wrangle_act.ipynb

{
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   "source": [
    "# Project: Wrangle and Analyze Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Gathering\n",
    "In this section, we will gather all the three datasets for our analysis. The methods for the process are different.\n",
    "#### 1. Directly download the WeRateDogs Twitter archive data (twitter_archive_enhanced.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "# Importing all the libraries for our analysis\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import requests \n",
    "import tweepy\n",
    "import time\n",
    "\n",
    "# This will be used to load the json data\n",
    "import json\n",
    "import os\n",
    "\n",
    "# This will handle all regular expresiions issue\n",
    "import re\n",
    "\n",
    "from IPython.display import HTML\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Setting the style of the plots\n",
    "sns.set(style='darkgrid', font_scale = 1.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive = pd.read_csv('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. We will use the Requests library to download the tweet image prediction (image_predictions.tsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "\n",
    "response = requests.get(url)\n",
    "\n",
    "# Saving the image-prediction file\n",
    "with open('image-predictions.tsv', 'wb') as file:\n",
    "    file.write(response.content) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the image-prediction file for our analysis\n",
    "img_predictions = pd.read_csv('image-predictions.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "img_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
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   ],
   "source": [
    "twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Finding out more on my image prediction data\n",
    "img_predictions.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Based on the information we have currently, we can't see the favorite counts (likes) and the retweet counts. As a result, We will use the Tweepy library to query additional data via the Twitter API (tweet_json.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Setting up API credentials for the Tweepy API scraping process\n",
    "consumer_key = '*******************************'\n",
    "consumer_secret = '*******************************'\n",
    "access_token = '**************************************************************'\n",
    "access_secret = '*******************************'\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "api = tweepy.API(auth, parser=tweepy.parsers.JSONParser(), \n",
    "                  wait_on_rate_limit = True)\n",
    "# , wait_on_rate_limit_notify =True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a list of tweet ids from the enhanced archive data.\n",
    "tweet_id = twitter_archive.tweet_id\n",
    "list(tweet_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Getting the additional information (favorites and retweet_counts) using the tweepy API for the tweet ids listed in the previous cell.\n",
    "\n",
    "> **Note**: There are chances that some tweet ids might not be found by the API and this could be because the tweet is deleted. To take of this possibility, we would be using the try and except function for the API get.\n",
    "While the **try** function will get the favorites and retweet_counts of all available tweet ids, the except function will fetch a list of tweet ids that are not found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting additional data using the twitter API for the ids in the tweet_id variable.\n",
    "\n",
    "# New list fot the twitter archive.\n",
    "json_list = []\n",
    "\n",
    "# We also need to create an error list for tweet ids that might not be found by the API which are on the tweet id list\n",
    "error_list = []\n",
    "\n",
    "# Creating a for loop to get the favorite and retweet counts of all the tweet ids in the tweet_id list.\n",
    "for id in tweet_id:\n",
    "    try:\n",
    "        tweet_page = api.get_status(id, tweet_mode = 'extended')\n",
    "        favorites = tweet_page['favorite_count']\n",
    "        retweet_count = tweet_page['retweet_count']\n",
    "        json_list.append({'tweet_id': int(id),\n",
    "                         'favorites': int(favorites),\n",
    "                         'retweet_count': int(retweet_count)\n",
    "                        })\n",
    "        print(id, favorite, retweet_count)\n",
    "    except Exception as e:\n",
    "        error_list.append(id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# json_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# error_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>In order to be sure that the exception list is actually unavailable, we will re-iterate the process, but this time, with the error list generated in the last iteration.</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "new_error_list = []\n",
    "\n",
    "for error in error_list:\n",
    "    try:\n",
    "        data = api.get_status(error, tweet_mode = 'extended')\n",
    "        favorites = data['favorite_count']\n",
    "        retweet_count = data['retweet_count']\n",
    "        new_list.append({'tweet_id': int(error),\n",
    "                         'favorites': int(favorites),\n",
    "                         'retweet_count': int(retweet_count)\n",
    "                        })\n",
    "        print(error, favorites, retweet_count)\n",
    "    except Exception as e:\n",
    "        new_error_list.append(error)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a pandas dataframe for our new list data\n",
    "json_data = pd.DataFrame(json_list, columns = ['tweet_id', 'favorites', 'retweet_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicates = json_data[json_data.duplicated(keep='first')]\n",
    "duplicates1 = json_data[json_data.duplicated(keep='last')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicates.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicates1.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exporting the data to tweet_json txt file \n",
    "json_data.to_csv('tweet_json.txt', encoding = 'utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the data for further analysis\n",
    "\n",
    "json_df = pd.read_csv('tweet_json.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>33812</td>\n",
       "      <td>7009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>29329</td>\n",
       "      <td>5302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>22055</td>\n",
       "      <td>3481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>36940</td>\n",
       "      <td>7226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>35311</td>\n",
       "      <td>7761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  favorites  retweet_count\n",
       "0  892420643555336193      33812           7009\n",
       "1  892177421306343426      29329           5302\n",
       "2  891815181378084864      22055           3481\n",
       "3  891689557279858688      36940           7226\n",
       "4  891327558926688256      35311           7761"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2325 entries, 0 to 2324\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count  Dtype\n",
      "---  ------         --------------  -----\n",
      " 0   tweet_id       2325 non-null   int64\n",
      " 1   favorites      2325 non-null   int64\n",
      " 2   retweet_count  2325 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 54.6 KB\n"
     ]
    }
   ],
   "source": [
    "# More information about the json tweet data\n",
    "json_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": 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",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "img_predictions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": 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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>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>2336</th>\n",
       "      <td>666273097616637952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 15:14:19 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Can take selfies 11/10 https://t.co/ws2AMaNwPW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666273097...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>666268910803644416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 14:57:41 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Very concerned about fellow dog trapped in com...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666268910...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2338</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 04:02:55 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Not familiar with this breed. No tail (weird)....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666104133...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339</th>\n",
       "      <td>666102155909144576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 03:55:04 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Oh my. Here you are seeing an Adobe Setter giv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666102155...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2340</th>\n",
       "      <td>666099513787052032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 03:44:34 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Can stand on stump for what seems like a while...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666099513...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2341</th>\n",
       "      <td>666094000022159362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 03:22:39 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This appears to be a Mongolian Presbyterian mi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666094000...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2342</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 02:38:37 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a well-established sunblockerspan...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666082916...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2343</th>\n",
       "      <td>666073100786774016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:59:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Let's hope this flight isn't Malaysian (lol). ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666073100...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2344</th>\n",
       "      <td>666071193221509120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:52:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a northern speckled Rhododendron....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666071193...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2345</th>\n",
       "      <td>666063827256086533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:22:45 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is the happiest dog you will ever see. Ve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666063827...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>the</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2346</th>\n",
       "      <td>666058600524156928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:01:59 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is the Rand Paul of retrievers folks! He'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666058600...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>the</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:55:59 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>My oh my. This is a rare blond Canadian terrie...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666057090...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:49:46 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a Siberian heavily armored polar bear ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666055525...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:35:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is an odd dog. Hard on the outside but lo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666051853...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>an</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:30:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a truly beautiful English Wilson Staff...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666050758...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "2336  666273097616637952                    NaN                  NaN   \n",
       "2337  666268910803644416                    NaN                  NaN   \n",
       "2338  666104133288665088                    NaN                  NaN   \n",
       "2339  666102155909144576                    NaN                  NaN   \n",
       "2340  666099513787052032                    NaN                  NaN   \n",
       "2341  666094000022159362                    NaN                  NaN   \n",
       "2342  666082916733198337                    NaN                  NaN   \n",
       "2343  666073100786774016                    NaN                  NaN   \n",
       "2344  666071193221509120                    NaN                  NaN   \n",
       "2345  666063827256086533                    NaN                  NaN   \n",
       "2346  666058600524156928                    NaN                  NaN   \n",
       "2347  666057090499244032                    NaN                  NaN   \n",
       "2348  666055525042405380                    NaN                  NaN   \n",
       "2349  666051853826850816                    NaN                  NaN   \n",
       "2350  666050758794694657                    NaN                  NaN   \n",
       "2351  666049248165822465                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "2355  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "2336  2015-11-16 15:14:19 +0000   \n",
       "2337  2015-11-16 14:57:41 +0000   \n",
       "2338  2015-11-16 04:02:55 +0000   \n",
       "2339  2015-11-16 03:55:04 +0000   \n",
       "2340  2015-11-16 03:44:34 +0000   \n",
       "2341  2015-11-16 03:22:39 +0000   \n",
       "2342  2015-11-16 02:38:37 +0000   \n",
       "2343  2015-11-16 01:59:36 +0000   \n",
       "2344  2015-11-16 01:52:02 +0000   \n",
       "2345  2015-11-16 01:22:45 +0000   \n",
       "2346  2015-11-16 01:01:59 +0000   \n",
       "2347  2015-11-16 00:55:59 +0000   \n",
       "2348  2015-11-16 00:49:46 +0000   \n",
       "2349  2015-11-16 00:35:11 +0000   \n",
       "2350  2015-11-16 00:30:50 +0000   \n",
       "2351  2015-11-16 00:24:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "2355  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "2336  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2337  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2338  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2339  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2340  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2341  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2342  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2343  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2344  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2345  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2346  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2347  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2348  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2349  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2350  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "2336     Can take selfies 11/10 https://t.co/ws2AMaNwPW                  NaN   \n",
       "2337  Very concerned about fellow dog trapped in com...                  NaN   \n",
       "2338  Not familiar with this breed. No tail (weird)....                  NaN   \n",
       "2339  Oh my. Here you are seeing an Adobe Setter giv...                  NaN   \n",
       "2340  Can stand on stump for what seems like a while...                  NaN   \n",
       "2341  This appears to be a Mongolian Presbyterian mi...                  NaN   \n",
       "2342  Here we have a well-established sunblockerspan...                  NaN   \n",
       "2343  Let's hope this flight isn't Malaysian (lol). ...                  NaN   \n",
       "2344  Here we have a northern speckled Rhododendron....                  NaN   \n",
       "2345  This is the happiest dog you will ever see. Ve...                  NaN   \n",
       "2346  Here is the Rand Paul of retrievers folks! He'...                  NaN   \n",
       "2347  My oh my. This is a rare blond Canadian terrie...                  NaN   \n",
       "2348  Here is a Siberian heavily armored polar bear ...                  NaN   \n",
       "2349  This is an odd dog. Hard on the outside but lo...                  NaN   \n",
       "2350  This is a truly beautiful English Wilson Staff...                  NaN   \n",
       "2351  Here we have a 1949 1st generation vulpix. Enj...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "2336                       NaN                        NaN   \n",
       "2337                       NaN                        NaN   \n",
       "2338                       NaN                        NaN   \n",
       "2339                       NaN                        NaN   \n",
       "2340                       NaN                        NaN   \n",
       "2341                       NaN                        NaN   \n",
       "2342                       NaN                        NaN   \n",
       "2343                       NaN                        NaN   \n",
       "2344                       NaN                        NaN   \n",
       "2345                       NaN                        NaN   \n",
       "2346                       NaN                        NaN   \n",
       "2347                       NaN                        NaN   \n",
       "2348                       NaN                        NaN   \n",
       "2349                       NaN                        NaN   \n",
       "2350                       NaN                        NaN   \n",
       "2351                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "2336  https://twitter.com/dog_rates/status/666273097...                11   \n",
       "2337  https://twitter.com/dog_rates/status/666268910...                10   \n",
       "2338  https://twitter.com/dog_rates/status/666104133...                 1   \n",
       "2339  https://twitter.com/dog_rates/status/666102155...                11   \n",
       "2340  https://twitter.com/dog_rates/status/666099513...                 8   \n",
       "2341  https://twitter.com/dog_rates/status/666094000...                 9   \n",
       "2342  https://twitter.com/dog_rates/status/666082916...                 6   \n",
       "2343  https://twitter.com/dog_rates/status/666073100...                10   \n",
       "2344  https://twitter.com/dog_rates/status/666071193...                 9   \n",
       "2345  https://twitter.com/dog_rates/status/666063827...                10   \n",
       "2346  https://twitter.com/dog_rates/status/666058600...                 8   \n",
       "2347  https://twitter.com/dog_rates/status/666057090...                 9   \n",
       "2348  https://twitter.com/dog_rates/status/666055525...                10   \n",
       "2349  https://twitter.com/dog_rates/status/666051853...                 2   \n",
       "2350  https://twitter.com/dog_rates/status/666050758...                10   \n",
       "2351  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator  name doggo floofer pupper puppo  \n",
       "2336                  10  None  None    None   None  None  \n",
       "2337                  10  None  None    None   None  None  \n",
       "2338                  10  None  None    None   None  None  \n",
       "2339                  10  None  None    None   None  None  \n",
       "2340                  10  None  None    None   None  None  \n",
       "2341                  10  None  None    None   None  None  \n",
       "2342                  10  None  None    None   None  None  \n",
       "2343                  10  None  None    None   None  None  \n",
       "2344                  10  None  None    None   None  None  \n",
       "2345                  10   the  None    None   None  None  \n",
       "2346                  10   the  None    None   None  None  \n",
       "2347                  10     a  None    None   None  None  \n",
       "2348                  10     a  None    None   None  None  \n",
       "2349                  10    an  None    None   None  None  \n",
       "2350                  10     a  None    None   None  None  \n",
