wrangle_act.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tweepy\n",
    "import json\n",
    "from pprint import pprint\n",
    "plt.style.use(\"seaborn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Authenticate to Twitter\n",
    "consumer_key = ####\n",
    "consumer_secret = ####\n",
    "ACCESS_TOKEN = ####\n",
    "ACCESS_TOKEN_SECRET = ####\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create API object\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Authentication OK\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    api.verify_credentials()\n",
    "    print(\"Authentication OK\")\n",
    "except:\n",
    "    print(\"Error during authentication\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gather"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "taking the IDs from the tw DataFrame since that has only the tweets that we want. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweepy Error: [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "Tweepy Error: [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "Tweepy Error: [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "Tweepy Error: [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "Tweepy Error: [{'code': 144, 'message': 'No status found with that ID.'}]\n",
      "Tweepy Error: [{'code': 144, 'message': 'No status found with that ID.'}]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Rate limit reached. Sleeping for: 147\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweepy Error: [{'code': 144, 'message': 'No status found with that ID.'}]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Rate limit reached. Sleeping for: 353\n"
     ]
    }
   ],
   "source": [
    "# WARNING: run this cell after having assessed and cleaned tw\n",
    "# collecting tweets by looping over IDs in tw dataframe    \n",
    "with open('tweet_json.txt', 'w') as outfile:\n",
    "    for id in tw.tweet_id:\n",
    "        try:\n",
    "            tweet = api.get_status(id, tweet_mode='extended')\n",
    "            json.dump(tweet._json, outfile)\n",
    "            outfile.write('\\n') \n",
    "        except tweepy.TweepError as e:\n",
    "            print(\"Tweepy Error: {}\".format(e))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# saving tweets to tweet_json.txt file.\n",
    "with open('tweet_json.txt', mode='r') as json_file:\n",
    "    dict_list = []\n",
    "    for line in json_file:\n",
    "        tmp_dict ={}\n",
    "        tweet_json = json.loads(line)\n",
    "        tmp_dict[\"tweet_id\"] = tweet_json['id']\n",
    "        tmp_dict['favorite_count'] = tweet_json['favorite_count']\n",
    "        tmp_dict['retweet_count'] = tweet_json['retweet_count']\n",
    "        dict_list.append(tmp_dict)\n",
    "#         pprint(tmp_dict)\n",
    "#         break\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets = pd.DataFrame(dict_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "downloading image_predictions.tsv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.get(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "335079"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(\"image_predictions.tsv\", mode='wb').write(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tweets table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2090 entries, 0 to 2089\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   tweet_id        2090 non-null   int64\n",
      " 1   favorite_count  2090 non-null   int64\n",
      " 2   retweet_count   2090 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 49.1 KB\n"
     ]
    }
   ],
   "source": [
    "tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>35169</td>\n",
       "      <td>7421</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>30437</td>\n",
       "      <td>5518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>22884</td>\n",
       "      <td>3645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>38467</td>\n",
       "      <td>7595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>36721</td>\n",
       "      <td>8173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  favorite_count  retweet_count\n",
       "0  892420643555336193           35169           7421\n",
       "1  892177421306343426           30437           5518\n",
       "2  891815181378084864           22884           3645\n",
       "3  891689557279858688           38467           7595\n",
       "4  891327558926688256           36721           8173"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2085</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>94</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>261</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2087</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>107</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2088</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>118</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2089</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2338</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  favorite_count  retweet_count\n",
       "2085  666049248165822465              94             38\n",
       "2086  666044226329800704             261            122\n",
       "2087  666033412701032449             107             39\n",
       "2088  666029285002620928             118             41\n",
       "2089  666020888022790149            2338            445"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "checking for duplicates in `tweet_id` col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tweets[tweets.duplicated(keep=False, subset=['tweet_id'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>757354760399941633</td>\n",
       "      <td>4404</td>\n",
       "      <td>1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>669214165781868544</td>\n",
       "      <td>404</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1589</th>\n",
       "      <td>676098748976615425</td>\n",
       "      <td>2834</td>\n",
       "      <td>1330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>886366144734445568</td>\n",
       "      <td>19284</td>\n",
       "      <td>2783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>740676976021798912</td>\n",
       "      <td>17571</td>\n",
       "      <td>6573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                tweet_id  favorite_count  retweet_count\n",
       "706   757354760399941633            4404           1380\n",
       "1913  669214165781868544             404            148\n",
       "1589  676098748976615425            2834           1330\n",
       "28    886366144734445568           19284           2783\n",
       "846   740676976021798912           17571           6573"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "twitter table looks good and does not need any cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## twitter_arch table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "reading twitter-archive-enhanced.csv into a df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_arch = pd.read_csv(\"twitter-archive-enhanced.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </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",
       "\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",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "\n",
       "   rating_denominator     name doggo floofer pupper puppo  \n",
       "0                  10  Phineas  None    None   None  None  \n",
       "1                  10    Tilly  None    None   None  None  \n",
       "2                  10   Archie  None    None   None  None  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_arch.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
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       "      <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>30</th>\n",
       "      <td>886267009285017600</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>2.281182e+09</td>\n",
