vWz7PeiVJ0hz1cp2NVRGxBfC/MvP6+c4wIrYGfh14XZn+g8CDEXEw8KIy2irgIuAdwMHAJzMzga+XvSI7lXHPz8w7ynTPB5aXa4JsnZmXlPZP0lyI7Nz51i5JkmavlxuxvQK4EvhSef3ciFg9j3nuBmwATouIb0bEJyJiS+DJmXkrQPm5Yxl/Z+DmjvevL23Tta/v0i5Jkoagl8Mo76I5THEXQGZeCew6j3luCuwJfCwznwfcxyOHTLrp1t8i59D+2AlHHB0RayJizYYNG6avWpIkzUkvYeOhzLx7UlvXjXeP1gPrM/PS8vpsmvBxWzk8Qvl5e8f4u3S8fzFwywzti7u0P0ZmnpyZyzJz2aJFi+axSJIkaSq9hI1rI+LVwCYRsTQiPgR8ba4zzMwfAjdHxDNK037At4DVwMQZJUcAXyjPVwOvLWel7APcXQ6znAfsHxHblY6h+wPnlWH3RsQ+5SyU13ZMS5IkDVgv90Z5M/AXwAPAmTQb+ffMc75vBs6IiM2AG4EjaYLPWRFxFPB94NAy7jnAy4B1wP1lXDLzjoh4D3BZGe/dE51FgTcCpwNb0HQMtXOoJElD0svZKPcDfxERJzQv8975zrT0+1jWZdB+XcZN4JgppnMqcGqX9jXAHvMsU5Ik9UEvZ6P8ckRcA1xNc3GvqyLi+fVLkyT1w8pYOewStMD1chjlFOCPMvM/ACLihcBpwLNrFiZJktqhlw6i904EDYDM/E9g3odSJEnSwjDlno2Oe5B8IyI+TtM5NIHfobm6pyRJ0oymO4zygUmvV3Q8n891NiRJ0gIyZdjIzBcPshBJktROM3YQjYhtaS6MtaRz/Mx8S72yJEmztTJWsiJXzDyiNGC9nI1yDvB14Brg4brlSJKktuklbDw+M4+tXokkDYh7AKTB6uXU109FxB9ExE4R8aSJR/XKJElSK/QSNh4E3g9cAlxeHmtqFiVpcLy6pBYy//4Ho5fDKMcCT8/MH9UuRpIktU8vezbW0txtVZIkadZ62bOxEbgyIi6kuc084KmvkiSpN72Ejc+XhyRJ0qzNGDYyc9UgCpEkSe3UyxVEb6LLvVAyc7cqFUmSpFbp5TDKso7njwcOBbzOhiRJ6smMZ6Nk5n93PH6QmX8H7DuA2iRJUgv0chhlz46XP0ezp2OrahVJkqRW6eUwygc6nj8EfBf47SrVSJKk1unlbJQXD6IQSZLUTr0cRtkceCWwpHP8zHx3vbIkSVJb9HIY5QvA3TQ3YHtghnElSZIepZewsTgzl1evRJIktVIvN2L7WkT8UvVKJElqEW9f/4he9my8EHhduZLoA0AAmZnPrlqZJElqhV7CxoHVq5CkMbEyVrIiVwy7DGms9HLq6/cGUYgkSWqnXvpsSJIkzZlhQ5IkVWXYkCRJVRk2JElSVYYNSdJI83oV48+wIUmSqjJsSJKkqgwbkiSpKsOGJEmqyrAhSZKqMmxIklSJZ9I0DBuSJKkqw4YkSarKsCFJkqoybEiSpKoMG5IkqSrDhiRpwfOskboMG5IkqSrDhiRJqsqwIUnSPNQ+BNOGQzyGDUmSVJVhQ5IkVWXYkCRJVRk2JElSVUMLGxGxSUR8MyL+rbzeNSIujYgbIuIzEbFZad+8vF5Xhi/pmMY7S/v1EXFAR/vy0rYuIo4b9LJJkqRHDHPPxluB6zpenwCcmJlLgTuBo0r7UcCdmfl04MQyHhGxO3AY8CxgOfDREmA2AT4CHAjsDhxexpUkSUMwlLAREYuB3wQ+UV4HsC9wdhllFXBIeX5weU0Zvl8Z/2Dg05n5QGbeBKwD9iqPdZl5Y2Y+CHy6jCtJkoZgWHs2/g74M+Dh8np74K7MfKi8Xg/sXJ7vDNwMUIbfXcb/Wfuk90zVLkkLQud1GdpwjQaNv4GHjYh4OXB7Zl7e2dxl1Jxh2Gzbu9VydESsiYg1GzZsmKZqSZI0V8PYs/EC4KCI+C7NIY59afZ0bBsRm5ZxFgO3lOfrgV0AyvBtgDs62ye9Z6r2x8jMkzNzWWYuW7Ro0fyXTJImcc+CNISwkZnvzMzFmbmEpoPnVzLzNcCFwKvKaEcAXyjPV5fXlOFfycws7YeVs1V2BZYC3wAuA5aWs1s2K/NYPYBFkyRJXYzSdTbeARwbEeto+mScUtpPAbYv7ccCxwFk5lrgLOBbwJeAYzJzY+nX8SbgPJqzXc4q40qS1BP3SPXXpjOPUk9mXgRcVJ7fSHMmyeRxfgIcOsX7jweO79J+DnBOH0uVpJHhhrD9VsZKVuSKYZfRN6O0Z0OSFjyDhNrIsCFJkqoybEiSWmGU9wqNcm2DYNiQJElVGTYkSdMa9H/lC30vQBsZNiRJGrCFFqgMG5IkqSrDhiRJqsqwIUmSqjJsSFpwx4+l2XD9mD/Dxgjr1x+4K4qkcTDI7yq/FwfLsCFpzvzCHpz5ftb+rjRMhg1JklSVYUOSVI17VGa2ED4jw4YkjYiFsNEZdf4O6jBsSJJazQAxfIYNSQPjl75GgX+Hg2fYkCRJVRk2JI0s/wNtJ3+vC49hQ5IkVWXYkKQh8L97LSSGDUmaxCAwOvxdtINhQ5IkVWXYkCSNHPdotIthQ9LYc8MkjTbDhiQtQAY0DZJhQ5IkVWXYkBYo/7PVbPk3o7kybEiSpKoMG6rO/4YkLSR+5z2WYUOSJFVl2JBazP+wNFv+zcxstp+Rn6lhQ5KkKQ0zWLQppBg2JEmagzaFgdoMG5I0BTcm6reF+jdl2JAkSVUZNiRJUlWGDUnSvCyEQwMLYRlrMmxIkjQi2hpqDBuaUVv/+FWPfzOajXH5exmXOkeRYUOSpBHSxlBj2FBftXElkTR73b4L/H5YuAwbkqQ5GffwMO71jxPDhiRprBkaRp9hQ1I13rBK8+HfQ3sYNiRJUlWGjTFl4pckjQvDhiRp3vwH6NH8PB7NsCFppPglPTydn30/fw/+TmXYkKQFbhzDwDjW3E1blmMmhg1JrTIqX96jUoc0CgwbA+YXkKRBnRI8St83o1TLfLRlOQbNsCENiF9SkhYqw4YkqVUM9qNn4GEjInaJiAsj4rqIWBsRby3tT4qI8yPihvJzu9IeEXFSRKyLiKsjYs+OaR1Rxr8hIo7oaH9+RFxT3nNSRMSgl3NcuZJKmjDV90HbvifatjyjaBh7Nh4C/iQznwnsAxwTEbsDxwEXZOZS4ILyGuBAYGl5HA18DJpwAqwA9gb2AlZMBJQyztEd71s+gOWSpFZw46t+G3jYyMxbM/OK8vxe4DpgZ+BgYFUZbRVwSHl+MPDJbHwd2DYidgIOAM7PzDsy807gfGB5GbZ1Zl6SmQl8smNakiRpwIbaZyMilgDPAy4FnpyZt0ITSIAdy2g7Azd3vG19aZuufX2X9m7zPzoi1kTEmg0bNsx3cSQNiP95j69B/e78GxktQwsbEfFE4LPA2zLznulG7dKWc2h/bGPmyZm5LDOXLVq0aKaSJUnSHAwlbETE42iCxhmZ+bnSfFs5BEL5eXtpXw/s0vH2xcAtM7Qv7tIuSWoZ92CMh2GcjRLAKcB1mfnBjkGrgYkzSo4AvtDR/tpyVso+wN3lMMt5wP4RsV3pGLo/cF4Zdm9E7FPm9dqOaUkaE25zA3m/AAAL9klEQVRENMr8+5ydYezZeAHwe8C+EXFlebwMeB/w0oi4AXhpeQ1wDnAjsA74R+CPADLzDuA9wGXl8e7SBvBG4BPlPd8Bzh3EgkkaXW4cpOHZdNAzzMz/pHu/CoD9uoyfwDFTTOtU4NQu7WuAPeZRpqQhMhgMjp+1BsEriEot5AZEw+DfnaZi2NDI8Itq9Pk7kjQXhg1JGlPThb9xCIb9rHEclnchM2xIqmohbwTGadlHvdZRr0/TM2xIkgbO8LCwGDakBcAvdknDZNiQJElVGTZGkP+Fjr+F8Duc6zL2+7NZCJ+15sa/jdFh2GgJVypJ0qgybEjSgLTxn4I2LpP6z7AxZlyxJUnjxrAhjZmVsdLQWUnn5zqfz3gh/34W8rJraoYNSZonN7DS9Awbkh7FDefc+dlJ3Rk2xsRC+RIbx+UcVs3j+FlJWpgMG5KAhRleFtoyL7Tl1egwbEgaiKk2dP3aAE6eThs3rG1cJi0Mhg35BSZJqsqwIUmSqjJsSJqVUdkTNt86RmU5pjLq9UmzYdiQVEWtvhiSxo9hQ9JQ9CNEzGUaoxheRrEmqZ8MG5J64gZxdD+DUa1LmmDYkCqZaQPgBmLw/Myl4TBsSNIIMyCpDQwbC5RfYMPjZz97E5/ZMC8N7+9NmjvDhiSNAMOM2sywoZ75ZTh8/g5G32x+R/4+tVAYNiRpHgYdGAwoGkeGjRFQ68vDL6XB8zOXpMcybGhBGqVQ0M8rbY7Scg3DOF7ka9jzlwbBsNEybnAWnn7+vmttrIf5N+n6IA2fYWOI/BKcnYXweY3iMo5iTZLGi2FjjAzyS98NzHD5+UtqE8NGi3RuoNxYaUK3v4VhXyRL0sJi2BgAv9A1YZz+FgZ9vYhx+mwkzY5hQ+qTmmdCjMuGeFzqlDRYhg2pw+SN5ShsPId9tok0SvwbHk+GDY2ctv23Px/2w5HUBoYNTavfG7h+dUzsZx+Btm3E27Y8ksafYUNzMioXcprqsMdU857rXhM34JI0d4aNFmjjhrDfV0IdtctYt/F3JklTMWyMuTZstGYTLMZhecehxvlaCMsoqX8MGyOu7VcNrT1Pr/8gScNn2BiSNm7YaxlU3aN2qEWS2sKwscDVOquj7RthO5BKUu8MGwLmdpbGbM