       "2351                  10  None  None    None   None  None  \n",
       "2352                  10     a  None    None   None  None  \n",
       "2353                  10     a  None    None   None  None  \n",
       "2354                  10     a  None    None   None  None  \n",
       "2355                  10  None  None    None   None  None  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the last 20 rows of the twitter archive \n",
    "twitter_archive.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 181 retweets\n"
     ]
    }
   ],
   "source": [
    "# Checking the number of retweets in our twitter archive\n",
    "retweets = twitter_archive.retweeted_status_id.value_counts().sum()\n",
    "print('There are {} retweets'.format(retweets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 78 replies\n"
     ]
    }
   ],
   "source": [
    "# Checking the number of replies in the twitter archive\n",
    "replies = twitter_archive.in_reply_to_status_id.value_counts().sum()\n",
    "print('There are {} replies'.format(replies))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 28,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## Assessing Data\n",
    "In this section, we will detect and document at least **eight (8) quality issues and two (2) tidiness issue** using **both** visual and programmatic assessements to assess the data.\n",
    "\n",
    "* Also, we only want original ratings (no retweets) that have images. Though there are 5000+ tweets in the dataset, not all are dog ratings and some are retweets.\n",
    ".\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality issues\n",
    "1. The Twitter Archive (twitter_archive) has 2356 entries while the image prediction (img_predictions) data has only 2075 entries. This could be as a result of retweet values and replies which the img_predictions do not have.\n",
    "\n",
    "2. The rating_denominator in the twitter_archive is not consistent. We have a value as low as 6 and one as high as 170. Based on provided information, the rating denominator should be 10 for all entries.\n",
    "\n",
    "3. The rating numerator contains outrageous values\n",
    "\n",
    "4. timestamp and retweeted_status_timestamp should be datetime format instead of object.\n",
    "\n",
    "5. timestamp column has +0000 which is not necessary\n",
    "\n",
    "6. The datatypes are not consistent for some of the columns\n",
    "\n",
    "7. Some dog names are not correct like a, an, the\n",
    "\n",
    "8. Our dataset contains 181 retweets and 78 replies which are not needed for our analysis, since we are focusing on actual tweets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 7,
        "hidden": false,
        "row": 40,
        "width": 12
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "### Tidiness issues\n",
    "9. The three tables need to be merged into one dataframe the purpose of our analysis.\n",
    "\n",
    "10. The variables, doggo, floofer, pupper, puppo need to be in a single column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 32,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## Cleaning Data\n",
    "In this section, we will clean **all** of the issues documented during the assessment. \n",
    "\n",
    "As always advised, we will make a copy of our original datasets before starting the cleaning process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of the original datasets\n",
    "cleaned_archive = twitter_archive.copy()\n",
    "cleaned_json = json_df.copy()\n",
    "cleaned_image = img_predictions.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #9: The three tables need to be merged into one dataframe the purpose of our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define:\n",
    "\n",
    "The three tables will be merged in order for us to carry out further analysis. This will make it possible for us to analyse the data once."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merging the archive and json datasets into one using the tweet_id as our primary key\n",
    "merge_archive_json = pd.merge(cleaned_archive, cleaned_json, on=['tweet_id','tweet_id'],how='left') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>112.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2293.0</td>\n",
       "      <td>423.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "2355  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "2355  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator  name doggo floofer pupper puppo  favorites  \\\n",
       "2354                  10     a  None    None   None  None      112.0   \n",
       "2355                  10  None  None    None   None  None     2293.0   \n",
       "\n",
       "      retweet_count  \n",
       "2354           39.0  \n",
       "2355          423.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirming if the merging worked fine\n",
    "merge_archive_json.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will now merge the image prediction table with the merge_archive_json\n",
    "new_merged_df = pd.merge(merge_archive_json, cleaned_image, on=['tweet_id', 'tweet_id'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>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>...</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>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>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <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>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  ...  img_num         p1  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...  ...      1.0     orange   \n",
       "1  https://twitter.com/dog_rates/status/892177421...  ...      1.0  Chihuahua   \n",
       "\n",
       "    p1_conf p1_dog        p2   p2_conf p2_dog        p3   p3_conf p3_dog  \n",
       "0  0.097049  False     bagel  0.085851  False    banana  0.076110  False  \n",
       "1  0.323581   True  Pekinese  0.090647   True  papillon  0.068957   True  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_merged_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In order to view the entire column at once, we will need to set max column to the maximum number of columns our dataframe has\n",
    "# We have about 30 columns in our dataset, so we will set max columns to 30\n",
    "pd.set_option('display.max_columns', 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        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",
       "      <th>favorites</th>\n",
       "      <th>retweet_count</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",
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       "      <th>0</th>\n",
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       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>33812.0</td>\n",
       "      <td>7009.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>29329.0</td>\n",
       "      <td>5302.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "\n",
       "   rating_denominator     name doggo floofer pupper puppo  favorites  \\\n",
       "0                  10  Phineas  None    None   None  None    33812.0   \n",
       "1                  10    Tilly  None    None   None  None    29329.0   \n",
       "\n",
       "   retweet_count                                          jpg_url  img_num  \\\n",
       "0         7009.0  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg      1.0   \n",
       "1         5302.0  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg      1.0   \n",
       "\n",
       "          p1   p1_conf p1_dog        p2   p2_conf p2_dog        p3   p3_conf  \\\n",
       "0     orange  0.097049  False     bagel  0.085851  False    banana  0.076110   \n",
       "1  Chihuahua  0.323581   True  Pekinese  0.090647   True  papillon  0.068957   \n",
       "\n",
       "  p3_dog  \n",
       "0  False  \n",
       "1   True  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_merged_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will make a copy of the new merged dataframe before we begin our cleaning operations\n",
    "merged_df = new_merged_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #1: The Twitter Archive (twitter_archive) has 2356 entries while the image prediction (img_predictions) data has only 2075 entries. This could be as a result of retweet values and replies which the img_predictions do not have."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: \n",
    "\n",
    " We will remove all rows that do not contain images. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2075\n",
       "True      281\n",
       "Name: jpg_url, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.jpg_url.isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df= merged_df[merged_df.jpg_url.notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2075\n",
       "Name: jpg_url, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.jpg_url.isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
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       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>29329.0</td>\n",
       "      <td>5302.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "\n",
       "   rating_denominator     name doggo floofer pupper puppo  favorites  \\\n",
       "0                  10  Phineas  None    None   None  None    33812.0   \n",
       "1                  10    Tilly  None    None   None  None    29329.0   \n",
       "\n",
       "   retweet_count                                          jpg_url  img_num  \\\n",
       "0         7009.0  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg      1.0   \n",
       "1         5302.0  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg      1.0   \n",
       "\n",
       "          p1   p1_conf p1_dog        p2   p2_conf p2_dog        p3   p3_conf  \\\n",
       "0     orange  0.097049  False     bagel  0.085851  False    banana  0.076110   \n",
       "1  Chihuahua  0.323581   True  Pekinese  0.090647   True  papillon  0.068957   \n",
       "\n",
       "  p3_dog  \n",
       "0  False  \n",
       "1   True  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #2: The rating_denominator in the twitter_archive is not consistent. We have a value as low as 6 and one as high as 170. Based on provided information, the rating denominator should be 10 for all entries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "### Define\n",
    "\n",
    "We will have to correct the rating values by looking at the content of the text column for more information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2057\n",
       "50        3\n",
       "11        2\n",
       "80        2\n",
       "70        1\n",
       "7         1\n",
       "150       1\n",
       "170       1\n",
       "20        1\n",
       "90        1\n",
       "40        1\n",
       "130       1\n",
       "110       1\n",
       "120       1\n",
       "2         1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": 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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye in Vietnam (?). Big fan of relaxing on stair. 8/10 would pet https://t.co/BLDqew2Ijj</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2075 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                    text\n",
       "0                                                          This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU\n",
       "1     This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV\n",
       "2                      This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB\n",
       "3                                                                This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ\n",
       "4     This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f\n",
       "...                                                                                                                                                                  ...\n",
       "2351                                            Here we have a 1949 1st generation vulpix. Enjoys sweat tea and Fox News. Cannot be phased. 5/10 https://t.co/4B7cOc1EDq\n",
       "2352                           This is a purebred Piers Morgan. Loves to Netflix and chill. Always looks like he forgot to unplug the iron. 6/10 https://t.co/DWnyCjf2mx\n",
       "2353                                  Here is a very happy pup. Big fan of well-maintained decks. Just look at that tongue. 9/10 would cuddle af https://t.co/y671yMhoiR\n",
       "2354                         This is a western brown Mitsubishi terrier. Upset about leaf. Actually 2 dogs here. 7/10 would walk the shit out of https://t.co/r7mOb2m0UI\n",
       "2355                                 Here we have a Japanese Irish Setter. Lost eye in Vietnam (?). Big fan of relaxing on stair. 8/10 would pet https://t.co/BLDqew2Ijj\n",
       "\n",
       "[2075 rows x 1 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assessing the full text length to find out more about the ratings\n",
    "pd.set_option('display.max_colwidth', 300)\n",
    "pd.DataFrame(merged_df.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We will focus on the tweets and their rating denominators. Also, considering tha the texts contain the actual ratings, we will be using it to confirm the ratings of the inconsistent rating denominators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_rating = merged_df[['tweet_id', 'text', 'rating_numerator','rating_denominator']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>820690176645140481</td>\n",
       "      <td>The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd</td>\n",
       "      <td>84</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>810984652412424192</td>\n",
       "      <td>Meet Sam. She smiles 24/7 &amp;amp; secretly aspires to be a reindeer. \\nKeep Sam smiling by clicking and sharing this link:\\nhttps://t.co/98tB8y7y7t https://t.co/LouL5vdvxx</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>758467244762497024</td>\n",
       "      <td>Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE</td>\n",
       "      <td>165</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>740373189193256964</td>\n",
       "      <td>After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>731156023742988288</td>\n",
       "      <td>Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>722974582966214656</td>\n",
       "      <td>Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>716439118184652801</td>\n",
       "      <td>This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1228</th>\n",
       "      <td>713900603437621249</td>\n",
       "      <td>Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1</td>\n",
       "      <td>99</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>710658690886586372</td>\n",
       "      <td>Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>709198395643068416</td>\n",
       "      <td>From left to right:\\nCletus, Jerome, Alejandro, Burp, &amp;amp; Titson\\nNone know where camera is. 45/50 would hug all at once https://t.co/sedre1ivTK</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>704054845121142784</td>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa</td>\n",
       "      <td>60</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>697463031882764288</td>\n",
       "      <td>Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>684225744407494656</td>\n",
       "      <td>Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3</td>\n",
       "      <td>143</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1635</th>\n",
       "      <td>684222868335505415</td>\n",
       "      <td>Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55</td>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>682962037429899265</td>\n",
       "      <td>This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1779</th>\n",
       "      <td>677716515794329600</td>\n",
       "      <td>IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq</td>\n",
       "      <td>144</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>675853064436391936</td>\n",
       "      <td>Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw</td>\n",
       "      <td>88</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  \\\n",
       "433   820690176645140481   \n",
       "516   810984652412424192   \n",
       "902   758467244762497024   \n",
       "1068  740373189193256964   \n",
       "1120  731156023742988288   \n",
       "1165  722974582966214656   \n",
       "1202  716439118184652801   \n",
       "1228  713900603437621249   \n",
       "1254  710658690886586372   \n",
       "1274  709198395643068416   \n",
       "1351  704054845121142784   \n",
       "1433  697463031882764288   \n",
       "1634  684225744407494656   \n",
       "1635  684222868335505415   \n",
       "1662  682962037429899265   \n",
       "1779  677716515794329600   \n",
       "1843  675853064436391936   \n",
       "2335  666287406224695296   \n",
       "\n",
       "                                                                                                                                                                           text  \\\n",
       "433                                                                         The floofs have been released I repeat the floofs have been released. 84/70 https://t.co/NIYC820tmd   \n",
       "516   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",
       "902                                                                                              Why does this never happen at my front door... 165/150 https://t.co/HmwrdfEfUE   \n",
       "1068                               After so many requests, this is Bretagne. She was the last surviving 9/11 search dog, and our second ever 14/10. RIP https://t.co/XAVDNDaVgQ   \n",
       "1120                                                  Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv   \n",
       "1165                                                                                                           Happy 4/20 from the squad! 13/10 for all https://t.co/eV1diwds8a   \n",
       "1202                                                      This is Bluebert. He just saw that both #FinalFur match ups are split 50/50. Amazed af. 11/10 https://t.co/Kky1DPG4iq   \n",
       "1228                                                                              Happy Saturday here's 9 puppers on a bench. 99/90 good work everybody https://t.co/mpvaVxKmc1   \n",
       "1254                                                               Here's a brigade of puppers. All look very prepared for whatever happens next. 80/80 https://t.co/0eb7R1Om12   \n",
       "1274                         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",
       "1351                                                                                         Here is a whole flock of puppers.  60/50 I'll take the lot https://t.co/9dpcw6MdWa   \n",
       "1433                                                                               Happy Wednesday here's a bucket of pups. 44/40 would pet all at once https://t.co/HppvrYuamZ   \n",
       "1634                                             Two sneaky puppers were not initially seen, moving the rating to 143/130. Please forgive us. Thank you https://t.co/kRK51Y5ac3   \n",
       "1635                               Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55   \n",
       "1662                                This is Darrel. He just robbed a 7/11 and is in a high speed police chase. Was just spotted by the helicopter 10/10 https://t.co/7EsP8LmSp5   \n",
       "1779                                                                                                     IT'S PUPPERGEDDON. Total of 144/120 ...I think https://t.co/ZanVtAtvIq   \n",
       "1843                                                                Here we have an entire platoon of puppers. Total score: 88/80 would pet all at once https://t.co/y93p6FLvVw   \n",
       "2335                                   This is an Albanian 3 1/2 legged  Episcopalian. Loves well-polished hardwood flooring. Penis on the collar. 9/10 https://t.co/d9NcXFKwLv   \n",
       "\n",
       "      rating_numerator  rating_denominator  \n",
       "433                 84                  70  \n",
       "516                 24                   7  \n",
       "902                165                 150  \n",
       "1068                 9                  11  \n",
       "1120               204                 170  \n",
       "1165                 4                  20  \n",
       "1202                50                  50  \n",
       "1228                99                  90  \n",