       "      <td>2017-07-15 16:51:35 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@NonWhiteHat @MayhewMayhem omg hello tanner yo...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>12</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",
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       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>881633300179243008</td>\n",
       "      <td>8.816070e+17</td>\n",
       "      <td>4.738443e+07</td>\n",
       "      <td>2017-07-02 21:58:53 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@roushfenway These are good dogs but 17/10 is ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</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>64</th>\n",
       "      <td>879674319642796034</td>\n",
       "      <td>8.795538e+17</td>\n",
       "      <td>3.105441e+09</td>\n",
       "      <td>2017-06-27 12:14:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@RealKentMurphy 14/10 confirmed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</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>113</th>\n",
       "      <td>870726314365509632</td>\n",
       "      <td>8.707262e+17</td>\n",
       "      <td>1.648776e+07</td>\n",
       "      <td>2017-06-02 19:38:25 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@ComplicitOwl @ShopWeRateDogs &amp;gt;10/10 is res...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>863427515083354112</td>\n",
       "      <td>8.634256e+17</td>\n",
       "      <td>7.759620e+07</td>\n",
       "      <td>2017-05-13 16:15:35 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>@Jack_Septic_Eye I'd need a few more pics to p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2038</th>\n",
       "      <td>671550332464455680</td>\n",
       "      <td>6.715449e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-12-01 04:44:10 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>After 22 minutes of careful deliberation this ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2149</th>\n",
       "      <td>669684865554620416</td>\n",
       "      <td>6.693544e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-26 01:11:28 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>After countless hours of research and hundreds...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2169</th>\n",
       "      <td>669353438988365824</td>\n",
       "      <td>6.678065e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-25 03:14:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tessa. She is also very pleased after ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/669353438...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>Tessa</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2189</th>\n",
       "      <td>668967877119254528</td>\n",
       "      <td>6.689207e+17</td>\n",
       "      <td>2.143566e+07</td>\n",
       "      <td>2015-11-24 01:42:25 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>12/10 good shit Bubka\\n@wane15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</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>2298</th>\n",
       "      <td>667070482143944705</td>\n",
       "      <td>6.670655e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2015-11-18 20:02:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>After much debate this dog is being upgraded t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "30    886267009285017600           8.862664e+17         2.281182e+09   \n",
       "55    881633300179243008           8.816070e+17         4.738443e+07   \n",
       "64    879674319642796034           8.795538e+17         3.105441e+09   \n",
       "113   870726314365509632           8.707262e+17         1.648776e+07   \n",
       "148   863427515083354112           8.634256e+17         7.759620e+07   \n",
       "...                  ...                    ...                  ...   \n",
       "2038  671550332464455680           6.715449e+17         4.196984e+09   \n",
       "2149  669684865554620416           6.693544e+17         4.196984e+09   \n",
       "2169  669353438988365824           6.678065e+17         4.196984e+09   \n",
       "2189  668967877119254528           6.689207e+17         2.143566e+07   \n",
       "2298  667070482143944705           6.670655e+17         4.196984e+09   \n",
       "\n",
       "                      timestamp  \\\n",
       "30    2017-07-15 16:51:35 +0000   \n",
       "55    2017-07-02 21:58:53 +0000   \n",
       "64    2017-06-27 12:14:36 +0000   \n",
       "113   2017-06-02 19:38:25 +0000   \n",
       "148   2017-05-13 16:15:35 +0000   \n",
       "...                         ...   \n",
       "2038  2015-12-01 04:44:10 +0000   \n",
       "2149  2015-11-26 01:11:28 +0000   \n",
       "2169  2015-11-25 03:14:30 +0000   \n",
       "2189  2015-11-24 01:42:25 +0000   \n",
       "2298  2015-11-18 20:02:51 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "30    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "55    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "64    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "113   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "148   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2038  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2149  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2169  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2189  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2298  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "30    @NonWhiteHat @MayhewMayhem omg hello tanner yo...                  NaN   \n",
       "55    @roushfenway These are good dogs but 17/10 is ...                  NaN   \n",
       "64                      @RealKentMurphy 14/10 confirmed                  NaN   \n",
       "113   @ComplicitOwl @ShopWeRateDogs &gt;10/10 is res...                  NaN   \n",
       "148   @Jack_Septic_Eye I'd need a few more pics to p...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "2038  After 22 minutes of careful deliberation this ...                  NaN   \n",
       "2149  After countless hours of research and hundreds...                  NaN   \n",
       "2169  This is Tessa. She is also very pleased after ...                  NaN   \n",
       "2189                     12/10 good shit Bubka\\n@wane15                  NaN   \n",
       "2298  After much debate this dog is being upgraded t...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "30                         NaN                        NaN   \n",
       "55                         NaN                        NaN   \n",
       "64                         NaN                        NaN   \n",
       "113                        NaN                        NaN   \n",
       "148                        NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2038                       NaN                        NaN   \n",
       "2149                       NaN                        NaN   \n",
       "2169                       NaN                        NaN   \n",
       "2189                       NaN                        NaN   \n",
       "2298                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "30                                                  NaN                12   \n",
       "55                                                  NaN                17   \n",
       "64                                                  NaN                14   \n",
       "113                                                 NaN                10   \n",
       "148                                                 NaN                12   \n",
       "...                                                 ...               ...   \n",
       "2038                                                NaN                 1   \n",
       "2149                                                NaN                11   \n",
       "2169  https://twitter.com/dog_rates/status/669353438...                10   \n",
       "2189                                                NaN                12   \n",
       "2298                                                NaN                10   \n",
       "\n",
       "      rating_denominator   name doggo floofer pupper puppo  \n",
       "30                    10   None  None    None   None  None  \n",
       "55                    10   None  None    None   None  None  \n",
       "64                    10   None  None    None   None  None  \n",
       "113                   10   None  None    None   None  None  \n",
       "148                   10   None  None    None   None  None  \n",
       "...                  ...    ...   ...     ...    ...   ...  \n",
       "2038                  10   None  None    None   None  None  \n",
       "2149                  10   None  None    None   None  None  \n",
       "2169                  10  Tessa  None    None   None  None  \n",
       "2189                  10   None  None    None   None  None  \n",
       "2298                  10   None  None    None   None  None  \n",
       "\n",
       "[78 rows x 17 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch[twitter_arch.in_reply_to_status_id.notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "      <td>2017-02-18 17:00:10 +0000</td>\n",
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       "      <td>Meet General. He wasn't content with the quali...</td>\n",
       "      <td>2016-05-19 01:38:16 +0000</td>\n",
       "      <td>https://www.gofundme.com/helpquinny,https://tw...</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>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>2221</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>745</td>\n",
       "      <td>2259</td>\n",
       "      <td>2346</td>\n",
       "      <td>2099</td>\n",
       "      <td>2326</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        timestamp  \\\n",
       "count                        2356   \n",
       "unique                       2356   \n",
       "top     2017-02-18 17:00:10 +0000   \n",
       "freq                            1   \n",
       "\n",
       "                                                   source  \\\n",
       "count                                                2356   \n",
       "unique                                                  4   \n",
       "top     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "freq                                                 2221   \n",
       "\n",
       "                                                     text  \\\n",
       "count                                                2356   \n",
       "unique                                               2356   \n",
       "top     Meet General. He wasn't content with the quali...   \n",
       "freq                                                    1   \n",
       "\n",
       "       retweeted_status_timestamp  \\\n",
       "count                         181   \n",
       "unique                        181   \n",
       "top     2016-05-19 01:38:16 +0000   \n",
       "freq                            1   \n",
       "\n",
       "                                            expanded_urls  name doggo floofer  \\\n",
       "count                                                2297  2356  2356    2356   \n",
       "unique                                               2218   957     2       2   \n",
       "top     https://www.gofundme.com/helpquinny,https://tw...  None  None    None   \n",
       "freq                                                    2   745  2259    2346   \n",
       "\n",
       "       pupper puppo  \n",
       "count    2356  2356  \n",
       "unique      2     2  \n",
       "top      None  None  \n",
       "freq     2099  2326  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.describe(include=\"O\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                         0\n",
       "in_reply_to_status_id         2278\n",
       "in_reply_to_user_id           2278\n",
       "timestamp                        0\n",
       "source                           0\n",
       "text                             0\n",
       "retweeted_status_id           2175\n",
       "retweeted_status_user_id      2175\n",
       "retweeted_status_timestamp    2175\n",
       "expanded_urls                   59\n",
       "rating_numerator                 0\n",
       "rating_denominator               0\n",
       "name                             0\n",
       "doggo                            0\n",
       "floofer                          0\n",
       "pupper                           0\n",
       "puppo                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NaNs for each column\n",
    "twitter_arch.isna().sum(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      558\n",
       "11      464\n",
       "10      461\n",
       "13      351\n",
       "9       158\n",
       "8       102\n",
       "7        55\n",
       "14       54\n",
       "5        37\n",
       "6        32\n",
       "3        19\n",
       "4        17\n",
       "1         9\n",
       "2         9\n",
       "420       2\n",
       "0         2\n",
       "15        2\n",
       "75        2\n",
       "80        1\n",
       "20        1\n",
       "24        1\n",
       "26        1\n",
       "44        1\n",
       "50        1\n",
       "60        1\n",
       "165       1\n",
       "84        1\n",
       "88        1\n",
       "144       1\n",
       "182       1\n",
       "143       1\n",
       "666       1\n",
       "960       1\n",
       "1776      1\n",
       "17        1\n",
       "27        1\n",
       "45        1\n",
       "99        1\n",
       "121       1\n",
       "204       1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 40 unique ratings\n",
    "twitter_arch.rating_numerator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None       745\n",
       "a           55\n",
       "Charlie     12\n",
       "Lucy        11\n",
       "Cooper      11\n",
       "          ... \n",
       "Grizzie      1\n",
       "Samsom       1\n",
       "Ulysses      1\n",
       "Ginger       1\n",
       "Gunner       1\n",
       "Name: name, Length: 957, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "957"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.name.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "202          None\n",
       "136          Coco\n",
       "1567        Louis\n",
       "1310    Kathmandu\n",
       "1027        Arlen\n",
       "921          None\n",
       "1505         None\n",
       "247          None\n",
       "1362           an\n",
       "1661         Lulu\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.name.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None       2346\n",
      "floofer      10\n",
      "Name: floofer, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(twitter_arch.floofer.value_counts())                            \n",
    "# floofer                          \n",
    "# pupper                           \n",
    "# puppo   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>name</th>\n",
       "      <th>floofer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Grizzwald</td>\n",
       "      <td>floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>None</td>\n",
       "      <td>floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>Doc</td>\n",
       "      <td>floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>None</td>\n",
       "      <td>floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>Blu</td>\n",
       "      <td>floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1022</th>\n",
       "      <td>None</td>\n",
       "      <td>floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>None</td>\n",
       "      <td>floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>Moose</td>\n",
       "      <td>floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>None</td>\n",
       "      <td>floofer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>Petrick</td>\n",
       "      <td>floofer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  floofer\n",