5Iqd3Z0429JI2uTYddwEIx243hqG08R62emhbSskrSILhno7JR3nDNtbaZ3jfKyyxJGjzDRiWj3NmwbWGgbcsjSW1j2JAkSVUZNiRJUlWGDUmSVFVrw0ZELI+I6yNiXUQcN+x6JElaqFoZNiJiE+AjwIHA7sDhEbH7cKt6hB0aJUkLSSvDBrAXsC4zb8zMB4FPAwcPuSZJkhaktoaNnYGbO16vL22SJGnAIjOHXUPfRcShwAGZ+fry+veAvTLzzZPGOxo4urx8BnB9j7PYAfhRn8qtYZTrs7a5s775GeX6rG1uRrk2GP36npGZWw1iRm29XPl6YJeO14uBWyaPlJknAyfPduIRsSYzl829vLpGuT5rmzvrm59Rrs/a5maUa4PxqG9Q82rrYZTLgKURsWtEbAYcBqweck2SJC1IrdyzkZkPRcSbgPOATYBTM3PtkMuSJGlBamXYAMjMc4BzKk1+1odeBmyU67O2ubO++Rnl+qxtbka5NrC+n2llB1FJkjQ62tpnQ5IkjYrMbP2D5syUC4HrgLXAW0v7k4DzgRvKz+1K+y8ClwAPAH86aVrfBa4BrgTWTDPPU4HbgWsntR9aangYWFahvm2Bs4H/KtP7lSnqW05zqu864LiO9jeVtqQ5bWuUajsFuAq4ukznGSNU2+nATeXv4krguSP22f1HR223AJ8fsfr2Ba4ArgVW0RziHUZ9Pa23/aqN5m/4yo7HPcDbRmGd7VNtVdbZPtV2OpXW2T7VV22d7VN9j1lnu73/Z+NPN7AtD2AnYM/yfCvg2zSXMf8/Ex8ecBxwQnm+I/DLwPF0Dxs79DDPXwf25LFfWs8sv+iLeCRs9LO+VcDry/PNgG271LYJ8B1gtzLOVcDuZdjzgCUTyzlitW3dMd4HyzxGpbbTgVdV/LubV32Txvss8NpRqY9mD+vNwC+U8d4NHDXo+maz3vaztkmfzw+Bp47KOtuH2qqts32o7XQqrrPzra/2Ojuf+phine02/YnHgjiMkpm3ZuYV5fm9NKlwZ5pLmK8qo60CDinj3J6ZlwE/ncc8Lwbu6NJ+XWZeP6mtL/VFxNY0X5anlPEezMy7upQ35eXcM/ObmfndEa3tnjKtALYA7hmV2roZpc+uY1pb0fxH8vkRqm974IHM/HYZ73zglUOor+f1ttJ3yn7AdzLze12GDXyd7VNtVdbZftTWzSh9dhNqrLN9qK/rOjvNPBZG2OgUEUto/hO4FHhyZt4KzR8ZTQqcSQJfjojLyxVIR6m+3YANwGkR8c2I+EREbNllvDldzn0UaouI02hS+C8CHxql2oDjI+LqiDgxIjbvfOOI1AfwW8AFExuBEanvR8DjImLi4kev4tEX5RtUfXPSh++UCYcBZ04xbBjrbF9qq7TO9qU26q2z/aoP6qyz861vxnV2sgUVNiLiiTS7o942+Rc3Cy/IzD1p7ih7TET8+gjVtynNLuCPZebzgPtodqk9ZlZd2nIcasvMI4Gfp0n0vzNCtb2T5sv0l2mOn77jZ28ajfomHM6kL5Zh15eZSfOFd2JEfAO4F3hoCPXNWp++UygXHzwI+OepRunSVnud7UttldbZftRWc53tR30Taqyz86pvpnW2mwUTNiLicTS/nDMy83Ol+baI2KkM34mmY9i0MvOW8vN24F+AvSJil4i4sjzeMMT61gPrM/PS8vpsYM8u9fV0OfdRrS0zNwKfAV45KrWV3ZuZmQ8Ap9Hsfhypzy4iti91fbGjbSTqy8xLMvPXMnMv4GKajm6Drm9W+vWdUhwIXJGZt5X3jsI627faKqyz866t8jo77/rK+LXW2XnXN9U6O5XWXtSrU0QEzfHa6zLzgx2DVgNHAO8rP78ww3S2BH4uM+8tz/cH3p2ZN9P0ZB5qfZn5w4i4OSKekc3x5f2Ab02uLyI2pVzOHfgBTUJ99SjXVup4WmauK89fQXNmwdBrK8N2ysxbS22HANeOymfXMalDgX/LzJ+U8UemvojYMTNvj2ZX9jtodm8PtL7Z6FdtHR713+sorLPzra3mOjvf2sqwautsP+orqqyz/aiv2zo77Zxymt6jbXkAL6TZNXU1j5zq8zKaTi4X0CSyC4AnlfGfQpPo7gHuKs+3pjn2e1V5rAX+Ypp5ngncStMxZz2lpy7N8bf1NKci3UZzSfW+1FeGPRdYU6b1ecopUF3qexlNT+bvdC4H8JYyvYdoEuy/jkJtNHvhvkpz2vG1wBk0YW/otZX2r3TU9k/AE0fp91qGXQQs7/d60afP7/00u9mvp5yCN6T6el1vL+1jbU8A/hvYZobvsWGss3Oujfrr7Hw/t9rr7LzqG8A6O9/P7zHr7HQPryAqSZKqWjB9NiRJ0nAYNiRJUlWGDUmSVJVhQ5IkVWXYkCRJVRk2JPVdRGwsFwZaGxFXRcSxETHt901ELImIrteOkDTeDBuSavhxZj43M58FvJTmXP0VM7xnCVNcqErSePM6G5L6LiL+JzOf2PF6N+AymlugPxX4FDBxM7Q3ZebXIuLrNLdyv4nmzpUn0VwR8UXA5sBHMvPjA1sISX1j2JDUd5PDRmm7k+bGV/cCD2fmTyJiKXBmZi6LiBcBf5qZLy/jHw3smJnvLZdE/ipwaGbeNNCFkTRvC+LeKJJGwsQdJB8HfDgingtsBH5hivH3B54dEa8qr7cBltLs+ZA0Rgwbkqorh1E20tyNcgXN/UWeQ9Nv7CdTvQ14c2aeN5AiJVVjB1FJVUXEIuAfgA9nc9x2G+DWzHwY+D1gkzLqvcBWHW89D3hjuaU2EfEL5W7LksaMezYk1bBFRFxJc8jkIZoOoRO3xP4o8NmIOBS4ELivtF8NPBQRVwGnA39Pc4bKFeXW2htobgUuaczYQVSSJFXlYRRJklSVYUOSJFVl2JAkSVUZNiRJUlWGDUmSVJVhQ5IkVWXYkCRJVRk2JElSVf8fZ279RdNu2XEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49412266a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure \n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Add x-axis and y-axis\n",
    "ax.bar(df_master.timestamp.values,\n",
    "       df_master['favorite_count'],\n",
    "       color='purple')\n",
    "\n",
    "# Set title and labels for axes\n",
    "ax.set(xlabel=\"Date\",\n",
    "       ylabel=\"number of favorite count\",\n",
    "       title=\"likes through time\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4941e157b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure \n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Add x-axis and y-axis\n",
    "ax.bar(df_master.timestamp.values,\n",
    "       df_master['retweet_count'],\n",
    "       color='red')\n",
    "\n",
    "# Set title and labels for axes\n",
    "ax.set(xlabel=\"Date\",\n",
    "       ylabel=\"number of retweet count\",\n",
    "       title=\"retweets through time\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>first_prediction_type</th>\n",
       "      <th>first_prediction_confidence</th>\n",
       "      <th>first_prediction_result</th>\n",
       "      <th>second_prediction_type</th>\n",
       "      <th>second_prediction_confidence</th>\n",
       "      <th>second_prediction_result</th>\n",
       "      <th>third_prediction_type</th>\n",
       "      <th>third_prediction_confidence</th>\n",
       "      <th>third_prediction_result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39467</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.07611</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  timestamp  \\\n",
       "0  892420643555336193 2017-08-01   \n",
       "\n",