       "1254                80                  80  \n",
       "1274                45                  50  \n",
       "1351                60                  50  \n",
       "1433                44                  40  \n",
       "1634               143                 130  \n",
       "1635               121                 110  \n",
       "1662                 7                  11  \n",
       "1779               144                 120  \n",
       "1843                88                  80  \n",
       "2335                 1                   2  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetching rating denon\n",
    "tweet_rating.query('rating_denominator !=10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4> We will correct the values that are closer to the actual expected rating. For the outragious values, they appear to be a cumulative rating from different users</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we don't have a single value for the numerator, we will manually fit the correct values.\n",
    "merged_df.loc[merged_df.tweet_id == 740373189193256964, 'rating_numerator'] = 14\n",
    "merged_df.loc[merged_df.tweet_id == 722974582966214656, 'rating_numerator'] = 13\n",
    "merged_df.loc[merged_df.tweet_id == 716439118184652801, 'rating_numerator'] = 11\n",
    "merged_df.loc[merged_df.tweet_id == 682962037429899265, 'rating_numerator'] = 10\n",
    "merged_df.loc[merged_df.tweet_id == 666287406224695296, 'rating_numerator'] = 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will use a for loop to iterate through the tweet_ids with actual rating_denominator of 10.\n",
    "den = [740373189193256964, 722974582966214656, 716439118184652801, 682962037429899265, 666287406224695296]\n",
    "\n",
    "for i in den:\n",
    "    merged_df.loc[merged_df.tweet_id==i, 'rating_denominator'] = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>By viewing the rating denominator, we can confirm that the value count for the rating denominator of 10 has increased.</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2062\n",
       "80        2\n",
       "50        2\n",
       "70        1\n",
       "7         1\n",
       "150       1\n",
       "170       1\n",
       "90        1\n",
       "40        1\n",
       "130       1\n",
       "110       1\n",
       "120       1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #3: The rating numerator contains outrageous values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "### Define\n",
    "\n",
    "According to this, <a href=\"https://www.matthewunrue.com/Projects/Udacity/WeRateDogs/Project.html\">site</a>, average value of the numerator should be between 12 and 13. So, we are going to set all values of the numerator above 14 to 13."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      474\n",
       "10      430\n",
       "11      414\n",
       "13      285\n",
       "9       151\n",
       "8        95\n",
       "7        51\n",
       "14       41\n",
       "5        34\n",
       "6        32\n",
       "3        19\n",
       "4        15\n",
       "2         9\n",
       "1         4\n",
       "0         2\n",
       "27        1\n",
       "60        1\n",
       "88        1\n",
       "144       1\n",
       "26        1\n",
       "121       1\n",
       "143       1\n",
       "44        1\n",
       "45        1\n",
       "75        1\n",
       "80        1\n",
       "99        1\n",
       "204       1\n",
       "1776      1\n",
       "165       1\n",
       "15        1\n",
       "84        1\n",
       "24        1\n",
       "420       1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.rating_numerator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df.loc[merged_df['rating_numerator']>14, 'rating_numerator'] = 13"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12    474\n",
       "10    430\n",
       "11    414\n",
       "13    304\n",
       "9     151\n",
       "8      95\n",
       "7      51\n",
       "14     41\n",
       "5      34\n",
       "6      32\n",
       "3      19\n",
       "4      15\n",
       "2       9\n",
       "1       4\n",
       "0       2\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.rating_numerator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>favorites</th>\n",
       "      <th>retweet_count</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>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",
       "      <td>33812.0</td>\n",
       "      <td>7009.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <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\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421306343426/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>29329.0</td>\n",
       "      <td>5302.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181378084864/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>22055.0</td>\n",
       "      <td>3481.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557279858688/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>36940.0</td>\n",
       "      <td>7226.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>35311.0</td>\n",
       "      <td>7761.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </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\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "4  <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",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "2                   This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB   \n",
       "3                                                             This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ   \n",
       "4  This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f   \n",
       "\n",
       "   retweeted_status_id  retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                  NaN                       NaN                        NaN   \n",
       "1                  NaN                       NaN                        NaN   \n",
       "2                  NaN                       NaN                        NaN   \n",
       "3                  NaN                       NaN                        NaN   \n",
       "4                  NaN                       NaN                        NaN   \n",
       "\n",
       "                                                                                                                     expanded_urls  \\\n",
       "0                                                                  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "1                                                                  https://twitter.com/dog_rates/status/892177421306343426/photo/1   \n",
       "2                                                                  https://twitter.com/dog_rates/status/891815181378084864/photo/1   \n",
       "3                                                                  https://twitter.com/dog_rates/status/891689557279858688/photo/1   \n",
       "4  https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator      name doggo floofer pupper puppo  \\\n",
       "0                13                  10   Phineas  None    None   None  None   \n",
       "1                13                  10     Tilly  None    None   None  None   \n",
       "2                12                  10    Archie  None    None   None  None   \n",
       "3                13                  10     Darla  None    None   None  None   \n",
       "4                12                  10  Franklin  None    None   None  None   \n",
       "\n",
       "   favorites  retweet_count                                          jpg_url  \\\n",
       "0    33812.0         7009.0  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "1    29329.0         5302.0  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2    22055.0         3481.0  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "3    36940.0         7226.0  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "4    35311.0         7761.0  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "\n",
       "   img_num           p1   p1_conf p1_dog                  p2   p2_conf p2_dog  \\\n",
       "0      1.0       orange  0.097049  False               bagel  0.085851  False   \n",
       "1      1.0    Chihuahua  0.323581   True            Pekinese  0.090647   True   \n",
       "2      1.0    Chihuahua  0.716012   True            malamute  0.078253   True   \n",
       "3      1.0  paper_towel  0.170278  False  Labrador_retriever  0.168086   True   \n",
       "4      2.0       basset  0.555712   True    English_springer  0.225770   True   \n",
       "\n",
       "                            p3   p3_conf p3_dog  \n",
       "0                       banana  0.076110  False  \n",
       "1                     papillon  0.068957   True  \n",
       "2                       kelpie  0.031379   True  \n",
       "3                      spatula  0.040836  False  \n",
       "4  German_short-haired_pointer  0.175219   True  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issues #4 and #5: timestamp column has +0000 which is not necessary, and timestamp should be datetime format instead of object.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "### Define\n",
    "We will remove the unncessary +0000 values attached to the timestamp\n",
    "The datatype of the timestamp will be changed to datetime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the +0000 value from the timestamp\n",
    "merged_df.timestamp = merged_df.timestamp.str.strip('+0000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the timestamp column to datetime\n",
    "merged_df.timestamp = pd.to_datetime(merged_df.timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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",
       "      <th>favorites</th>\n",
       "      <th>retweet_count</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>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56</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",
       "      <td>33812.0</td>\n",
       "      <td>7009.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1.0</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",
       "</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   \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   \n",
       "\n",
       "   favorites  retweet_count                                          jpg_url  \\\n",
       "0    33812.0         7009.0  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "   img_num      p1   p1_conf p1_dog     p2   p2_conf p2_dog      p3  p3_conf  \\\n",
       "0      1.0  orange  0.097049  False  bagel  0.085851  False  banana  0.07611   \n",
       "\n",
       "  p3_dog  \n",
       "0  False  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2075 entries, 0 to 2355\n",
      "Data columns (total 30 columns):\n",
      " #   Column                      Non-Null Count  Dtype         \n",
      "---  ------                      --------------  -----         \n",
      " 0   tweet_id                    2075 non-null   int64         \n",
      " 1   in_reply_to_status_id       23 non-null     float64       \n",
      " 2   in_reply_to_user_id         23 non-null     float64       \n",
      " 3   timestamp                   2075 non-null   datetime64[ns]\n",
      " 4   source                      2075 non-null   object        \n",
      " 5   text                        2075 non-null   object        \n",
      " 6   retweeted_status_id         81 non-null     float64       \n",
      " 7   retweeted_status_user_id    81 non-null     float64       \n",
      " 8   retweeted_status_timestamp  81 non-null     object        \n",
      " 9   expanded_urls               2075 non-null   object        \n",
      " 10  rating_numerator            2075 non-null   int64         \n",
      " 11  rating_denominator          2075 non-null   int64         \n",
      " 12  name                        2075 non-null   object        \n",
      " 13  doggo                       2075 non-null   object        \n",
      " 14  floofer                     2075 non-null   object        \n",
      " 15  pupper                      2075 non-null   object        \n",
      " 16  puppo                       2075 non-null   object        \n",
      " 17  favorites                   2055 non-null   float64       \n",
      " 18  retweet_count               2055 non-null   float64       \n",
      " 19  jpg_url                     2075 non-null   object        \n",
      " 20  img_num                     2075 non-null   float64       \n",
      " 21  p1                          2075 non-null   object        \n",
      " 22  p1_conf                     2075 non-null   float64       \n",
      " 23  p1_dog                      2075 non-null   object        \n",
      " 24  p2                          2075 non-null   object        \n",
      " 25  p2_conf                     2075 non-null   float64       \n",
      " 26  p2_dog                      2075 non-null   object        \n",
      " 27  p3                          2075 non-null   object        \n",
      " 28  p3_conf                     2075 non-null   float64       \n",
      " 29  p3_dog                      2075 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(10), int64(3), object(16)\n",
      "memory usage: 502.5+ KB\n"
     ]
    }
   ],
   "source": [
    "merged_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #6: The datatypes are not consistent for some of the columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "### Define\n",
    "We will convert each column to their appropriate datatype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a list of the category for the iteration\n",
    "category = ['name', 'doggo', 'floofer', 'pupper', 'puppo', 'source']\n",
    "\n",
    "\n",
    "\n",
    "for c in category:\n",
    "    merged_df[c] = merged_df[c].astype('category')\n",
    "\n",
    "    \n",
    "merged_df['tweet_id'] = merged_df['tweet_id'].astype(object)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2075 entries, 0 to 2355\n",
      "Data columns (total 30 columns):\n",
      " #   Column                      Non-Null Count  Dtype         \n",
      "---  ------                      --------------  -----         \n",
      " 0   tweet_id                    2075 non-null   object        \n",
      " 1   in_reply_to_status_id       23 non-null     float64       \n",
      " 2   in_reply_to_user_id         23 non-null     float64       \n",
      " 3   timestamp                   2075 non-null   datetime64[ns]\n",
      " 4   source                      2075 non-null   category      \n",
      " 5   text                        2075 non-null   object        \n",
      " 6   retweeted_status_id         81 non-null     float64       \n",
      " 7   retweeted_status_user_id    81 non-null     float64       \n",
      " 8   retweeted_status_timestamp  81 non-null     object        \n",
      " 9   expanded_urls               2075 non-null   object        \n",
      " 10  rating_numerator            2075 non-null   int64         \n",
      " 11  rating_denominator          2075 non-null   int64         \n",
      " 12  name                        2075 non-null   category      \n",
      " 13  doggo                       2075 non-null   category      \n",
      " 14  floofer                     2075 non-null   category      \n",
      " 15  pupper                      2075 non-null   category      \n",
      " 16  puppo                       2075 non-null   category      \n",
      " 17  favorites                   2055 non-null   float64       \n",
      " 18  retweet_count               2055 non-null   float64       \n",
      " 19  jpg_url                     2075 non-null   object        \n",
      " 20  img_num                     2075 non-null   float64       \n",
      " 21  p1                          2075 non-null   object        \n",
      " 22  p1_conf                     2075 non-null   float64       \n",
      " 23  p1_dog                      2075 non-null   object        \n",
      " 24  p2                          2075 non-null   object        \n",
      " 25  p2_conf                     2075 non-null   float64       \n",
      " 26  p2_dog                      2075 non-null   object        \n",
      " 27  p3                          2075 non-null   object        \n",
      " 28  p3_conf                     2075 non-null   float64       \n",
      " 29  p3_dog                      2075 non-null   object        \n",
      "dtypes: category(6), datetime64[ns](1), float64(10), int64(2), object(11)\n",
      "memory usage: 459.7+ KB\n"
     ]
    }
   ],
   "source": [
    "merged_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #7: Some dog names are not correct like a, an, the."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "### Define\n",
    "\n",
    "We will change the names that are not consistent to None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The inconsistent names are the ones starting with lower case, so we will use that to filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We see all incorrect names have their first letter has lowercase. We will use this to change them into 'None'. \n",
    "dog_names=[]\n",
    "\n",
    "# The function iterates through name column checking if the first letter is lowercase.\n",
    "def wrong_dog_name(name_row):\n",
    "    if name_row['name'][0].islower() == True:\n",
    "        dog_names.append('None')\n",
    "    else:\n",
    "        dog_names.append(name_row['name'])\n",
    "        \n",
    "# call function using pandas apply by columns\n",
    "merged_df.apply(wrong_dog_name, axis=1)\n",
    "\n",
    "\n",
    "#add names list to master dataframe and check dataframe\n",
    "merged_df['names'] = dog_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56</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",
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       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>33812.0</td>\n",
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       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "      <td>Phineas</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</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 Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421306343426/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>29329.0</td>\n",
       "      <td>5302.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "      <td>Tilly</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</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 Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181378084864/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
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       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>22055.0</td>\n",
       "      <td>3481.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "      <td>Archie</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</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 Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557279858688/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>36940.0</td>\n",
       "      <td>7226.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "      <td>Darla</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</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 Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>35311.0</td>\n",
       "      <td>7761.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "      <td>Franklin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "            timestamp  \\\n",
       "0 2017-08-01 16:23:56   \n",
       "1 2017-08-01 00:17:27   \n",
       "2 2017-07-31 00:18:03   \n",
       "3 2017-07-30 15:58:51   \n",
       "4 2017-07-29 16:00:24   \n",
       "\n",
       "                                                                               source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "4  <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",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "2                   This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB   \n",
       "3                                                             This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ   \n",
       "4  This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f   \n",
       "\n",
       "   retweeted_status_id  retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                  NaN                       NaN                        NaN   \n",
       "1                  NaN                       NaN                        NaN   \n",
       "2                  NaN                       NaN                        NaN   \n",