       "46    Grizzwald  floofer\n",
       "200        None  floofer\n",
       "582         Doc  floofer\n",
       "774        None  floofer\n",
       "984         Blu  floofer\n",
       "1022       None  floofer\n",
       "1091       None  floofer\n",
       "1110      Moose  floofer\n",
       "1534       None  floofer\n",
       "1614    Petrick  floofer"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.loc[twitter_arch.floofer=='floofer', ['name', 'floofer']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2356 - 2175"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',\n",
       "       'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',\n",
       "       'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.columns\n"
   ]
  },
  {
   "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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Here is a pupper approaching maximum borkdrive...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>Here is a perfect example of someone who has t...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>Guys this is getting so out of hand. We only r...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>This is a mighty rare blue-tailed hammer sherk...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>Viewer discretion is advised. This is a terrib...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>This is a carrot. We only rate dogs. Please on...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>This is a very rare Great Alaskan Bush Pupper....</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>People please. This is a Deadly Mediterranean ...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>This is a taco. We only rate dogs. Please only...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>Here is a heartbreaking scene of an incredible...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1351</th>\n",
       "      <td>Here is a whole flock of puppers.  60/50 I'll ...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1361</th>\n",
       "      <td>This is a Butternut Cumberfloof. It's not wind...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>This is a Wild Tuscan Poofwiggle. Careful not ...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>\"Pupper is a present to world. Here is a bow f...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>This is a rare Arctic Wubberfloof. Unamused by...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>Guys this really needs to stop. We've been ove...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1785</th>\n",
       "      <td>This is a dog swinging. I really enjoyed it so...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>This is a Sizzlin Menorah spaniel from Brookly...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1854</th>\n",
       "      <td>Seriously guys?! Only send in dogs. I only rat...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>C'mon guys. We've been over this. We only rate...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>This is a fluffy albino Bacardi Columbia mix. ...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>This is a Sagitariot Baklava mix. Loves her ne...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>This is a heavily opinionated dog. Loves walls...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>This is a Lofted Aphrodisiac Terrier named Kip...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>This is a baby Rand Paul. Curls for days. 11/1...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>This is a Tuscaloosa Alcatraz named Jacob (Yac...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>This is a Helvetica Listerine named Rufus. Thi...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>This is a Deciduous Trimester mix named Spork....</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2125</th>\n",
       "      <td>This is a Rich Mahogany Seltzer named Cherokee...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>This is a Speckled Cauliflower Yosemite named ...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2146</th>\n",
       "      <td>This is a spotted Lipitor Rumpelstiltskin name...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2153</th>\n",
       "      <td>This is a brave dog. Excellent free climber. T...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>This is a Coriander Baton Rouge named Alfredo....</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>This is a Slovakian Helter Skelter Feta named ...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2198</th>\n",
       "      <td>This is a wild Toblerone from Papua New Guinea...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>Here is a horned dog. Much grace. Can jump ove...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2218</th>\n",
       "      <td>This is a Birmingham Quagmire named Chuk. Love...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>Here is a mother dog caring for her pups. Snaz...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>This is a Trans Siberian Kellogg named Alfonso...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>This is a Shotokon Macadamia mix named Cheryl....</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>This is a rare Hungarian Pinot named Jessiga. ...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2264</th>\n",
       "      <td>This is a southwest Coriander named Klint. Hat...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>This is a northern Wahoo named Kohl. He runs t...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>This is a Dasani Kingfisher from Maine. His na...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>This is a curly Ticonderoga named Pepe. No fee...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2311</th>\n",
       "      <td>This is a purebred Bacardi named Octaviath. Ca...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>This is a golden Buckminsterfullerene named Jo...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>This is a southern Vesuvius bumblegruff. Can d...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>This is a funny dog. Weird toes. Won't come do...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>My oh my. This is a rare blond Canadian terrie...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>Here is a Siberian heavily armored polar bear ...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>This is a truly beautiful English Wilson Staff...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text name\n",
       "56    Here is a pupper approaching maximum borkdrive...    a\n",
       "649   Here is a perfect example of someone who has t...    a\n",
       "801   Guys this is getting so out of hand. We only r...    a\n",
       "1002  This is a mighty rare blue-tailed hammer sherk...    a\n",
       "1004  Viewer discretion is advised. This is a terrib...    a\n",
       "1017  This is a carrot. We only rate dogs. Please on...    a\n",
       "1049  This is a very rare Great Alaskan Bush Pupper....    a\n",
       "1193  People please. This is a Deadly Mediterranean ...    a\n",
       "1207  This is a taco. We only rate dogs. Please only...    a\n",
       "1340  Here is a heartbreaking scene of an incredible...    a\n",
       "1351  Here is a whole flock of puppers.  60/50 I'll ...    a\n",
       "1361  This is a Butternut Cumberfloof. It's not wind...    a\n",
       "1368  This is a Wild Tuscan Poofwiggle. Careful not ...    a\n",
       "1382  \"Pupper is a present to world. Here is a bow f...    a\n",
       "1499  This is a rare Arctic Wubberfloof. Unamused by...    a\n",
       "1737  Guys this really needs to stop. We've been ove...    a\n",
       "1785  This is a dog swinging. I really enjoyed it so...    a\n",
       "1853  This is a Sizzlin Menorah spaniel from Brookly...    a\n",
       "1854  Seriously guys?! Only send in dogs. I only rat...    a\n",
       "1877  C'mon guys. We've been over this. We only rate...    a\n",
       "1878  This is a fluffy albino Bacardi Columbia mix. ...    a\n",