       "                                                                               source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                            text  \\\n",
       "0  This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU   \n",
       "\n",
       "                                                     expanded_urls  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator     name dog_stage  favorite_count  \\\n",
       "0  13                10                  Phineas  NaN       39467            \n",
       "\n",
       "            ...            img_num first_prediction_type  \\\n",
       "0           ...            1        orange                 \n",
       "\n",
       "   first_prediction_confidence first_prediction_result  \\\n",
       "0  0.097049                     False                    \n",
       "\n",
       "   second_prediction_type  second_prediction_confidence  \\\n",
       "0  bagel                   0.085851                       \n",
       "\n",
       "  second_prediction_result  third_prediction_type  \\\n",
       "0  False                    banana                  \n",
       "\n",
       "   third_prediction_confidence third_prediction_result  \n",
       "0  0.07611                      False                   \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4941646cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#is there a relation between the favorite_count and retweet_count?\n",
    "df_master.plot(x='favorite_count', y='retweet_count', kind='scatter');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49412c3710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# is there a relation between favorite count and rating?\n",
    "#filtering by important conditions to prevent misleading\n",
    "df_rating=df_master.query('(15>rating_numerator > 6) and (rating_denominator == 10)')\n",
    "\n",
    "df_rating.plot(x='rating_numerator', y='favorite_count', kind='scatter');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "769    79515\n",
       "Name: retweet_count, dtype: int64"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding most tweeted and liked post.\n",
    "df_master.query('favorite_count==favorite_count.max()').favorite_count\n",
    "df_master.query('retweet_count==retweet_count.max()').retweet_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',\n",
       "       'rating_numerator', 'rating_denominator', 'name', 'dog_stage',\n",