       "3                  NaN                       NaN                        NaN   \n",
       "4                  NaN                       NaN                        NaN   \n",
       "\n",
       "                                                                                                                     expanded_urls  \\\n",
       "0                                                                  https://twitter.com/dog_rates/status/892420643555336193/photo/1   \n",
       "1                                                                  https://twitter.com/dog_rates/status/892177421306343426/photo/1   \n",
       "2                                                                  https://twitter.com/dog_rates/status/891815181378084864/photo/1   \n",
       "3                                                                  https://twitter.com/dog_rates/status/891689557279858688/photo/1   \n",
       "4  https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator      name doggo floofer  ... puppo  \\\n",
       "0                13                  10   Phineas  None    None  ...  None   \n",
       "1                13                  10     Tilly  None    None  ...  None   \n",
       "2                12                  10    Archie  None    None  ...  None   \n",
       "3                13                  10     Darla  None    None  ...  None   \n",
       "4                12                  10  Franklin  None    None  ...  None   \n",
       "\n",
       "  favorites  retweet_count                                          jpg_url  \\\n",
       "0   33812.0         7009.0  https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "1   29329.0         5302.0  https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2   22055.0         3481.0  https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "3   36940.0         7226.0  https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "4   35311.0         7761.0  https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "\n",
       "  img_num           p1   p1_conf  p1_dog                  p2   p2_conf  \\\n",
       "0     1.0       orange  0.097049   False               bagel  0.085851   \n",
       "1     1.0    Chihuahua  0.323581    True            Pekinese  0.090647   \n",
       "2     1.0    Chihuahua  0.716012    True            malamute  0.078253   \n",
       "3     1.0  paper_towel  0.170278   False  Labrador_retriever  0.168086   \n",
       "4     2.0       basset  0.555712    True    English_springer  0.225770   \n",
       "\n",
       "   p2_dog                           p3   p3_conf  p3_dog     names  \n",
       "0   False                       banana  0.076110   False   Phineas  \n",
       "1    True                     papillon  0.068957    True     Tilly  \n",
       "2    True                       kelpie  0.031379    True    Archie  \n",
       "3    True                      spatula  0.040836   False     Darla  \n",
       "4    True  German_short-haired_pointer  0.175219    True  Franklin  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #8: Our dataset contains 181 retweets and 78 replies which are not needed for our analysis, since we are focusing on actual tweets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "### Define\n",
    "\n",
    "We will drop the retweets and replies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the irrelevant colummns\n",
    "merged_df = merged_df[merged_df.retweeted_status_id.isnull()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = merged_df.drop(['retweeted_status_id','retweeted_status_user_id','retweeted_status_timestamp'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1994 entries, 0 to 2355\n",
      "Data columns (total 28 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   tweet_id               1994 non-null   object        \n",
      " 1   in_reply_to_status_id  23 non-null     float64       \n",
      " 2   in_reply_to_user_id    23 non-null     float64       \n",
      " 3   timestamp              1994 non-null   datetime64[ns]\n",
      " 4   source                 1994 non-null   category      \n",
      " 5   text                   1994 non-null   object        \n",
      " 6   expanded_urls          1994 non-null   object        \n",
      " 7   rating_numerator       1994 non-null   int64         \n",
      " 8   rating_denominator     1994 non-null   int64         \n",
      " 9   name                   1994 non-null   category      \n",
      " 10  doggo                  1994 non-null   category      \n",
      " 11  floofer                1994 non-null   category      \n",
      " 12  pupper                 1994 non-null   category      \n",
      " 13  puppo                  1994 non-null   category      \n",
      " 14  favorites              1984 non-null   float64       \n",
      " 15  retweet_count          1984 non-null   float64       \n",
      " 16  jpg_url                1994 non-null   object        \n",
      " 17  img_num                1994 non-null   float64       \n",
      " 18  p1                     1994 non-null   object        \n",
      " 19  p1_conf                1994 non-null   float64       \n",
      " 20  p1_dog                 1994 non-null   object        \n",
      " 21  p2                     1994 non-null   object        \n",
      " 22  p2_conf                1994 non-null   float64       \n",
      " 23  p2_dog                 1994 non-null   object        \n",
      " 24  p3                     1994 non-null   object        \n",
      " 25  p3_conf                1994 non-null   float64       \n",
      " 26  p3_dog                 1994 non-null   object        \n",
      " 27  names                  1994 non-null   object        \n",
      "dtypes: category(6), datetime64[ns](1), float64(8), int64(2), object(11)\n",
      "memory usage: 412.1+ KB\n"
     ]
    }
   ],
   "source": [
    "merged_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exteacting the tweet source using the regular expression library 're'\n",
    "merged_df['source'] = merged_df['source'].apply(lambda x: re.findall(r'>(.*)<', x)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Twitter for iPhone    1955\n",
       "Twitter Web Client      28\n",
       "TweetDeck               11\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.source.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #10: The variables, doggo, floofer, pupper, puppo need to be in a single column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1688, 203, 63, 22, 9, 7, 1, 1]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df[['doggo', 'floofer', 'pupper', 'puppo']].value_counts().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       doggo\n",
      "None    1920\n",
      "doggo     74\n",
      "         floofer\n",
      "None        1986\n",
      "floofer        8\n",
      "        pupper\n",
      "None      1782\n",
      "pupper     212\n",
      "       puppo\n",
      "None    1971\n",
      "puppo     23\n"
     ]
    }
   ],
   "source": [
    "# Checking the count of each dog breed\n",
    "dog_breeds = ['doggo', 'floofer', 'pupper', 'puppo']\n",
    "for i in dog_breeds:\n",
    "    x = pd.DataFrame(merged_df[i].value_counts())\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collapsing the dog_breeds into one column for proper analysis.\n",
    "merged_df = pd.melt(merged_df, \n",
    "        id_vars = ['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',\n",
    "       'rating_numerator', 'rating_denominator', 'name', 'favorites', 'retweet_count', 'jpg_url', 'img_num',\n",
    "       'p1', 'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf',\n",
    "       'p3_dog'], \n",
    "        value_vars = ['doggo', 'floofer', 'pupper', 'puppo'], \n",
    "        var_name = 'types', value_name = 'dog_type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>After the melting process, we have many duplicated tweet_ids. So we will need to drop those duplicated ids for our analysis to be accurate </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the duplicated tweets by their ids\n",
    "merged_df = merged_df.sort_values('dog_type').drop_duplicates('tweet_id', keep = 'last')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": 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>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>favorites</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>types</th>\n",
       "      <th>dog_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5200</th>\n",
       "      <td>690015576308211712</td>\n",
       "      <td>2016-01-21 03:38:27</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This pupper can only sleep on shoes. It's a crippling disease. Tearing his family apart. 12/10 I'd totally pet tho https://t.co/03XlvS8izg</td>\n",
       "      <td>https://twitter.com/dog_rates/status/690015576308211712/photo/1,https://twitter.com/dog_rates/status/690015576308211712/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>2311.0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CZNtgWhWkAAbq3W.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.949609</td>\n",
       "      <td>True</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.033084</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.016663</td>\n",
       "      <td>True</td>\n",
       "      <td>pupper</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5513</th>\n",
       "      <td>675113801096802304</td>\n",
       "      <td>2015-12-11 00:44:07</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Meet Zuzu. He just graduated college. Astute pupper. Needs 2 leashes to contain him. Wasn't ready for the pic. 10/10 https://t.co/2H5SKmk0k7</td>\n",
       "      <td>https://twitter.com/dog_rates/status/675113801096802304/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Zuzu</td>\n",
       "      <td>1740.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CV58a4nXAAApywo.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>bow</td>\n",
       "      <td>0.168020</td>\n",
       "      <td>False</td>\n",
       "      <td>quill</td>\n",
       "      <td>0.108807</td>\n",
       "      <td>False</td>\n",
       "      <td>joystick</td>\n",
       "      <td>0.043312</td>\n",
       "      <td>False</td>\n",
       "      <td>pupper</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5204</th>\n",
       "      <td>689905486972461056</td>\n",
       "      <td>2016-01-20 20:21:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Say hello to Gizmo. He's quite the pupper. Confused by bed, but agile af. Can barely catch on camera. 11/10 so quick https://t.co/IE4ZblyZRY</td>\n",
       "      <td>https://twitter.com/dog_rates/status/689905486972461056/photo/1,https://twitter.com/dog_rates/status/689905486972461056/photo/1,https://twitter.com/dog_rates/status/689905486972461056/photo/1,https://twitter.com/dog_rates/status/689905486972461056/photo/1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Gizmo</td>\n",
       "      <td>2263.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CZMJYCRVAAE35Wk.jpg</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.943331</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.023675</td>\n",
       "      <td>True</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.007165</td>\n",
       "      <td>True</td>\n",
       "      <td>pupper</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4064</th>\n",
       "      <td>874296783580663808</td>\n",
       "      <td>2017-06-12 16:06:11</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Jed. He may be the fanciest pupper in the game right now. Knows it too. 13/10 would sign modeling contract https://t.co/0YplNnSMEm</td>\n",
       "      <td>https://twitter.com/dog_rates/status/874296783580663808/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Jed</td>\n",
       "      <td>22969.0</td>\n",
       "      <td>3483.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DCIgSR0XgAANEOY.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.437216</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_poodle</td>\n",
       "      <td>0.277191</td>\n",
       "      <td>True</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.157402</td>\n",
       "      <td>True</td>\n",
       "      <td>pupper</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5543</th>\n",
       "      <td>674447403907457024</td>\n",
       "      <td>2015-12-09 04:36:06</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This pupper just wants a belly rub. This pupper has nothing to do w the tree being sideways now. 10/10 good pupper https://t.co/AyJ7Ohk71f</td>\n",
       "      <td>https://twitter.com/dog_rates/status/674447403907457024/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>957.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CVweVUfW4AACPwI.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brabancon_griffon</td>\n",
       "      <td>0.409909</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.244649</td>\n",
       "      <td>True</td>\n",
       "      <td>bull_mastiff</td>\n",
       "      <td>0.074819</td>\n",
       "      <td>True</td>\n",
       "      <td>pupper</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5057</th>\n",
       "      <td>702598099714314240</td>\n",
       "      <td>2016-02-24 20:56:55</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Sansa. She's gotten too big for her chair. Not so smol anymore. 11/10 once a pupper, always a pupper https://t.co/IpAoztle2s</td>\n",
       "      <td>https://twitter.com/dog_rates/status/702598099714314240/photo/1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Sansa</td>\n",
       "      <td>11354.0</td>\n",
       "      <td>3145.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CcAhPevW8AAoknv.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.219179</td>\n",
       "      <td>True</td>\n",
       "      <td>badger</td>\n",
       "      <td>0.133584</td>\n",
       "      <td>False</td>\n",
       "      <td>Siamese_cat</td>\n",
       "      <td>0.074440</td>\n",
       "      <td>False</td>\n",
       "      <td>pupper</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5209</th>\n",
       "      <td>689623661272240129</td>\n",
       "      <td>2016-01-20 01:41:08</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Lucy. She's terrified of the stuffed billed dog. 10/10 stay strong pupper https://t.co/QnvSjjyh7n</td>\n",
       "      <td>https://twitter.com/dog_rates/status/689623661272240129/photo/1,https://twitter.com/dog_rates/status/689623661272240129/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Lucy</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>581.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CZIJD2SWIAMJgNI.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.279604</td>\n",
       "      <td>True</td>\n",
       "      <td>mashed_potato</td>\n",
       "      <td>0.208564</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.077481</td>\n",
       "      <td>True</td>\n",
       "      <td>pupper</td>\n",
       "      <td>pupper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6281</th>\n",
       "      <td>825026590719483904</td>\n",
       "      <td>2017-01-27 17:04:02</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Say hello to Pablo. He's one gorgeous puppo. A true 12/10. Click the link to see why Pablo requests your assistance\\n\\nhttps://t.co/koHvVQp9bL https://t.co/IhW0JKf7kc</td>\n",
       "      <td>https://www.gofundme.com/my-puppys-double-cataract-surgery,https://twitter.com/dog_rates/status/825026590719483904/photo/1,https://twitter.com/dog_rates/status/825026590719483904/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Pablo</td>\n",
       "      <td>5910.0</td>\n",
       "      <td>1168.0</td>\n",
       "      <td>https://pbs.twimg.com/media/C3MVTeHWcAAGNfx.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.524454</td>\n",
       "      <td>True</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.467678</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.004976</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5996</th>\n",
       "      <td>889531135344209921</td>\n",
       "      <td>2017-07-24 17:02:04</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Stuart. He's sporting his favorite fanny pack. Secretly filled with bones only. 13/10 puppared puppo #BarkWeek https://t.co/y70o6h3isq</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889531135344209921/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Stuart</td>\n",
       "      <td>13354.0</td>\n",
       "      <td>1881.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.953442</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.013834</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.007958</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6680</th>\n",
       "      <td>752519690950500352</td>\n",
       "      <td>2016-07-11 15:07:30</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Hopefully this puppo on a swing will help get you through your Monday. 11/10 would push https://t.co/G54yClasz2</td>\n",
       "      <td>https://twitter.com/dog_rates/status/752519690950500352/photo/1,https://twitter.com/dog_rates/status/752519690950500352/photo/1,https://twitter.com/dog_rates/status/752519690950500352/photo/1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>6869.0</td>\n",
       "      <td>3157.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CnF8qVDWYAAh0g1.jpg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>swing</td>\n",
       "      <td>0.999984</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6130</th>\n",
       "      <td>855851453814013952</td>\n",
       "      <td>2017-04-22 18:31:02</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>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</td>\n",
       "      <td>https://twitter.com/dog_rates/status/855851453814013952/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>41914.0</td>\n",
       "      <td>16141.0</td>\n",
       "      <td>https://pbs.twimg.com/media/C-CYWrvWAAU8AXH.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>flat-coated_retriever</td>\n",
       "      <td>0.321676</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.115138</td>\n",
       "      <td>True</td>\n",
       "      <td>groenendael</td>\n",
       "      <td>0.096100</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6291</th>\n",
       "      <td>822872901745569793</td>\n",
       "      <td>2017-01-21 18:26:02</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here's a super supportive puppo participating in the Toronto  #WomensMarch today. 13/10 https://t.co/nTz3FtorBc</td>\n",
       "      <td>https://twitter.com/dog_rates/status/822872901745569793/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>124128.0</td>\n",
       "      <td>39930.0</td>\n",
       "      <td>https://pbs.twimg.com/media/C2tugXLXgAArJO4.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lakeland_terrier</td>\n",
       "      <td>0.196015</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.160329</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.069126</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6691</th>\n",
       "      <td>751132876104687617</td>\n",
       "      <td>2016-07-07 19:16:47</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Cooper. He's just so damn happy. 10/10 what's your secret puppo? https://t.co/yToDwVXEpA</td>\n",
       "      <td>https://twitter.com/dog_rates/status/751132876104687617/photo/1,https://twitter.com/dog_rates/status/751132876104687617/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Cooper</td>\n",
       "      <td>4731.0</td>\n",
       "      <td>1188.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CmyPXNOW8AEtaJ-.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.929390</td>\n",
       "      <td>True</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.038254</td>\n",
       "      <td>True</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.007610</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6448</th>\n",
       "      <td>793195938047070209</td>\n",
       "      <td>2016-10-31 21:00:23</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Say hello to Lily. She's pupset that her costume doesn't fit as well as last year. 12/10 poor puppo https://t.co/YSi6K1firY</td>\n",
       "      <td>https://twitter.com/dog_rates/status/793195938047070209/photo/1,https://twitter.com/dog_rates/status/793195938047070209/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Lily</td>\n",
       "      <td>14469.0</td>\n",
       "      <td>5281.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CwH_foYWgAEvTyI.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.654762</td>\n",