       "1923  This is a Sagitariot Baklava mix. Loves her ne...    a\n",
       "1941  This is a heavily opinionated dog. Loves walls...    a\n",
       "1955  This is a Lofted Aphrodisiac Terrier named Kip...    a\n",
       "1994  This is a baby Rand Paul. Curls for days. 11/1...    a\n",
       "2034  This is a Tuscaloosa Alcatraz named Jacob (Yac...    a\n",
       "2066  This is a Helvetica Listerine named Rufus. Thi...    a\n",
       "2116  This is a Deciduous Trimester mix named Spork....    a\n",
       "2125  This is a Rich Mahogany Seltzer named Cherokee...    a\n",
       "2128  This is a Speckled Cauliflower Yosemite named ...    a\n",
       "2146  This is a spotted Lipitor Rumpelstiltskin name...    a\n",
       "2153  This is a brave dog. Excellent free climber. T...    a\n",
       "2161  This is a Coriander Baton Rouge named Alfredo....    a\n",
       "2191  This is a Slovakian Helter Skelter Feta named ...    a\n",
       "2198  This is a wild Toblerone from Papua New Guinea...    a\n",
       "2211  Here is a horned dog. Much grace. Can jump ove...    a\n",
       "2218  This is a Birmingham Quagmire named Chuk. Love...    a\n",
       "2222  Here is a mother dog caring for her pups. Snaz...    a\n",
       "2235  This is a Trans Siberian Kellogg named Alfonso...    a\n",
       "2249  This is a Shotokon Macadamia mix named Cheryl....    a\n",
       "2255  This is a rare Hungarian Pinot named Jessiga. ...    a\n",
       "2264  This is a southwest Coriander named Klint. Hat...    a\n",
       "2273  This is a northern Wahoo named Kohl. He runs t...    a\n",
       "2287  This is a Dasani Kingfisher from Maine. His na...    a\n",
       "2304  This is a curly Ticonderoga named Pepe. No fee...    a\n",
       "2311  This is a purebred Bacardi named Octaviath. Ca...    a\n",
       "2314  This is a golden Buckminsterfullerene named Jo...    a\n",
       "2327  This is a southern Vesuvius bumblegruff. Can d...    a\n",
       "2334  This is a funny dog. Weird toes. Won't come do...    a\n",
       "2347  My oh my. This is a rare blond Canadian terrie...    a\n",
       "2348  Here is a Siberian heavily armored polar bear ...    a\n",
       "2350  This is a truly beautiful English Wilson Staff...    a\n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...    a\n",
       "2353  Here is a very happy pup. Big fan of well-main...    a\n",
       "2354  This is a western brown Mitsubishi terrier. Up...    a"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.loc[twitter_arch.name == 'a', ['text', 'name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(twitter_arch.loc[twitter_arch.name == 'a', ['text', 'name']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'This is a Tuscaloosa Alcatraz named Jacob (Yacōb). Loves to sit in swing. Stellar tongue. 11/10 look at his feet https://t.co/2IslQ8ZSc7'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this dog is names Jacob but listed as 'a' in name column\n",
    "twitter_arch.iloc[2034, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',\n",
       "       'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',\n",
       "       'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_arch.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicates\n",
    "twitter_arch.duplicated(keep=False, subset=\"tweet_id\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### `twitter_arch` table\n",
    "- `doggo`, `floofer`, `pupper` and `puppo` columns all contain \"None\" instead of NaN\n",
    "- `doggo`, `floofer`, `pupper` and `puppo` columns should all be dropped\n",
    "- a lot of missing values for dog stages\n",
    "- some dogs have more than 1 dog stage\n",
    "- 745 dog names set to \"None\" instead of NaN\n",
    "- a lot of missing values for dog names\n",
    "- some dog names don't sound like dog names at all\n",
    "- 55 dogs are named 'a' and some of those have names in the text column and some don't\n",
    "- `expanded_urls` contains missing vlaues (NaNs)\n",
    "- there are 2356 - 2175 = 181 retweets in this data set (not needed)    \n",
    "- there are 78 replies to tweets in this data set (not needed)\n",
    "- wrong datatype for `timestamp` column\n",
    "- `in_reply_to_status_id`, `in_reply_to_user_id`, `retweeted_status_id`, `retweeted_status_user_id` and  \n",
    "`retweeted_status_timestamp` columns not relevant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tidiness Issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- dog stages should be grouped under 1 column called `dog_stage`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## image_pred table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "loading tsv file into a df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_pred = pd.read_csv(\"image_predictions.tsv\", sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "image_pred.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.384514e+17</td>\n",
       "      <td>1.203855</td>\n",
       "      <td>0.594548</td>\n",
       "      <td>1.345886e-01</td>\n",
       "      <td>6.032417e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.785203e+16</td>\n",
       "      <td>0.561875</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>1.006657e-01</td>\n",
       "      <td>5.090593e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.764835e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364412</td>\n",
       "      <td>5.388625e-02</td>\n",
       "      <td>1.622240e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.119988e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588230</td>\n",
       "      <td>1.181810e-01</td>\n",
       "      <td>4.944380e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.932034e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843855</td>\n",
       "      <td>1.955655e-01</td>\n",
       "      <td>9.180755e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id      img_num      p1_conf       p2_conf       p3_conf\n",
       "count  2.075000e+03  2075.000000  2075.000000  2.075000e+03  2.075000e+03\n",
       "mean   7.384514e+17     1.203855     0.594548  1.345886e-01  6.032417e-02\n",
       "std    6.785203e+16     0.561875     0.271174  1.006657e-01  5.090593e-02\n",
       "min    6.660209e+17     1.000000     0.044333  1.011300e-08  1.740170e-10\n",
       "25%    6.764835e+17     1.000000     0.364412  5.388625e-02  1.622240e-02\n",
       "50%    7.119988e+17     1.000000     0.588230  1.181810e-01  4.944380e-02\n",
       "75%    7.932034e+17     1.000000     0.843855  1.955655e-01  9.180755e-02\n",
       "max    8.924206e+17     4.000000     1.000000  4.880140e-01  2.734190e-01"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_pred.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 12)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_pred.loc[image_pred.p1_conf == 1, :].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pred = image_pred.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2075"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "543"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_pred.loc[(df_pred.p1_dog == False), :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "388"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_pred.loc[(df_pred.p1_dog == False) & (df_pred.p2_dog == False), :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "324"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 324 data points are definitely not dogs\n",
    "len(df_pred.loc[(df_pred.p1_dog == False) & (df_pred.p2_dog == False) & (df_pred.p3_dog == False), :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_pred.loc[(df_pred.p1_dog == False) & (df_pred.p2_dog == True), :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1398"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "len(df_pred.loc[(df_pred.p1_dog == True) & (df_pred.p2_dog == True), :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1532"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_pred.loc[(df_pred.p1_dog == True), :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicates in the table\n",