       "       'favorite_count', 'retweet_count', 'jpg_url', 'img_num',\n",
       "       'first_prediction_type', 'first_prediction_confidence',\n",
       "       'first_prediction_result', 'second_prediction_type',\n",
       "       'second_prediction_confidence', 'second_prediction_result',\n",
       "       'third_prediction_type', 'third_prediction_confidence',\n",
       "       'third_prediction_result'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_master.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('golden_retriever', 137),\n",
       " ('Labrador_retriever', 94),\n",
       " ('Pembroke', 88),\n",
       " ('Chihuahua', 78),\n",
       " ('pug', 54),\n",
       " ('chow', 41)]"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "lst=Counter(df_master['first_prediction_type']).most_common()[:6] \n",
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49404da6d8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "locations = range(len(lst))\n",
    "heights=[lst[0][1], lst[1][1], lst[2][1],lst[3][1], lst[4][1], lst[5][1]]\n",
    "labels=[lst[0][0], lst[1][0], lst[2][0], lst[3][0], lst[4][0], lst[5][0]]\n",
    "\n",
    "plt.bar(locations, heights)\n",
    "plt.xticks(locations, labels, rotation=45);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_true=df_master.query('first_prediction_result == True')\n",
    "filtered_true.first_prediction_type.nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12361.131386861314"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfo= filtered_true.groupby('first_prediction_type').favorite_count.mean()\n",
    "dfo.golden_retriever"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f493e5f9278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean1=dfo.golden_retriever\n",
    "mean2=dfo.Labrador_retriever\n",
    "mean3=dfo.Pembroke\n",
    "mean4=dfo.Chihuahua\n",
    "mean5=dfo.pug\n",
    "mean6=dfo.chow\n",
    "heights=[mean1, mean2, mean3, mean4, mean5, mean6]\n",
    "locations = range(len(lst))\n",
    "labels=['golden_retriever', 'Labrador_retriever', 'Pembroke', 'Chihuahua', 'pug', 'chow']\n",
    "plt.bar(locations, heights)\n",
    "plt.xticks(locations, labels, rotation=45);\n",
    "# .favorite_count.mean().plot(kind='bar', color= 'blue', figsize=(3,3));"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}