       "      <td>True</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.074100</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.042339</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6060</th>\n",
       "      <td>874012996292530176</td>\n",
       "      <td>2017-06-11 21:18:31</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Sebastian. He can't see all the colors of the rainbow, but he can see that this flag makes his human happy. 13/10 #PrideMonth puppo https://t.co/XBE0evJZ6V</td>\n",
       "      <td>https://twitter.com/dog_rates/status/874012996292530176/photo/1,https://twitter.com/dog_rates/status/874012996292530176/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Sebastian</td>\n",
       "      <td>30126.0</td>\n",
       "      <td>8595.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DCEeLxjXsAAvNSM.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.806674</td>\n",
       "      <td>True</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.116622</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.049182</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5994</th>\n",
       "      <td>889665388333682689</td>\n",
       "      <td>2017-07-25 01:55:32</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here's a puppo that seems to be on the fence about something haha no but seriously someone help her. 13/10 https://t.co/BxvuXk0UCm</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889665388333682689/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>42059.0</td>\n",
       "      <td>8352.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DFi579UWsAAatzw.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.966327</td>\n",
       "      <td>True</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.027356</td>\n",
       "      <td>True</td>\n",
       "      <td>basenji</td>\n",
       "      <td>0.004633</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6116</th>\n",
       "      <td>859607811541651456</td>\n",
       "      <td>2017-05-03 03:17:27</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Sorry for the lack of posts today. I came home from school and had to spend quality time with my puppo. Her name is Zoey and she's 13/10 https://t.co/BArWupFAn0</td>\n",
       "      <td>https://twitter.com/dog_rates/status/859607811541651456/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>16840.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>https://pbs.twimg.com/media/C-3wvtxXcAUTuBE.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.895529</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish_setter</td>\n",
       "      <td>0.024099</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.019285</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6481</th>\n",
       "      <td>787717603741622272</td>\n",
       "      <td>2016-10-16 18:11:26</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Tonks. She is a service puppo. Can hear a caterpillar hiccup from 7 miles away. 13/10 would follow anywhere https://t.co/i622ZbWkUp</td>\n",
       "      <td>https://twitter.com/dog_rates/status/787717603741622272/photo/1,https://twitter.com/dog_rates/status/787717603741622272/photo/1,https://twitter.com/dog_rates/status/787717603741622272/photo/1,https://twitter.com/dog_rates/status/787717603741622272/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tonks</td>\n",
       "      <td>9708.0</td>\n",
       "      <td>2588.0</td>\n",
       "      <td>https://pbs.twimg.com/media/Cu6I9vvWIAAZG0a.jpg</td>\n",
       "      <td>3.0</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.992339</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.004920</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.000853</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6308</th>\n",
       "      <td>819952236453363712</td>\n",
       "      <td>2017-01-13 17:00:21</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Oliver. He has dreams of being a service puppo so he can help his owner. 13/10 selfless af\\n\\nmake it happen:\\nhttps://t.co/f5WMsx0a9K https://t.co/6lJz0DKZIb</td>\n",
       "      <td>https://www.gofundme.com/servicedogoliver,https://twitter.com/dog_rates/status/819952236453363712/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Oliver</td>\n",
       "      <td>5016.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>https://pbs.twimg.com/media/C2EONHNWQAUWxkP.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>American_Staffordshire_terrier</td>\n",
       "      <td>0.925505</td>\n",
       "      <td>True</td>\n",
       "      <td>Staffordshire_bullterrier</td>\n",
       "      <td>0.036221</td>\n",
       "      <td>True</td>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.020412</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6795</th>\n",
       "      <td>738537504001953792</td>\n",
       "      <td>2016-06-03 01:07:16</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Bayley. She fell asleep trying to escape her evil fence enclosure. 11/10 night night puppo https://t.co/AxSiqAKEKu</td>\n",
       "      <td>https://twitter.com/dog_rates/status/738537504001953792/photo/1,https://twitter.com/dog_rates/status/738537504001953792/photo/1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Bayley</td>\n",
       "      <td>4708.0</td>\n",
       "      <td>1401.0</td>\n",
       "      <td>https://pbs.twimg.com/media/Cj_P7rSUgAAYQbz.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.808737</td>\n",
       "      <td>True</td>\n",
       "      <td>gibbon</td>\n",
       "      <td>0.028942</td>\n",
       "      <td>False</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.026498</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6753</th>\n",
       "      <td>744995568523612160</td>\n",
       "      <td>2016-06-20 20:49:19</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Abby. She got her face stuck in a glass. Churlish af. 9/10 rookie move puppo https://t.co/2FPb45NXrK</td>\n",
       "      <td>https://twitter.com/dog_rates/status/744995568523612160/photo/1,https://twitter.com/dog_rates/status/744995568523612160/photo/1</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Abby</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>569.0</td>\n",
       "      <td>https://pbs.twimg.com/media/ClbBg4WWEAMjwJu.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Old_English_sheepdog</td>\n",
       "      <td>0.427481</td>\n",
       "      <td>True</td>\n",
       "      <td>Shih-Tzu</td>\n",
       "      <td>0.146336</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_terrier</td>\n",
       "      <td>0.134269</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6661</th>\n",
       "      <td>756275833623502848</td>\n",
       "      <td>2016-07-21 23:53:04</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>When ur older siblings get to play in the deep end but dad says ur not old enough. Maybe one day puppo. All 10/10 https://t.co/JrDAzMhwG9</td>\n",
       "      <td>https://twitter.com/dog_rates/status/756275833623502848/photo/1,https://twitter.com/dog_rates/status/756275833623502848/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>5984.0</td>\n",
       "      <td>1408.0</td>\n",
       "      <td>https://pbs.twimg.com/media/Cn7U2xlW8AI9Pqp.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Airedale</td>\n",
       "      <td>0.602957</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.086981</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.086276</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6516</th>\n",
       "      <td>780931614150983680</td>\n",
       "      <td>2016-09-28 00:46:20</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>I want to finally rate this iconic puppo who thinks the parade is all for him. 13/10 would absolutely attend https://t.co/5dUYOu4b8d</td>\n",
       "      <td>https://twitter.com/dog_rates/status/780931614150983680/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>20495.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CtZtJxAXEAAyPGd.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>padlock</td>\n",
       "      <td>0.731564</td>\n",
       "      <td>False</td>\n",
       "      <td>necklace</td>\n",
       "      <td>0.065462</td>\n",
       "      <td>False</td>\n",
       "      <td>chain</td>\n",
       "      <td>0.036469</td>\n",
       "      <td>False</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6402</th>\n",
       "      <td>802239329049477120</td>\n",
       "      <td>2016-11-25 19:55:35</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Loki. He'll do your taxes for you. Can also make room in your budget for all the things you bought today. 12/10 what a puppo https://t.co/5oWrHCWg87</td>\n",
       "      <td>https://twitter.com/dog_rates/status/802239329049477120/photo/1,https://twitter.com/dog_rates/status/802239329049477120/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Loki</td>\n",
       "      <td>8651.0</td>\n",
       "      <td>2460.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CyIgaTEVEAA-9zS.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.482498</td>\n",
       "      <td>True</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.335774</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.134589</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6394</th>\n",
       "      <td>803773340896923648</td>\n",
       "      <td>2016-11-30 01:31:12</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Diogi. He fell in the pool as soon as he was brought home. Clumsy puppo. 12/10 would pet until dry https://t.co/ZxeRjMKaWt</td>\n",
       "      <td>https://twitter.com/dog_rates/status/803773340896923648/photo/1,https://twitter.com/dog_rates/status/803773340896923648/photo/1,https://twitter.com/dog_rates/status/803773340896923648/photo/1,https://twitter.com/dog_rates/status/803773340896923648/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Diogi</td>\n",
       "      <td>9567.0</td>\n",
       "      <td>2591.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CyeTku-XcAALkBd.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.817066</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.059707</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.034195</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6463</th>\n",
       "      <td>790946055508652032</td>\n",
       "      <td>2016-10-25 16:00:09</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Betty. She's assisting with the dishes. Such a good puppo. 12/10 h*ckin helpful af https://t.co/dgvTPZ9tgI</td>\n",
       "      <td>https://twitter.com/dog_rates/status/790946055508652032/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Betty</td>\n",
       "      <td>15869.0</td>\n",
       "      <td>4392.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CvoBPWRWgAA4het.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>dishwasher</td>\n",
       "      <td>0.700466</td>\n",
       "      <td>False</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.245773</td>\n",
       "      <td>True</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.039012</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6087</th>\n",
       "      <td>867421006826221569</td>\n",
       "      <td>2017-05-24 16:44:18</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Shikha. She just watched you drop a skittle on the ground and still eat it. Could not be less impressed. 12/10 superior puppo https://t.co/XZlZKd73go</td>\n",
       "      <td>https://twitter.com/dog_rates/status/867421006826221569/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Shikha</td>\n",
       "      <td>14348.0</td>\n",
       "      <td>2118.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DAmyy8FXYAIH8Ty.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.616457</td>\n",
       "      <td>True</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.381330</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.001670</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6044</th>\n",
       "      <td>878776093423087618</td>\n",
       "      <td>2017-06-25 00:45:22</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Snoopy. He's a proud #PrideMonthPuppo. Impeccable handwriting for not having thumbs. 13/10 would love back #PrideMonth https://t.co/lNZwgNO4gS</td>\n",
       "      <td>https://twitter.com/dog_rates/status/878776093423087618/photo/1,https://twitter.com/dog_rates/status/878776093423087618/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Snoopy</td>\n",
       "      <td>17139.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DDIKMXzW0AEibje.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.734684</td>\n",
       "      <td>True</td>\n",
       "      <td>whippet</td>\n",
       "      <td>0.150487</td>\n",
       "      <td>True</td>\n",
       "      <td>Ibizan_hound</td>\n",
       "      <td>0.039725</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6279</th>\n",
       "      <td>825535076884762624</td>\n",
       "      <td>2017-01-29 02:44:34</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here's a very loving and accepting puppo. Appears to have read her Constitution well. 14/10 would pat head approvingly https://t.co/6ao80wIpV1</td>\n",
       "      <td>https://twitter.com/dog_rates/status/825535076884762624/photo/1</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>53994.0</td>\n",
       "      <td>16363.0</td>\n",
       "      <td>https://pbs.twimg.com/media/C3TjvitXAAAI-QH.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.681495</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.147940</td>\n",
       "      <td>True</td>\n",
       "      <td>black-and-tan_coonhound</td>\n",
       "      <td>0.024525</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6764</th>\n",
       "      <td>743253157753532416</td>\n",
       "      <td>2016-06-16 01:25:36</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Kilo. He cannot reach the snackum. Nifty tongue, but not nifty enough. 10/10 maybe one day puppo https://t.co/gSmp31Zrsx</td>\n",
       "      <td>https://twitter.com/dog_rates/status/743253157753532416/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Kilo</td>\n",
       "      <td>3935.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>https://pbs.twimg.com/media/ClCQzFUUYAA5vAu.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.442612</td>\n",
       "      <td>True</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.368137</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.177822</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id           timestamp              source  \\\n",
       "5200  690015576308211712 2016-01-21 03:38:27  Twitter for iPhone   \n",
       "5513  675113801096802304 2015-12-11 00:44:07  Twitter for iPhone   \n",
       "5204  689905486972461056 2016-01-20 20:21:00  Twitter for iPhone   \n",
       "4064  874296783580663808 2017-06-12 16:06:11  Twitter for iPhone   \n",
       "5543  674447403907457024 2015-12-09 04:36:06  Twitter for iPhone   \n",
       "5057  702598099714314240 2016-02-24 20:56:55  Twitter for iPhone   \n",
       "5209  689623661272240129 2016-01-20 01:41:08  Twitter for iPhone   \n",
       "6281  825026590719483904 2017-01-27 17:04:02  Twitter for iPhone   \n",
       "5996  889531135344209921 2017-07-24 17:02:04  Twitter for iPhone   \n",
       "6680  752519690950500352 2016-07-11 15:07:30  Twitter for iPhone   \n",
       "6130  855851453814013952 2017-04-22 18:31:02  Twitter for iPhone   \n",
       "6291  822872901745569793 2017-01-21 18:26:02  Twitter for iPhone   \n",
       "6691  751132876104687617 2016-07-07 19:16:47  Twitter for iPhone   \n",
       "6448  793195938047070209 2016-10-31 21:00:23  Twitter for iPhone   \n",
       "6060  874012996292530176 2017-06-11 21:18:31  Twitter for iPhone   \n",
       "5994  889665388333682689 2017-07-25 01:55:32  Twitter for iPhone   \n",
       "6116  859607811541651456 2017-05-03 03:17:27  Twitter for iPhone   \n",
       "6481  787717603741622272 2016-10-16 18:11:26  Twitter for iPhone   \n",
       "6308  819952236453363712 2017-01-13 17:00:21  Twitter for iPhone   \n",
       "6795  738537504001953792 2016-06-03 01:07:16  Twitter for iPhone   \n",
       "6753  744995568523612160 2016-06-20 20:49:19  Twitter for iPhone   \n",
       "6661  756275833623502848 2016-07-21 23:53:04  Twitter for iPhone   \n",
       "6516  780931614150983680 2016-09-28 00:46:20  Twitter for iPhone   \n",
       "6402  802239329049477120 2016-11-25 19:55:35  Twitter for iPhone   \n",
       "6394  803773340896923648 2016-11-30 01:31:12  Twitter for iPhone   \n",
       "6463  790946055508652032 2016-10-25 16:00:09  Twitter for iPhone   \n",
       "6087  867421006826221569 2017-05-24 16:44:18  Twitter for iPhone   \n",
       "6044  878776093423087618 2017-06-25 00:45:22  Twitter for iPhone   \n",
       "6279  825535076884762624 2017-01-29 02:44:34  Twitter for iPhone   \n",
       "6764  743253157753532416 2016-06-16 01:25:36  Twitter for iPhone   \n",
       "\n",
       "                                                                                                                                                                        text  \\\n",
       "5200                              This pupper can only sleep on shoes. It's a crippling disease. Tearing his family apart. 12/10 I'd totally pet tho https://t.co/03XlvS8izg   \n",
       "5513                            Meet Zuzu. He just graduated college. Astute pupper. Needs 2 leashes to contain him. Wasn't ready for the pic. 10/10 https://t.co/2H5SKmk0k7   \n",
       "5204                            Say hello to Gizmo. He's quite the pupper. Confused by bed, but agile af. Can barely catch on camera. 11/10 so quick https://t.co/IE4ZblyZRY   \n",
       "4064                              This is Jed. He may be the fanciest pupper in the game right now. Knows it too. 13/10 would sign modeling contract https://t.co/0YplNnSMEm   \n",
       "5543                              This pupper just wants a belly rub. This pupper has nothing to do w the tree being sideways now. 10/10 good pupper https://t.co/AyJ7Ohk71f   \n",
       "5057                                    This is Sansa. She's gotten too big for her chair. Not so smol anymore. 11/10 once a pupper, always a pupper https://t.co/IpAoztle2s   \n",
       "5209                                                               This is Lucy. She's terrified of the stuffed billed dog. 10/10 stay strong pupper https://t.co/QnvSjjyh7n   \n",
       "6281  Say hello to Pablo. He's one gorgeous puppo. A true 12/10. Click the link to see why Pablo requests your assistance\\n\\nhttps://t.co/koHvVQp9bL https://t.co/IhW0JKf7kc   \n",
       "5996                          This is Stuart. He's sporting his favorite fanny pack. Secretly filled with bones only. 13/10 puppared puppo #BarkWeek https://t.co/y70o6h3isq   \n",
       "6680                                                         Hopefully this puppo on a swing will help get you through your Monday. 11/10 would push https://t.co/G54yClasz2   \n",
       "6130          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",
       "6291                                                         Here's a super supportive puppo participating in the Toronto  #WomensMarch today. 13/10 https://t.co/nTz3FtorBc   \n",
       "6691                                                                        This is Cooper. He's just so damn happy. 10/10 what's your secret puppo? https://t.co/yToDwVXEpA   \n",
       "6448                                             Say hello to Lily. She's pupset that her costume doesn't fit as well as last year. 12/10 poor puppo https://t.co/YSi6K1firY   \n",
       "6060     This is Sebastian. He can't see all the colors of the rainbow, but he can see that this flag makes his human happy. 13/10 #PrideMonth puppo https://t.co/XBE0evJZ6V   \n",
       "5994                                      Here's a puppo that seems to be on the fence about something haha no but seriously someone help her. 13/10 https://t.co/BxvuXk0UCm   \n",
       "6116        Sorry for the lack of posts today. I came home from school and had to spend quality time with my puppo. Her name is Zoey and she's 13/10 https://t.co/BArWupFAn0   \n",
       "6481                             This is Tonks. She is a service puppo. Can hear a caterpillar hiccup from 7 miles away. 13/10 would follow anywhere https://t.co/i622ZbWkUp   \n",
       "6308  This is Oliver. He has dreams of being a service puppo so he can help his owner. 13/10 selfless af\\n\\nmake it happen:\\nhttps://t.co/f5WMsx0a9K https://t.co/6lJz0DKZIb   \n",
       "6795                                              This is Bayley. She fell asleep trying to escape her evil fence enclosure. 11/10 night night puppo https://t.co/AxSiqAKEKu   \n",