    "image_pred.duplicated(keep=False, subset='tweet_id').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`image_pred` table\n",
    "- 324 data points are definitely not dogs (all p's=False)\n",
    "- one row has a 1 confidence\n",
    "- dog breed inconsistent (some capital and some small case)\n",
    "- dog breeds sepearated by underscore\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Master Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Master` Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quality issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- drop all irrelevant columns from master\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tidiness issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 3 tables to be joined into 1 master table\n",
    "- master table should have a `breed` column\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tw table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw = twitter_arch.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "tw.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "change 745 dog names from \"None\" to NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw.name.replace(\"None\", np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                         0\n",
       "in_reply_to_status_id         2278\n",
       "in_reply_to_user_id           2278\n",
       "timestamp                        0\n",
       "source                           0\n",
       "text                             0\n",
       "retweeted_status_id           2175\n",
       "retweeted_status_user_id      2175\n",
       "retweeted_status_timestamp    2175\n",
       "expanded_urls                   59\n",
       "rating_numerator                 0\n",
       "rating_denominator               0\n",
       "name                           745\n",
       "doggo                            0\n",
       "floofer                          0\n",
       "pupper                           0\n",
       "puppo                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.isna().sum(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tw[tw.name == \"None\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change all \"None\" values in `doggo`, `floofer`, `pupper` and `puppo` columns to NaNs\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw.doggo.replace(\"None\", np.nan, inplace=True)\n",
    "tw.floofer.replace(\"None\", np.nan, inplace=True)\n",
    "tw.pupper.replace(\"None\", np.nan, inplace=True)\n",
    "tw.puppo.replace(\"None\", np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(len(tw[tw.doggo == \"None\"]))\n",
    "print(len(tw[tw.floofer == \"None\"]))\n",
    "print(len(tw[tw.pupper == \"None\"]))\n",
    "print(len(tw[tw.puppo == \"None\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        1611 non-null   object \n",
      " 13  doggo                       97 non-null     object \n",
      " 14  floofer                     10 non-null     object \n",
      " 15  pupper                      257 non-null    object \n",
      " 16  puppo                       30 non-null     object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "tw.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove 181 retweets to tweets in this data set as they are not needed. We're only interested in tweets\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop all rows that are retweets (181 rows)\n",
    "condition = (tw.retweeted_status_id.notna())\n",
    "tw.drop(tw[condition].index, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2175 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2175 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                   2175 non-null   object \n",
      " 4   source                      2175 non-null   object \n",
      " 5   text                        2175 non-null   object \n",
      " 6   retweeted_status_id         0 non-null      float64\n",
      " 7   retweeted_status_user_id    0 non-null      float64\n",
      " 8   retweeted_status_timestamp  0 non-null      object \n",
      " 9   expanded_urls               2117 non-null   object \n",
      " 10  rating_numerator            2175 non-null   int64  \n",
      " 11  rating_denominator          2175 non-null   int64  \n",
      " 12  name                        1495 non-null   object \n",
      " 13  doggo                       87 non-null     object \n",
      " 14  floofer                     10 non-null     object \n",
      " 15  pupper                      234 non-null    object \n",
      " 16  puppo                       25 non-null     object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 305.9+ KB\n"
     ]
    }
   ],
   "source": [
    "tw.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove 78 replies to tweets in this data set. We're only interested in tweets\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop all rows that are replies (78 rows)\n",
    "condition = (tw.in_reply_to_status_id.notna())\n",
    "tw.drop(tw[condition].index, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2097 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2097 non-null   int64  \n",
      " 1   in_reply_to_status_id       0 non-null      float64\n",
      " 2   in_reply_to_user_id         0 non-null      float64\n",
      " 3   timestamp                   2097 non-null   object \n",
      " 4   source                      2097 non-null   object \n",
      " 5   text                        2097 non-null   object \n",
      " 6   retweeted_status_id         0 non-null      float64\n",
      " 7   retweeted_status_user_id    0 non-null      float64\n",
      " 8   retweeted_status_timestamp  0 non-null      object \n",
      " 9   expanded_urls               2094 non-null   object \n",
      " 10  rating_numerator            2097 non-null   int64  \n",
      " 11  rating_denominator          2097 non-null   int64  \n",
      " 12  name                        1494 non-null   object \n",
      " 13  doggo                       83 non-null     object \n",
      " 14  floofer                     10 non-null     object \n",
      " 15  pupper                      230 non-null    object \n",
      " 16  puppo                       24 non-null     object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 294.9+ KB\n"
     ]
    }
   ],
   "source": [
    "tw.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change wrong datatype for `timestamp` column to datetime datatype\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw.timestamp = pd.to_datetime(tw.timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime64[ns, UTC]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.timestamp.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop `in_reply_to_status_id`, `in_reply_to_user_id`, `retweeted_status_id`, `retweeted_status_user_id` and\n",
    "`retweeted_status_timestamp` columns since they are not relevant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',\n",
       "       'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',\n",
       "       'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'doggo', 'floofer', 'pupper', 'puppo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw.drop(['in_reply_to_status_id', 'in_reply_to_user_id','retweeted_status_id', 'retweeted_status_user_id',\n",
    "       'retweeted_status_timestamp'],axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',\n",
       "       'rating_numerator', 'rating_denominator', 'name', 'doggo', 'floofer',\n",
       "       'pupper', 'puppo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>890240255349198849</td>\n",
       "      <td>2017-07-26 15:59:51+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Cassie. She is a college pup. Studying...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/890240255...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>Cassie</td>\n",
       "      <td>doggo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>884162670584377345</td>\n",
       "      <td>2017-07-09 21:29:42+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Yogi. He doesn't have any important dog m...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/884162670...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Yogi</td>\n",
       "      <td>doggo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>872967104147763200</td>\n",
       "      <td>2017-06-09 00:02:31+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here's a very large dog. He has a date later. ...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/872967104...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>doggo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>871515927908634625</td>\n",
       "      <td>2017-06-04 23:56:03+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Napolean. He's a Raggedy East Nicaragu...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/871515927...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Napolean</td>\n",
       "      <td>doggo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>871102520638267392</td>\n",
       "      <td>2017-06-03 20:33:19+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Never doubt a doggo 14/10 https://t.co/AbBLh2FZCH</td>\n",
       "      <td>https://twitter.com/animalcog/status/871075758...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>doggo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>732375214819057664</td>\n",
       "      <td>2016-05-17 01:00:32+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Kyle (pronounced 'Mitch'). He strives ...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/732375214...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Kyle</td>\n",
       "      <td>doggo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1141</th>\n",
       "      <td>727644517743104000</td>\n",
       "      <td>2016-05-03 23:42:26+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here's a doggo struggling to cope with the win...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/727644517...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>doggo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1156</th>\n",
       "      <td>724771698126512129</td>\n",
       "      <td>2016-04-26 01:26:53+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Nothin better than a doggo and a sunset. 11/10...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/724771698...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>doggo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1176</th>\n",
       "      <td>719991154352222208</td>\n",
       "      <td>2016-04-12 20:50:42+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This doggo was initially thrilled when she saw...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/719991154...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>doggo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>716080869887381504</td>\n",
       "      <td>2016-04-02 01:52:38+00:00</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here's a super majestic doggo and a sunset 11/...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/716080869...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>doggo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                 timestamp  \\\n",
       "9     890240255349198849 2017-07-26 15:59:51+00:00   \n",
       "43    884162670584377345 2017-07-09 21:29:42+00:00   \n",
       "99    872967104147763200 2017-06-09 00:02:31+00:00   \n",
       "108   871515927908634625 2017-06-04 23:56:03+00:00   \n",
       "110   871102520638267392 2017-06-03 20:33:19+00:00   \n",
       "...                  ...                       ...   \n",
       "1117  732375214819057664 2016-05-17 01:00:32+00:00   \n",
       "1141  727644517743104000 2016-05-03 23:42:26+00:00   \n",
       "1156  724771698126512129 2016-04-26 01:26:53+00:00   \n",
       "1176  719991154352222208 2016-04-12 20:50:42+00:00   \n",
       "1204  716080869887381504 2016-04-02 01:52:38+00:00   \n",
       "\n",
       "                                                 source  \\\n",
       "9     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "43    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "99    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "108   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "110   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "1117  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1141  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1156  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1176  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1204  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  \\\n",
       "9     This is Cassie. She is a college pup. Studying...   \n",
       "43    Meet Yogi. He doesn't have any important dog m...   \n",
       "99    Here's a very large dog. He has a date later. ...   \n",
       "108   This is Napolean. He's a Raggedy East Nicaragu...   \n",
       "110   Never doubt a doggo 14/10 https://t.co/AbBLh2FZCH   \n",
       "...                                                 ...   \n",
       "1117  This is Kyle (pronounced 'Mitch'). He strives ...   \n",
       "1141  Here's a doggo struggling to cope with the win...   \n",
       "1156  Nothin better than a doggo and a sunset. 11/10...   \n",
       "1176  This doggo was initially thrilled when she saw...   \n",
       "1204  Here's a super majestic doggo and a sunset 11/...   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "9     https://twitter.com/dog_rates/status/890240255...                14   \n",
       "43    https://twitter.com/dog_rates/status/884162670...                12   \n",
       "99    https://twitter.com/dog_rates/status/872967104...                12   \n",
       "108   https://twitter.com/dog_rates/status/871515927...                12   \n",
       "110   https://twitter.com/animalcog/status/871075758...                14   \n",
       "...                                                 ...               ...   \n",
       "1117  https://twitter.com/dog_rates/status/732375214...                11   \n",
       "1141  https://twitter.com/dog_rates/status/727644517...                13   \n",
       "1156  https://twitter.com/dog_rates/status/724771698...                11   \n",
       "1176  https://twitter.com/dog_rates/status/719991154...                10   \n",
       "1204  https://twitter.com/dog_rates/status/716080869...                11   \n",
       "\n",
       "      rating_denominator      name  doggo floofer pupper puppo  \n",
       "9                     10    Cassie  doggo     NaN    NaN   NaN  \n",
       "43                    10      Yogi  doggo     NaN    NaN   NaN  \n",
       "99                    10       NaN  doggo     NaN    NaN   NaN  \n",
       "108                   10  Napolean  doggo     NaN    NaN   NaN  \n",
       "110                   10       NaN  doggo     NaN    NaN   NaN  \n",
       "...                  ...       ...    ...     ...    ...   ...  \n",
       "1117                  10      Kyle  doggo     NaN    NaN   NaN  \n",
       "1141                  10       NaN  doggo     NaN    NaN   NaN  \n",
       "1156                  10       NaN  doggo     NaN    NaN   NaN  \n",
       "1176                  10       NaN  doggo     NaN    NaN   NaN  \n",
       "1204                  10       NaN  doggo     NaN    NaN   NaN  \n",
       "\n",
       "[83 rows x 12 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.loc[tw.doggo.notna(), : ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                                             890240255349198849\n",
       "timestamp                                     2017-07-26 15:59:51+00:00\n",
       "source                <a href=\"http://twitter.com/download/iphone\" r...\n",
       "text                  This is Cassie. She is a college pup. Studying...\n",
       "expanded_urls         https://twitter.com/dog_rates/status/890240255...\n",
       "rating_numerator                                                     14\n",
       "rating_denominator                                                   10\n",