       "6753                                                            This is Abby. She got her face stuck in a glass. Churlish af. 9/10 rookie move puppo https://t.co/2FPb45NXrK   \n",
       "6661                               When ur older siblings get to play in the deep end but dad says ur not old enough. Maybe one day puppo. All 10/10 https://t.co/JrDAzMhwG9   \n",
       "6516                                    I want to finally rate this iconic puppo who thinks the parade is all for him. 13/10 would absolutely attend https://t.co/5dUYOu4b8d   \n",
       "6402            This is Loki. He'll do your taxes for you. Can also make room in your budget for all the things you bought today. 12/10 what a puppo https://t.co/5oWrHCWg87   \n",
       "6394                                      This is Diogi. He fell in the pool as soon as he was brought home. Clumsy puppo. 12/10 would pet until dry https://t.co/ZxeRjMKaWt   \n",
       "6463                                                      This is Betty. She's assisting with the dishes. Such a good puppo. 12/10 h*ckin helpful af https://t.co/dgvTPZ9tgI   \n",
       "6087           This is Shikha. She just watched you drop a skittle on the ground and still eat it. Could not be less impressed. 12/10 superior puppo https://t.co/XZlZKd73go   \n",
       "6044                  This is Snoopy. He's a proud #PrideMonthPuppo. Impeccable handwriting for not having thumbs. 13/10 would love back #PrideMonth https://t.co/lNZwgNO4gS   \n",
       "6279                          Here's a very loving and accepting puppo. Appears to have read her Constitution well. 14/10 would pat head approvingly https://t.co/6ao80wIpV1   \n",
       "6764                                        This is Kilo. He cannot reach the snackum. Nifty tongue, but not nifty enough. 10/10 maybe one day puppo https://t.co/gSmp31Zrsx   \n",
       "\n",
       "                                                                                                                                                                                                                                                        expanded_urls  \\\n",
       "5200                                                                                                                                  https://twitter.com/dog_rates/status/690015576308211712/photo/1,https://twitter.com/dog_rates/status/690015576308211712/photo/1   \n",
       "5513                                                                                                                                                                                                  https://twitter.com/dog_rates/status/675113801096802304/photo/1   \n",
       "5204  https://twitter.com/dog_rates/status/689905486972461056/photo/1,https://twitter.com/dog_rates/status/689905486972461056/photo/1,https://twitter.com/dog_rates/status/689905486972461056/photo/1,https://twitter.com/dog_rates/status/689905486972461056/photo/1   \n",
       "4064                                                                                                                                                                                                  https://twitter.com/dog_rates/status/874296783580663808/photo/1   \n",
       "5543                                                                                                                                                                                                  https://twitter.com/dog_rates/status/674447403907457024/photo/1   \n",
       "5057                                                                                                                                                                                                  https://twitter.com/dog_rates/status/702598099714314240/photo/1   \n",
       "5209                                                                                                                                  https://twitter.com/dog_rates/status/689623661272240129/photo/1,https://twitter.com/dog_rates/status/689623661272240129/photo/1   \n",
       "6281                                                                       https://www.gofundme.com/my-puppys-double-cataract-surgery,https://twitter.com/dog_rates/status/825026590719483904/photo/1,https://twitter.com/dog_rates/status/825026590719483904/photo/1   \n",
       "5996                                                                                                                                                                                                  https://twitter.com/dog_rates/status/889531135344209921/photo/1   \n",
       "6680                                                                  https://twitter.com/dog_rates/status/752519690950500352/photo/1,https://twitter.com/dog_rates/status/752519690950500352/photo/1,https://twitter.com/dog_rates/status/752519690950500352/photo/1   \n",
       "6130                                                                                                                                                                                                  https://twitter.com/dog_rates/status/855851453814013952/photo/1   \n",
       "6291                                                                                                                                                                                                  https://twitter.com/dog_rates/status/822872901745569793/photo/1   \n",
       "6691                                                                                                                                  https://twitter.com/dog_rates/status/751132876104687617/photo/1,https://twitter.com/dog_rates/status/751132876104687617/photo/1   \n",
       "6448                                                                                                                                  https://twitter.com/dog_rates/status/793195938047070209/photo/1,https://twitter.com/dog_rates/status/793195938047070209/photo/1   \n",
       "6060                                                                                                                                  https://twitter.com/dog_rates/status/874012996292530176/photo/1,https://twitter.com/dog_rates/status/874012996292530176/photo/1   \n",
       "5994                                                                                                                                                                                                  https://twitter.com/dog_rates/status/889665388333682689/photo/1   \n",
       "6116                                                                                                                                                                                                  https://twitter.com/dog_rates/status/859607811541651456/photo/1   \n",
       "6481  https://twitter.com/dog_rates/status/787717603741622272/photo/1,https://twitter.com/dog_rates/status/787717603741622272/photo/1,https://twitter.com/dog_rates/status/787717603741622272/photo/1,https://twitter.com/dog_rates/status/787717603741622272/photo/1   \n",
       "6308                                                                                                                                                        https://www.gofundme.com/servicedogoliver,https://twitter.com/dog_rates/status/819952236453363712/photo/1   \n",
       "6795                                                                                                                                  https://twitter.com/dog_rates/status/738537504001953792/photo/1,https://twitter.com/dog_rates/status/738537504001953792/photo/1   \n",
       "6753                                                                                                                                  https://twitter.com/dog_rates/status/744995568523612160/photo/1,https://twitter.com/dog_rates/status/744995568523612160/photo/1   \n",
       "6661                                                                                                                                  https://twitter.com/dog_rates/status/756275833623502848/photo/1,https://twitter.com/dog_rates/status/756275833623502848/photo/1   \n",
       "6516                                                                                                                                                                                                  https://twitter.com/dog_rates/status/780931614150983680/photo/1   \n",
       "6402                                                                                                                                  https://twitter.com/dog_rates/status/802239329049477120/photo/1,https://twitter.com/dog_rates/status/802239329049477120/photo/1   \n",
       "6394  https://twitter.com/dog_rates/status/803773340896923648/photo/1,https://twitter.com/dog_rates/status/803773340896923648/photo/1,https://twitter.com/dog_rates/status/803773340896923648/photo/1,https://twitter.com/dog_rates/status/803773340896923648/photo/1   \n",
       "6463                                                                                                                                                                                                  https://twitter.com/dog_rates/status/790946055508652032/photo/1   \n",
       "6087                                                                                                                                                                                                  https://twitter.com/dog_rates/status/867421006826221569/photo/1   \n",
       "6044                                                                                                                                  https://twitter.com/dog_rates/status/878776093423087618/photo/1,https://twitter.com/dog_rates/status/878776093423087618/photo/1   \n",
       "6279                                                                                                                                                                                                  https://twitter.com/dog_rates/status/825535076884762624/photo/1   \n",
       "6764                                                                                                                                                                                                  https://twitter.com/dog_rates/status/743253157753532416/photo/1   \n",
       "\n",
       "      rating_numerator  rating_denominator       name  favorites  \\\n",
       "5200                12                  10       None     2311.0   \n",
       "5513                10                  10       Zuzu     1740.0   \n",
       "5204                11                  10      Gizmo     2263.0   \n",
       "4064                13                  10        Jed    22969.0   \n",
       "5543                10                  10       None      957.0   \n",
       "5057                11                  10      Sansa    11354.0   \n",
       "5209                10                  10       Lucy     2046.0   \n",
       "6281                12                  10      Pablo     5910.0   \n",
       "5996                13                  10     Stuart    13354.0   \n",
       "6680                11                  10       None     6869.0   \n",
       "6130                13                  10       None    41914.0   \n",
       "6291                13                  10       None   124128.0   \n",
       "6691                10                  10     Cooper     4731.0   \n",
       "6448                12                  10       Lily    14469.0   \n",
       "6060                13                  10  Sebastian    30126.0   \n",
       "5994                13                  10       None    42059.0   \n",
       "6116                13                  10       None    16840.0   \n",
       "6481                13                  10      Tonks     9708.0   \n",
       "6308                13                  10     Oliver     5016.0   \n",
       "6795                11                  10     Bayley     4708.0   \n",
       "6753                 9                  10       Abby     2750.0   \n",
       "6661                10                  10       None     5984.0   \n",
       "6516                13                  10       None    20495.0   \n",
       "6402                12                  10       Loki     8651.0   \n",
       "6394                12                  10      Diogi     9567.0   \n",
       "6463                12                  10      Betty    15869.0   \n",
       "6087                12                  10     Shikha    14348.0   \n",
       "6044                13                  10     Snoopy    17139.0   \n",
       "6279                14                  10       None    53994.0   \n",
       "6764                10                  10       Kilo     3935.0   \n",
       "\n",
       "      retweet_count                                          jpg_url  img_num  \\\n",
       "5200          664.0  https://pbs.twimg.com/media/CZNtgWhWkAAbq3W.jpg      2.0   \n",
       "5513          690.0  https://pbs.twimg.com/media/CV58a4nXAAApywo.jpg      1.0   \n",
       "5204          624.0  https://pbs.twimg.com/media/CZMJYCRVAAE35Wk.jpg      4.0   \n",
       "4064         3483.0  https://pbs.twimg.com/media/DCIgSR0XgAANEOY.jpg      1.0   \n",
       "5543          315.0  https://pbs.twimg.com/media/CVweVUfW4AACPwI.jpg      1.0   \n",
       "5057         3145.0  https://pbs.twimg.com/media/CcAhPevW8AAoknv.jpg      1.0   \n",
       "5209          581.0  https://pbs.twimg.com/media/CZIJD2SWIAMJgNI.jpg      1.0   \n",
       "6281         1168.0  https://pbs.twimg.com/media/C3MVTeHWcAAGNfx.jpg      2.0   \n",
       "5996         1881.0  https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg      1.0   \n",
       "6680         3157.0  https://pbs.twimg.com/media/CnF8qVDWYAAh0g1.jpg      3.0   \n",
       "6130        16141.0  https://pbs.twimg.com/media/C-CYWrvWAAU8AXH.jpg      1.0   \n",
       "6291        39930.0  https://pbs.twimg.com/media/C2tugXLXgAArJO4.jpg      1.0   \n",
       "6691         1188.0  https://pbs.twimg.com/media/CmyPXNOW8AEtaJ-.jpg      1.0   \n",
       "6448         5281.0  https://pbs.twimg.com/media/CwH_foYWgAEvTyI.jpg      2.0   \n",
       "6060         8595.0  https://pbs.twimg.com/media/DCEeLxjXsAAvNSM.jpg      2.0   \n",
       "5994         8352.0  https://pbs.twimg.com/media/DFi579UWsAAatzw.jpg      1.0   \n",
       "6116         1366.0  https://pbs.twimg.com/media/C-3wvtxXcAUTuBE.jpg      1.0   \n",
       "6481         2588.0  https://pbs.twimg.com/media/Cu6I9vvWIAAZG0a.jpg      3.0   \n",
       "6308         1082.0  https://pbs.twimg.com/media/C2EONHNWQAUWxkP.jpg      1.0   \n",
       "6795         1401.0  https://pbs.twimg.com/media/Cj_P7rSUgAAYQbz.jpg      1.0   \n",
       "6753          569.0  https://pbs.twimg.com/media/ClbBg4WWEAMjwJu.jpg      1.0   \n",
       "6661         1408.0  https://pbs.twimg.com/media/Cn7U2xlW8AI9Pqp.jpg      1.0   \n",
       "6516         6750.0  https://pbs.twimg.com/media/CtZtJxAXEAAyPGd.jpg      1.0   \n",
       "6402         2460.0  https://pbs.twimg.com/media/CyIgaTEVEAA-9zS.jpg      2.0   \n",
       "6394         2591.0  https://pbs.twimg.com/media/CyeTku-XcAALkBd.jpg      2.0   \n",
       "6463         4392.0  https://pbs.twimg.com/media/CvoBPWRWgAA4het.jpg      1.0   \n",
       "6087         2118.0  https://pbs.twimg.com/media/DAmyy8FXYAIH8Ty.jpg      1.0   \n",
       "6044         3433.0  https://pbs.twimg.com/media/DDIKMXzW0AEibje.jpg      2.0   \n",
       "6279        16363.0  https://pbs.twimg.com/media/C3TjvitXAAAI-QH.jpg      1.0   \n",
       "6764         1124.0  https://pbs.twimg.com/media/ClCQzFUUYAA5vAu.jpg      1.0   \n",
       "\n",
       "                                  p1   p1_conf p1_dog  \\\n",
       "5200                        malamute  0.949609   True   \n",
       "5513                             bow  0.168020  False   \n",
       "5204                      Pomeranian  0.943331   True   \n",
       "4064                  cocker_spaniel  0.437216   True   \n",
       "5543               Brabancon_griffon  0.409909   True   \n",
       "5057                          kelpie  0.219179   True   \n",
       "5209                      toy_poodle  0.279604   True   \n",
       "6281                      Eskimo_dog  0.524454   True   \n",
       "5996                golden_retriever  0.953442   True   \n",
       "6680                           swing  0.999984  False   \n",
       "6130           flat-coated_retriever  0.321676   True   \n",
       "6291                Lakeland_terrier  0.196015   True   \n",
       "6691              Labrador_retriever  0.929390   True   \n",
       "6448              Labrador_retriever  0.654762   True   \n",
       "6060                        Cardigan  0.806674   True   \n",
       "5994                        Pembroke  0.966327   True   \n",
       "6116                golden_retriever  0.895529   True   \n",
       "6481                 German_shepherd  0.992339   True   \n",
       "6308  American_Staffordshire_terrier  0.925505   True   \n",
       "6795                            chow  0.808737   True   \n",
       "6753            Old_English_sheepdog  0.427481   True   \n",
       "6661                        Airedale  0.602957   True   \n",
       "6516                         padlock  0.731564  False   \n",
       "6402                      Eskimo_dog  0.482498   True   \n",
       "6394              miniature_pinscher  0.817066   True   \n",
       "6463                      dishwasher  0.700466  False   \n",
       "6087                      Eskimo_dog  0.616457   True   \n",
       "6044               Italian_greyhound  0.734684   True   \n",
       "6279                      Rottweiler  0.681495   True   \n",
       "6764                        malamute  0.442612   True   \n",
       "\n",
       "                             p2   p2_conf p2_dog                       p3  \\\n",
       "5200             Siberian_husky  0.033084   True               Eskimo_dog   \n",
       "5513                      quill  0.108807  False                 joystick   \n",
       "5204          Shetland_sheepdog  0.023675   True                     chow   \n",
       "4064           miniature_poodle  0.277191   True               toy_poodle   \n",
       "5543                   malinois  0.244649   True             bull_mastiff   \n",
       "5057                     badger  0.133584  False              Siamese_cat   \n",
       "5209              mashed_potato  0.208564  False       Labrador_retriever   \n",
       "6281             Siberian_husky  0.467678   True                 malamute   \n",
       "5996         Labrador_retriever  0.013834   True                  redbone   \n",
       "6680         Labrador_retriever  0.000010   True               Eskimo_dog   \n",
       "6130         Labrador_retriever  0.115138   True              groenendael   \n",
       "6291         Labrador_retriever  0.160329   True            Irish_terrier   \n",
       "6691   Chesapeake_Bay_retriever  0.038254   True         golden_retriever   \n",
       "6448           golden_retriever  0.074100   True                Chihuahua   \n",
       "6060                   Pembroke  0.116622   True                   kelpie   \n",
       "5994                   Cardigan  0.027356   True                  basenji   \n",
       "6116               Irish_setter  0.024099   True       Labrador_retriever   \n",
       "6481                   malinois  0.004920   True                   kelpie   \n",
       "6308  Staffordshire_bullterrier  0.036221   True        Italian_greyhound   \n",
       "6795                     gibbon  0.028942  False                 Pembroke   \n",
       "6753                   Shih-Tzu  0.146336   True          Tibetan_terrier   \n",
       "6661              Irish_terrier  0.086981   True               bloodhound   \n",
       "6516                   necklace  0.065462  False                    chain   \n",
       "6402             Siberian_husky  0.335774   True                 malamute   \n",
       "6394                    redbone  0.059707   True            Irish_terrier   \n",
       "6463           golden_retriever  0.245773   True                     chow   \n",
       "6087             Siberian_husky  0.381330   True                 malamute   \n",
       "6044                    whippet  0.150487   True             Ibizan_hound   \n",
       "6279            Tibetan_mastiff  0.147940   True  black-and-tan_coonhound   \n",
       "6764             Siberian_husky  0.368137   True               Eskimo_dog   \n",
       "\n",
       "       p3_conf p3_dog   types dog_type  \n",
       "5200  0.016663   True  pupper   pupper  \n",
       "5513  0.043312  False  pupper   pupper  \n",
       "5204  0.007165   True  pupper   pupper  \n",
       "4064  0.157402   True  pupper   pupper  \n",
       "5543  0.074819   True  pupper   pupper  \n",
       "5057  0.074440  False  pupper   pupper  \n",
       "5209  0.077481   True  pupper   pupper  \n",
       "6281  0.004976   True   puppo    puppo  \n",
       "5996  0.007958   True   puppo    puppo  \n",
       "6680  0.000001   True   puppo    puppo  \n",
       "6130  0.096100   True   puppo    puppo  \n",
       "6291  0.069126   True   puppo    puppo  \n",
       "6691  0.007610   True   puppo    puppo  \n",
       "6448  0.042339   True   puppo    puppo  \n",
       "6060  0.049182   True   puppo    puppo  \n",
       "5994  0.004633   True   puppo    puppo  \n",
       "6116  0.019285   True   puppo    puppo  \n",
       "6481  0.000853   True   puppo    puppo  \n",
       "6308  0.020412   True   puppo    puppo  \n",
       "6795  0.026498   True   puppo    puppo  \n",
       "6753  0.134269   True   puppo    puppo  \n",
       "6661  0.086276   True   puppo    puppo  \n",
       "6516  0.036469  False   puppo    puppo  \n",
       "6402  0.134589   True   puppo    puppo  \n",
       "6394  0.034195   True   puppo    puppo  \n",
       "6463  0.039012   True   puppo    puppo  \n",
       "6087  0.001670   True   puppo    puppo  \n",
       "6044  0.039725   True   puppo    puppo  \n",
       "6279  0.024525   True   puppo    puppo  \n",
       "6764  0.177822   True   puppo    puppo  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.tail(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the 'types' column\n",
    "merged_df.drop('types', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we need to also merge the dog breed and their confidence level.\n",
    "\n",
    "dog_breed = []\n",
    "confidence_level = []\n",
    "def breed_conf(col):\n",
    "    if col['p1_dog'] == True:\n",
    "        dog_breed.append(col['p1'])\n",
    "        confidence_level.append(col['p1_conf'])\n",
    "    elif col['p2_dog'] == True:\n",
    "        dog_breed.append(col['p2'])\n",
    "        confidence_level.append(col['p2_conf'])\n",
    "    elif col['p3_dog'] == True:\n",
    "        dog_breed.append(col['p3'])\n",
    "        confidence_level.append(col['p3_conf'])\n",
    "    else:\n",
    "        dog_breed.append('unknown_breed')\n",
    "        confidence_level.append(0)\n",
    "# We will use the apply function apply the properties of the breed_conf function to our dataframe\n",
    "merged_df.apply(breed_conf, axis = 1)\n",
    "\n",
    "#We will now effect the changes to our DataFrame, df_merged by adding the two lists created\n",
    "merged_df['dog_breed'] = dog_breed\n",
    "merged_df['confidence'] = confidence_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>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>favorites</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>dog_type</th>\n",
       "      <th>dog_breed</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6044</th>\n",
       "      <td>878776093423087618</td>\n",
       "      <td>2017-06-25 00:45:22</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Snoopy. He's a proud #PrideMonthPuppo. Impeccable handwriting for not having thumbs. 13/10 would love back #PrideMonth https://t.co/lNZwgNO4gS</td>\n",
       "      <td>https://twitter.com/dog_rates/status/878776093423087618/photo/1,https://twitter.com/dog_rates/status/878776093423087618/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Snoopy</td>\n",
       "      <td>17139.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>https://pbs.twimg.com/media/DDIKMXzW0AEibje.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.734684</td>\n",
       "      <td>True</td>\n",
       "      <td>whippet</td>\n",
       "      <td>0.150487</td>\n",
       "      <td>True</td>\n",
       "      <td>Ibizan_hound</td>\n",
       "      <td>0.039725</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.734684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6279</th>\n",
       "      <td>825535076884762624</td>\n",
       "      <td>2017-01-29 02:44:34</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Here's a very loving and accepting puppo. Appears to have read her Constitution well. 14/10 would pat head approvingly https://t.co/6ao80wIpV1</td>\n",
       "      <td>https://twitter.com/dog_rates/status/825535076884762624/photo/1</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>53994.0</td>\n",
       "      <td>16363.0</td>\n",
       "      <td>https://pbs.twimg.com/media/C3TjvitXAAAI-QH.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.681495</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.147940</td>\n",
       "      <td>True</td>\n",
       "      <td>black-and-tan_coonhound</td>\n",
       "      <td>0.024525</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.681495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6764</th>\n",
       "      <td>743253157753532416</td>\n",
       "      <td>2016-06-16 01:25:36</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Kilo. He cannot reach the snackum. Nifty tongue, but not nifty enough. 10/10 maybe one day puppo https://t.co/gSmp31Zrsx</td>\n",
       "      <td>https://twitter.com/dog_rates/status/743253157753532416/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Kilo</td>\n",
       "      <td>3935.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>https://pbs.twimg.com/media/ClCQzFUUYAA5vAu.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.442612</td>\n",
       "      <td>True</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.368137</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.177822</td>\n",
       "      <td>True</td>\n",
       "      <td>puppo</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.442612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id           timestamp              source  \\\n",
       "6044  878776093423087618 2017-06-25 00:45:22  Twitter for iPhone   \n",
       "6279  825535076884762624 2017-01-29 02:44:34  Twitter for iPhone   \n",
       "6764  743253157753532416 2016-06-16 01:25:36  Twitter for iPhone   \n",
       "\n",
       "                                                                                                                                                        text  \\\n",
       "6044  This is Snoopy. He's a proud #PrideMonthPuppo. Impeccable handwriting for not having thumbs. 13/10 would love back #PrideMonth https://t.co/lNZwgNO4gS   \n",
       "6279          Here's a very loving and accepting puppo. Appears to have read her Constitution well. 14/10 would pat head approvingly https://t.co/6ao80wIpV1   \n",
       "6764                        This is Kilo. He cannot reach the snackum. Nifty tongue, but not nifty enough. 10/10 maybe one day puppo https://t.co/gSmp31Zrsx   \n",
       "\n",
       "                                                                                                                        expanded_urls  \\\n",
       "6044  https://twitter.com/dog_rates/status/878776093423087618/photo/1,https://twitter.com/dog_rates/status/878776093423087618/photo/1   \n",
       "6279                                                                  https://twitter.com/dog_rates/status/825535076884762624/photo/1   \n",
       "6764                                                                  https://twitter.com/dog_rates/status/743253157753532416/photo/1   \n",
       "\n",
       "      rating_numerator  rating_denominator    name  favorites  retweet_count  \\\n",
       "6044                13                  10  Snoopy    17139.0         3433.0   \n",
       "6279                14                  10    None    53994.0        16363.0   \n",
       "6764                10                  10    Kilo     3935.0         1124.0   \n",
       "\n",
       "                                              jpg_url  img_num  \\\n",
       "6044  https://pbs.twimg.com/media/DDIKMXzW0AEibje.jpg      2.0   \n",
       "6279  https://pbs.twimg.com/media/C3TjvitXAAAI-QH.jpg      1.0   \n",
       "6764  https://pbs.twimg.com/media/ClCQzFUUYAA5vAu.jpg      1.0   \n",
       "\n",
       "                     p1   p1_conf p1_dog               p2   p2_conf p2_dog  \\\n",
       "6044  Italian_greyhound  0.734684   True          whippet  0.150487   True   \n",
       "6279         Rottweiler  0.681495   True  Tibetan_mastiff  0.147940   True   \n",
       "6764           malamute  0.442612   True   Siberian_husky  0.368137   True   \n",
       "\n",
       "                           p3   p3_conf p3_dog dog_type          dog_breed  \\\n",
       "6044             Ibizan_hound  0.039725   True    puppo  Italian_greyhound   \n",
       "6279  black-and-tan_coonhound  0.024525   True    puppo         Rottweiler   \n",
       "6764               Eskimo_dog  0.177822   True    puppo           malamute   \n",
       "\n",
       "      confidence  \n",
       "6044    0.734684  \n",
       "6279    0.681495  \n",
       "6764    0.442612  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will now drop those columns.\n",
    "cols_drop = ['p1','p1_conf','p1_dog','p2','p2_conf', 'p2_dog','p3', 'p3_conf', 'p3_dog']\n",
    "\n",
    "merged_df.drop(cols_drop, axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</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>favorites</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>dog_type</th>\n",
       "      <th>dog_breed</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1918</th>\n",
       "      <td>667405339315146752</td>\n",
       "      <td>2015-11-19 18:13:27</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Biden. Biden just tripped... 7/10 https://t.co/3Fm9PwLju1</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667405339315146752/photo/1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Biden</td>\n",
       "      <td>411.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CUMZnmhUEAEbtis.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Saint_Bernard</td>\n",
       "      <td>0.381377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>667435689202614272</td>\n",
       "      <td>2015-11-19 20:14:03</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Ermergerd 12/10 https://t.co/PQni2sjPsm</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667435689202614272/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>266.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CUM1OHCW4AEgGSi.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.999091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>667437278097252352</td>\n",
       "      <td>2015-11-19 20:20:22</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Never seen this breed before. Very pointy pup. Hurts when you cuddle. Still cute tho. 10/10 https://t.co/97HuBrVuOx</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667437278097252352/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>396.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CUM2qWaWoAUZ06L.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>unknown_breed</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>667443425659232256</td>\n",
       "      <td>2015-11-19 20:44:47</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Exotic dog here. Long neck. Weird paws. Obsessed with bread. Waddles. Flies sometimes (wow!). Very happy dog. 6/10 https://t.co/rqO4I3nf2N</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667443425659232256/photo/1</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>682.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CUM8QZwW4AAVsBl.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>unknown_breed</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>667453023279554560</td>\n",
       "      <td>2015-11-19 21:22:56</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>Meet Cupcake. I would do unspeakable things for Cupcake. 11/10 https://t.co/6uLCWR9Efa</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667453023279554560/photo/1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Cupcake</td>\n",
       "      <td>279.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CUNE_OSUwAAdHhX.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.825670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id           timestamp              source  \\\n",
       "1918  667405339315146752 2015-11-19 18:13:27  Twitter for iPhone   \n",
       "1917  667435689202614272 2015-11-19 20:14:03  Twitter for iPhone   \n",
       "1916  667437278097252352 2015-11-19 20:20:22  Twitter for iPhone   \n",
       "1915  667443425659232256 2015-11-19 20:44:47  Twitter for iPhone   \n",
       "1914  667453023279554560 2015-11-19 21:22:56  Twitter Web Client   \n",
       "\n",
       "                                                                                                                                            text  \\\n",
       "1918                                                                           This is Biden. Biden just tripped... 7/10 https://t.co/3Fm9PwLju1   \n",
       "1917                                                                                                     Ermergerd 12/10 https://t.co/PQni2sjPsm   \n",
       "1916                         Never seen this breed before. Very pointy pup. Hurts when you cuddle. Still cute tho. 10/10 https://t.co/97HuBrVuOx   \n",
       "1915  Exotic dog here. Long neck. Weird paws. Obsessed with bread. Waddles. Flies sometimes (wow!). Very happy dog. 6/10 https://t.co/rqO4I3nf2N   \n",
       "1914                                                      Meet Cupcake. I would do unspeakable things for Cupcake. 11/10 https://t.co/6uLCWR9Efa   \n",
       "\n",
       "                                                        expanded_urls  \\\n",
       "1918  https://twitter.com/dog_rates/status/667405339315146752/photo/1   \n",
       "1917  https://twitter.com/dog_rates/status/667435689202614272/photo/1   \n",
       "1916  https://twitter.com/dog_rates/status/667437278097252352/photo/1   \n",
       "1915  https://twitter.com/dog_rates/status/667443425659232256/photo/1   \n",
       "1914  https://twitter.com/dog_rates/status/667453023279554560/photo/1   \n",
       "\n",
       "      rating_numerator  rating_denominator     name  favorites  retweet_count  \\\n",
       "1918                 7                  10    Biden      411.0          197.0   \n",
       "1917                12                  10     None      266.0           76.0   \n",
       "1916                10                  10     None      396.0          198.0   \n",
       "1915                 6                  10     None      682.0          490.0   \n",
       "1914                11                  10  Cupcake      279.0           74.0   \n",
       "\n",
       "                                              jpg_url  img_num dog_type  \\\n",
       "1918  https://pbs.twimg.com/media/CUMZnmhUEAEbtis.jpg      1.0     None   \n",
       "1917  https://pbs.twimg.com/media/CUM1OHCW4AEgGSi.jpg      1.0     None   \n",
       "1916  https://pbs.twimg.com/media/CUM2qWaWoAUZ06L.jpg      1.0     None   \n",
       "1915  https://pbs.twimg.com/media/CUM8QZwW4AAVsBl.jpg      1.0     None   \n",
       "1914  https://pbs.twimg.com/media/CUNE_OSUwAAdHhX.jpg      1.0     None   \n",
       "\n",
       "               dog_breed  confidence  \n",
       "1918       Saint_Bernard    0.381377  \n",
       "1917          Rottweiler    0.999091  \n",
       "1916       unknown_breed    0.000000  \n",
       "1915       unknown_breed    0.000000  \n",
       "1914  Labrador_retriever    0.825670  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing Data\n",
    "We will now save the gathered, assessed, and cleaned master dataset to a CSV file named \"twitter_archive_master.csv\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df.to_csv('twitter_archive_master.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing and Visualizing Data\n",
    "In this section, we will analyze and visualize the wrangled data. We will be producing up to **four (4) insights and accompanying visualizations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the wrangled dataset into a new dataframe called, df_clean\n",
    "df_clean = pd.read_csv('twitter_archive_master.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <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>favorites</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>dog_type</th>\n",
       "      <th>dog_breed</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>667405339315146752</td>\n",
       "      <td>2015-11-19 18:13:27</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Biden. Biden just tripped... 7/10 https://t.co/3Fm9PwLju1</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667405339315146752/photo/1</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>Biden</td>\n",
       "      <td>411.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CUMZnmhUEAEbtis.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Saint_Bernard</td>\n",
       "      <td>0.381377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>667435689202614272</td>\n",
       "      <td>2015-11-19 20:14:03</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Ermergerd 12/10 https://t.co/PQni2sjPsm</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667435689202614272/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>266.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CUM1OHCW4AEgGSi.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.999091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>667437278097252352</td>\n",
       "      <td>2015-11-19 20:20:22</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Never seen this breed before. Very pointy pup. Hurts when you cuddle. Still cute tho. 10/10 https://t.co/97HuBrVuOx</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667437278097252352/photo/1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>396.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CUM2qWaWoAUZ06L.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>unknown_breed</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>667443425659232256</td>\n",
       "      <td>2015-11-19 20:44:47</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>Exotic dog here. Long neck. Weird paws. Obsessed with bread. Waddles. Flies sometimes (wow!). Very happy dog. 6/10 https://t.co/rqO4I3nf2N</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667443425659232256/photo/1</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>682.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CUM8QZwW4AAVsBl.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>unknown_breed</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>667453023279554560</td>\n",
       "      <td>2015-11-19 21:22:56</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "      <td>Meet Cupcake. I would do unspeakable things for Cupcake. 11/10 https://t.co/6uLCWR9Efa</td>\n",
       "      <td>https://twitter.com/dog_rates/status/667453023279554560/photo/1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Cupcake</td>\n",
       "      <td>279.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>https://pbs.twimg.com/media/CUNE_OSUwAAdHhX.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>None</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.825670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id            timestamp              source  \\\n",
       "0  667405339315146752  2015-11-19 18:13:27  Twitter for iPhone   \n",
       "1  667435689202614272  2015-11-19 20:14:03  Twitter for iPhone   \n",
       "2  667437278097252352  2015-11-19 20:20:22  Twitter for iPhone   \n",
       "3  667443425659232256  2015-11-19 20:44:47  Twitter for iPhone   \n",
       "4  667453023279554560  2015-11-19 21:22:56  Twitter Web Client   \n",
       "\n",
       "                                                                                                                                         text  \\\n",
       "0                                                                           This is Biden. Biden just tripped... 7/10 https://t.co/3Fm9PwLju1   \n",
       "1                                                                                                     Ermergerd 12/10 https://t.co/PQni2sjPsm   \n",
       "2                         Never seen this breed before. Very pointy pup. Hurts when you cuddle. Still cute tho. 10/10 https://t.co/97HuBrVuOx   \n",
       "3  Exotic dog here. Long neck. Weird paws. Obsessed with bread. Waddles. Flies sometimes (wow!). Very happy dog. 6/10 https://t.co/rqO4I3nf2N   \n",
       "4                                                      Meet Cupcake. I would do unspeakable things for Cupcake. 11/10 https://t.co/6uLCWR9Efa   \n",
       "\n",
       "                                                     expanded_urls  \\\n",
       "0  https://twitter.com/dog_rates/status/667405339315146752/photo/1   \n",
       "1  https://twitter.com/dog_rates/status/667435689202614272/photo/1   \n",
       "2  https://twitter.com/dog_rates/status/667437278097252352/photo/1   \n",
       "3  https://twitter.com/dog_rates/status/667443425659232256/photo/1   \n",
       "4  https://twitter.com/dog_rates/status/667453023279554560/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator     name  favorites  retweet_count  \\\n",
       "0                 7                  10    Biden      411.0          197.0   \n",
       "1                12                  10     None      266.0           76.0   \n",
       "2                10                  10     None      396.0          198.0   \n",
       "3                 6                  10     None      682.0          490.0   \n",
       "4                11                  10  Cupcake      279.0           74.0   \n",
       "\n",
       "                                           jpg_url  img_num dog_type  \\\n",
       "0  https://pbs.twimg.com/media/CUMZnmhUEAEbtis.jpg      1.0     None   \n",
       "1  https://pbs.twimg.com/media/CUM1OHCW4AEgGSi.jpg      1.0     None   \n",
       "2  https://pbs.twimg.com/media/CUM2qWaWoAUZ06L.jpg      1.0     None   \n",
       "3  https://pbs.twimg.com/media/CUM8QZwW4AAVsBl.jpg      1.0     None   \n",
       "4  https://pbs.twimg.com/media/CUNE_OSUwAAdHhX.jpg      1.0     None   \n",
       "\n",
       "            dog_breed  confidence  \n",
       "0       Saint_Bernard    0.381377  \n",
       "1          Rottweiler    0.999091  \n",
       "2       unknown_breed    0.000000  \n",
       "3       unknown_breed    0.000000  \n",
       "4  Labrador_retriever    0.825670  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Twitter for iPhone    1955\n",
       "Twitter Web Client      28\n",
       "TweetDeck               11\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.source.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>favorites</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>img_num</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.994000e+03</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.358508e+17</td>\n",
       "      <td>10.573220</td>\n",
       "      <td>10.510030</td>\n",
       "      <td>7710.504032</td>\n",
       "      <td>2244.851815</td>\n",
       "      <td>1.203109</td>\n",
       "      <td>0.464991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.747816e+16</td>\n",
       "      <td>2.187385</td>\n",
       "      <td>7.261522</td>\n",
       "      <td>11379.443289</td>\n",
       "      <td>4021.490596</td>\n",
       "      <td>0.560777</td>\n",
       "      <td>0.339470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.758475e+17</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1644.250000</td>\n",
       "      <td>495.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.084748e+17</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3464.500000</td>\n",
       "      <td>1080.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.459130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.877873e+17</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9556.250000</td>\n",
       "      <td>2553.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.776386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>144901.000000</td>\n",
       "      <td>70750.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.999956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  rating_numerator  rating_denominator      favorites  \\\n",
       "count  1.994000e+03       1994.000000         1994.000000    1984.000000   \n",
       "mean   7.358508e+17         10.573220           10.510030    7710.504032   \n",
       "std    6.747816e+16          2.187385            7.261522   11379.443289   \n",
       "min    6.660209e+17          0.000000            7.000000      66.000000   \n",
       "25%    6.758475e+17         10.000000           10.000000    1644.250000   \n",
       "50%    7.084748e+17         11.000000           10.000000    3464.500000   \n",
       "75%    7.877873e+17         12.000000           10.000000    9556.250000   \n",
       "max    8.924206e+17         14.000000          170.000000  144901.000000   \n",
       "\n",
       "       retweet_count      img_num   confidence  \n",
       "count    1984.000000  1994.000000  1994.000000  \n",
       "mean     2244.851815     1.203109     0.464991  \n",
       "std      4021.490596     0.560777     0.339470  \n",
       "min        11.000000     1.000000     0.000000  \n",
       "25%       495.750000     1.000000     0.140466  \n",
       "50%      1080.000000     1.000000     0.459130  \n",
       "75%      2553.750000     1.000000     0.776386  \n",
       "max     70750.000000     4.000000     0.999956  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unknown_breed               308\n",
       "golden_retriever            158\n",
       "Labrador_retriever          108\n",
       "Pembroke                     95\n",
       "Chihuahua                    91\n",
       "pug                          62\n",
       "toy_poodle                   51\n",
       "chow                         48\n",
       "Pomeranian                   42\n",
       "Samoyed                      42\n",
       "malamute                     33\n",
       "French_bulldog               31\n",
       "Chesapeake_Bay_retriever     31\n",
       "cocker_spaniel               30\n",
       "miniature_pinscher           25\n",
       "Name: dog_breed, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting the number of dog_breeds in descending order\n",
    "df_clean.dog_breed.value_counts().nlargest(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:\n",
    "1. Which dog breed has the highest ratings?\n",
    "\n",
    "2. Among the dog_breeds, which top 10 were tweeted most?\n",
    "\n",
    "3. Which dog breed recorded the highest retweet count?\n",
    "\n",
    "5. Which dog breed has the highest number of likes on average?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have dog_breed named \"Unknown breed\". These are breeeds that we are not able to identify.\n",
    "# We need to remove them because they are not relevant to our analysis.\n",
    "df_clean = df_clean[df_clean.dog_breed !='unknown_breed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "golden_retriever            158\n",
       "Labrador_retriever          108\n",
       "Pembroke                     95\n",
       "Chihuahua                    91\n",
       "pug                          62\n",
       "toy_poodle                   51\n",
       "chow                         48\n",
       "Pomeranian                   42\n",
       "Samoyed                      42\n",
       "malamute                     33\n",
       "Chesapeake_Bay_retriever     31\n",
       "French_bulldog               31\n",
       "cocker_spaniel               30\n",
       "miniature_pinscher           25\n",
       "Eskimo_dog                   22\n",
       "Name: dog_breed, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking again\n",
    "df_clean.dog_breed.value_counts().nlargest(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog_breed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bouvier_des_Flandres</th>\n",
       "      <td>1.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clumber</th>\n",
       "      <td>1.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saluki</th>\n",
       "      <td>4.0</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>briard</th>\n",
       "      <td>3.0</td>\n",
       "      <td>12.333333</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tibetan_mastiff</th>\n",
       "      <td>4.0</td>\n",
       "      <td>12.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.25</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Border_terrier</th>\n",
       "      <td>7.0</td>\n",
       "      <td>12.142857</td>\n",
       "      <td>0.690066</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>silky_terrier</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard_schnauzer</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gordon_setter</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>2.217356</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.25</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Irish_setter</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>1.707825</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.5</td>\n",
       "      <td>12.50</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samoyed</th>\n",
       "      <td>42.0</td>\n",
       "      <td>11.690476</td>\n",
       "      <td>1.352290</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>golden_retriever</th>\n",
       "      <td>158.0</td>\n",
       "      <td>11.594937</td>\n",
       "      <td>1.321252</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great_Pyrenees</th>\n",
       "      <td>15.0</td>\n",
       "      <td>11.533333</td>\n",
       "      <td>1.245946</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.50</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australian_terrier</th>\n",
       "      <td>2.0</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>2.121320</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.5</td>\n",
       "      <td>12.25</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>giant_schnauzer</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>1.732051</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wire-haired_fox_terrier</th>\n",
       "      <td>2.0</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.25</td>\n",
       "      <td>11.5</td>\n",
       "      <td>11.75</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chow</th>\n",
       "      <td>48.0</td>\n",
       "      <td>11.437500</td>\n",
       "      <td>1.366876</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norfolk_terrier</th>\n",
       "      <td>7.0</td>\n",
       "      <td>11.428571</td>\n",
       "      <td>1.133893</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pembroke</th>\n",
       "      <td>95.0</td>\n",
       "      <td>11.410526</td>\n",
       "      <td>1.716594</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eskimo_dog</th>\n",
       "      <td>22.0</td>\n",
       "      <td>11.409091</td>\n",
       "      <td>1.402688</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count       mean       std   min    25%   50%    75%  \\\n",
       "dog_breed                                                                       \n",
       "Bouvier_des_Flandres       1.0  13.000000       NaN  13.0  13.00  13.0  13.00   \n",
       "clumber                    1.0  13.000000       NaN  13.0  13.00  13.0  13.00   \n",
       "Saluki                     4.0  12.500000  1.000000  11.0  12.50  13.0  13.00   \n",
       "briard                     3.0  12.333333  0.577350  12.0  12.00  12.0  12.50   \n",
       "Tibetan_mastiff            4.0  12.250000  0.500000  12.0  12.00  12.0  12.25   \n",
       "Border_terrier             7.0  12.142857  0.690066  11.0  12.00  12.0  12.50   \n",
       "silky_terrier              1.0  12.000000       NaN  12.0  12.00  12.0  12.00   \n",
       "standard_schnauzer         1.0  12.000000       NaN  12.0  12.00  12.0  12.00   \n",
       "Gordon_setter              4.0  11.750000  2.217356   9.0  10.50  12.0  13.25   \n",
       "Irish_setter               4.0  11.750000  1.707825  10.0  10.75  11.5  12.50   \n",
       "Samoyed                   42.0  11.690476  1.352290   7.0  11.00  12.0  13.00   \n",
       "golden_retriever         158.0  11.594937  1.321252   5.0  11.00  12.0  12.00   \n",
       "Great_Pyrenees            15.0  11.533333  1.245946   9.0  11.00  12.0  12.50   \n",
       "Australian_terrier         2.0  11.500000  2.121320  10.0  10.75  11.5  12.25   \n",
       "giant_schnauzer            4.0  11.500000  1.732051  10.0  10.00  11.5  13.00   \n",
       "wire-haired_fox_terrier    2.0  11.500000  0.707107  11.0  11.25  11.5  11.75   \n",
       "chow                      48.0  11.437500  1.366876   7.0  11.00  12.0  12.00   \n",
       "Norfolk_terrier            7.0  11.428571  1.133893  10.0  10.50  12.0  12.00   \n",
       "Pembroke                  95.0  11.410526  1.716594   4.0  11.00  12.0  12.00   \n",
       "Eskimo_dog                22.0  11.409091  1.402688   9.0  10.00  12.0  12.00   \n",
       "\n",
       "                          max  \n",
       "dog_breed                      \n",
       "Bouvier_des_Flandres     13.0  \n",
       "clumber                  13.0  \n",
       "Saluki                   13.0  \n",
       "briard                   13.0  \n",
       "Tibetan_mastiff          13.0  \n",
       "Border_terrier           13.0  \n",
       "silky_terrier            12.0  \n",
       "standard_schnauzer       12.0  \n",
       "Gordon_setter            14.0  \n",
       "Irish_setter             14.0  \n",
       "Samoyed                  14.0  \n",
       "golden_retriever         14.0  \n",
       "Great_Pyrenees           13.0  \n",
       "Australian_terrier       13.0  \n",
       "giant_schnauzer          13.0  \n",
       "wire-haired_fox_terrier  12.0  \n",
       "chow                     13.0  \n",
       "Norfolk_terrier          13.0  \n",
       "Pembroke                 14.0  \n",
       "Eskimo_dog               14.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will analyze the summary statistics of each dog breed based on their tweet rating, \n",
    "# and this is sorted in descending order of maximum tweet rating for the first 20 dog_breeds\n",
    "df_clean.groupby('dog_breed')['rating_numerator'].describe().nlargest(20, 'mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can analyze the average tweet rating of the top 20 dog breeds\n",
    "plt.figure(figsize=(16,10))\n",
    "df_clean.groupby('dog_breed')['rating_numerator'].mean().sort_values(ascending=False).nlargest(20).plot(kind='barh')\n",
    "plt.title('Top 20 Average Tweet Rating of Dog Breeds')\n",
    "plt.ylabel(None)\n",
    "plt.xlabel('Tweet Rating');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Bourvier Des Flandres and Clumber recorded the highest average tweet rating with an average tweet rating of 13, followed by Saluki with average tweet rating of 12.5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "golden_retriever      158\n",
       "Labrador_retriever    108\n",
       "Pembroke               95\n",
       "Chihuahua              91\n",
       "pug                    62\n",
       "toy_poodle             51\n",
       "chow                   48\n",
       "Pomeranian             42\n",
       "Samoyed                42\n",
       "malamute               33\n",
       "Name: dog_breed, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['dog_breed'].value_counts().nlargest(10).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will now plot the top 10 dog breeds tweeted on the weratedog handle\n",
    "plt.figure(figsize=(12,8))\n",
    "df_clean['dog_breed'].value_counts().nlargest(10).sort_values(ascending=False).plot(kind='barh')\n",
    "plt.xlabel('Tweet Count')\n",
    "plt.title('Top 10 Tweeted Breeds of Dogs');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Golden Retriever recorded the highest tweet count of 158, followed by Labrador Retriever with tweet count of 108**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>retweet_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog_breed</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>golden_retriever</th>\n",
       "      <td>477396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labrador_retriever</th>\n",
       "      <td>313615.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pembroke</th>\n",
       "      <td>236940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chihuahua</th>\n",
       "      <td>210907.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samoyed</th>\n",
       "      <td>156134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French_bulldog</th>\n",
       "      <td>132219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cocker_spaniel</th>\n",
       "      <td>118787.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chow</th>\n",
       "      <td>107263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pug</th>\n",
       "      <td>94427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pomeranian</th>\n",
       "      <td>91599.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    retweet_count\n",
       "dog_breed                        \n",
       "golden_retriever         477396.0\n",
       "Labrador_retriever       313615.0\n",
       "Pembroke                 236940.0\n",
       "Chihuahua                210907.0\n",
       "Samoyed                  156134.0\n",
       "French_bulldog           132219.0\n",
       "cocker_spaniel           118787.0\n",
       "chow                     107263.0\n",
       "pug                       94427.0\n",
       "Pomeranian                91599.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_clean.groupby('dog_breed')['retweet_count'].sum().nlargest(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dog breeds with highest retweet count.\n",
    "plt.figure(figsize = (12,7))\n",
    "df_clean.groupby('dog_breed')['retweet_count'].sum().nlargest(10).plot(kind='barh', label=None)\n",
    "plt.title('Dog Breeds With Highest Number of Retweets\\n')\n",
    "plt.ylabel(None)\n",
    "plt.xlabel('\\nNumber of Retweets');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Golden Retrieve has the highest number of retweets with over 470,000 retweets, and it is followed by Labrador Retriever with over 31,000 retweets**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In order to find the average number of likes (favorites) per tweet per dog breed, we need to first find the total count of tweets and the total number of likes.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of retweet count for each dog\n",
    "count_likes = df_clean.groupby('dog_breed')['favorites'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_likes = df_clean.groupby('dog_breed')['favorites'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average number of likes per tweet per dog\n",
    "avg_like = sum_likes/count_likes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dog_breed\n",
       "Bedlington_terrier         22850.800000\n",
       "Saluki                     20808.750000\n",
       "French_bulldog             17540.666667\n",
       "Bouvier_des_Flandres       15357.000000\n",
       "Afghan_hound               14708.666667\n",
       "black-and-tan_coonhound    14673.000000\n",
       "flat-coated_retriever      14523.500000\n",
       "Irish_water_spaniel        13981.666667\n",
       "whippet                    12709.545455\n",
       "Leonberg                   12659.000000\n",
       "Name: favorites, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_like.nlargest(10).nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,7))\n",
    "avg_like.nlargest(10).plot(kind='barh')\n",
    "plt.title('Top 10 Dog Breeds With Highest Average Number of Likes Per Tweet\\n')\n",
    "plt.ylabel(None)\n",
    "plt.xlabel('\\nAverage Number of Likes per tweet');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Bedlington Terrier has the highest likes on average to the tune of 22,851 approximately, and it is followed by Saluki with approximately 20,809 likes on average**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "1. Bourvier Des Flandres and Clumber recorded the highest average tweet rating with an average tweet rating of 13, followed by Saluki with average tweet rating of 12.5.\n",
    "\n",
    "2. Golden Retriever recorded the highest tweet count of 158, followed by Labrador Retriever with tweet count of 108.\n",
    "\n",
    "3. Golden Retrieve has the highest number of retweets with over 470,000 retweets, and it is followed by Labrador Retriever with over 31,000 retweets.\n",
    "\n",
    "4. Bedlington Terrier has the highest likes on average to the tune of 22,851 approximately, and it is followed by Saluki with approximately 20,809 likes on average\n",
    "\n",
    "We can conclude that based on number, Golden Retriever is most popular dog breed in homes, while Berlington Terrier has the highest audience vote."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "https://en.wikipedia.org/wiki/WeRateDogs\n",
    "\n",
    "https://www.dailydot.com/debug/twitter-dog-rates-dmca-takedown/\n",
    "\n",
    "https://www.vice.com/en/article/aekvg8/twitter-rate-dogs-account-suspended\n",
    "\n",
    "https://help.twitter.com/en/managing-your-account/how-to-download-your-twitter-archive\n"
   ]
  }
 ],
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