       "name                                                             Cassie\n",
       "doggo                                                             doggo\n",
       "floofer                                                             NaN\n",
       "pupper                                                              NaN\n",
       "puppo                                                               NaN\n",
       "Name: 9, dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.iloc[9, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',\n",
       "       'rating_numerator', 'rating_denominator', 'name', 'doggo', 'floofer',\n",
       "       'pupper', 'puppo'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw.doggo.replace(np.nan, '', inplace=True)\n",
    "tw.floofer.replace(np.nan, '', inplace=True)\n",
    "tw.pupper.replace(np.nan, '', inplace=True)\n",
    "tw.puppo.replace(np.nan, '', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw['dog_stage'] = tw.doggo + tw.floofer + tw.pupper + tw.puppo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',\n",
       "       'rating_numerator', 'rating_denominator', 'name', 'doggo', 'floofer',\n",
       "       'pupper', 'puppo', 'dog_stage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2097 entries, 0 to 2355\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2097 non-null   int64              \n",
      " 1   timestamp           2097 non-null   datetime64[ns, UTC]\n",
      " 2   source              2097 non-null   object             \n",
      " 3   text                2097 non-null   object             \n",
      " 4   expanded_urls       2094 non-null   object             \n",
      " 5   rating_numerator    2097 non-null   int64              \n",
      " 6   rating_denominator  2097 non-null   int64              \n",
      " 7   name                1494 non-null   object             \n",
      " 8   doggo               2097 non-null   object             \n",
      " 9   floofer             2097 non-null   object             \n",
      " 10  pupper              2097 non-null   object             \n",
      " 11  puppo               2097 non-null   object             \n",
      " 12  dog_stage           2097 non-null   object             \n",
      "dtypes: datetime64[ns, UTC](1), int64(3), object(9)\n",
      "memory usage: 229.4+ KB\n"
     ]
    }
   ],
   "source": [
    "tw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        \n",
       "1        \n",
       "2        \n",
       "3        \n",
       "4        \n",
       "       ..\n",
       "2351     \n",
       "2352     \n",
       "2353     \n",
       "2354     \n",
       "2355     \n",
       "Name: dog_stage, Length: 2097, dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.dog_stage.str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                1761\n",
       "pupper           221\n",
       "doggo             72\n",
       "puppo             23\n",
       "doggopupper        9\n",
       "floofer            9\n",
       "doggofloofer       1\n",
       "doggopuppo         1\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the stages of entries with more than 1 dog stage. For example doggopupper is a dog that is in the middle stage of a doggo and a pupper so their stage should be \"doggo,pupper\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw.loc[tw.dog_stage == \"doggopupper\", 'dog_stage'] = \"doggo, pupper\"\n",
    "tw.loc[tw.dog_stage == \"doggofloofer\", 'dog_stage'] = \"doggo, floofer\"\n",
    "tw.loc[tw.dog_stage == \"doggopuppo\", 'dog_stage'] = \"doggo, puppo\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                  1761\n",
       "pupper             221\n",
       "doggo               72\n",
       "puppo               23\n",
       "doggo, pupper        9\n",
       "floofer              9\n",
       "doggo, floofer       1\n",
       "doggo, puppo         1\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace all '' with NaNs in `dog_stage` column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1761"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tw.loc[tw.dog_stage == '', 'dog_stage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw.loc[tw.dog_stage == '', 'dog_stage'] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',\n",
       "       'rating_numerator', 'rating_denominator', 'name', 'doggo', 'floofer',\n",
       "       'pupper', 'puppo', 'dog_stage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop columns `doggo`, `floofer`, `pupper`, `puppo` since they are not needed anymore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw.drop(columns=['doggo', 'floofer', 'pupper', 'puppo'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',\n",
       "       'rating_numerator', 'rating_denominator', 'name', 'dog_stage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2097 entries, 0 to 2355\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2097 non-null   int64              \n",
      " 1   timestamp           2097 non-null   datetime64[ns, UTC]\n",
      " 2   source              2097 non-null   object             \n",
      " 3   text                2097 non-null   object             \n",
      " 4   expanded_urls       2094 non-null   object             \n",
      " 5   rating_numerator    2097 non-null   int64              \n",
      " 6   rating_denominator  2097 non-null   int64              \n",
      " 7   name                1494 non-null   object             \n",
      " 8   dog_stage           336 non-null    object             \n",
      "dtypes: datetime64[ns, UTC](1), int64(3), object(5)\n",
      "memory usage: 163.8+ KB\n"
     ]
    }
   ],
   "source": [
    "tw.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change innacurated/wrong dog names to NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Abby',\n",
       " 'Ace',\n",
       " 'Acro',\n",
       " 'Adele',\n",
       " 'Aiden',\n",
       " 'Aja',\n",
       " 'Akumi',\n",
       " 'Al',\n",
       " 'Albert',\n",
       " 'Albus',\n",
       " 'Aldrick',\n",
       " 'Alejandro',\n",
       " 'Alexander',\n",
       " 'Alexanderson',\n",
       " 'Alf',\n",
       " 'Alfie',\n",
       " 'Alfy',\n",
       " 'Alice',\n",
       " 'Amber',\n",
       " 'Ambrose',\n",
       " 'Amy',\n",
       " 'Amélie',\n",
       " 'Anakin',\n",
       " 'Andru',\n",
       " 'Andy',\n",
       " 'Angel',\n",
       " 'Anna',\n",
       " 'Anthony',\n",
       " 'Antony',\n",
       " 'Apollo',\n",
       " 'Aqua',\n",
       " 'Archie',\n",
       " 'Arlen',\n",
       " 'Arlo',\n",
       " 'Arnie',\n",
       " 'Arnold',\n",
       " 'Arya',\n",
       " 'Ash',\n",
       " 'Asher',\n",
       " 'Ashleigh',\n",
       " 'Aspen',\n",
       " 'Astrid',\n",
       " 'Atlas',\n",
       " 'Atticus',\n",
       " 'Aubie',\n",
       " 'Augie',\n",
       " 'Autumn',\n",
       " 'Ava',\n",
       " 'Axel',\n",
       " 'Bailey',\n",
       " 'Baloo',\n",
       " 'Balto',\n",
       " 'Banditt',\n",
       " 'Banjo',\n",
       " 'Barclay',\n",
       " 'Barney',\n",
       " 'Baron',\n",
       " 'Barry',\n",
       " 'Batdog',\n",
       " 'Bauer',\n",
       " 'Baxter',\n",
       " 'Bayley',\n",
       " 'BeBe',\n",
       " 'Bear',\n",
       " 'Beau',\n",
       " 'Beckham',\n",
       " 'Beebop',\n",
       " 'Beemo',\n",
       " 'Bell',\n",
       " 'Bella',\n",
       " 'Belle',\n",
       " 'Ben',\n",
       " 'Benedict',\n",
       " 'Benji',\n",
       " 'Benny',\n",
       " 'Bentley',\n",
       " 'Berb',\n",
       " 'Berkeley',\n",
       " 'Bernie',\n",
       " 'Bert',\n",
       " 'Bertson',\n",
       " 'Betty',\n",
       " 'Beya',\n",
       " 'Biden',\n",
       " 'Bilbo',\n",
       " 'Billl',\n",
       " 'Billy',\n",
       " 'Binky',\n",
       " 'Birf',\n",
       " 'Bisquick',\n",
       " 'Blakely',\n",
       " 'Blanket',\n",
       " 'Blipson',\n",
       " 'Blitz',\n",
       " 'Bloo',\n",
       " 'Bloop',\n",
       " 'Blu',\n",
       " 'Blue',\n",
       " 'Bluebert',\n",
       " 'Bo',\n",
       " 'Bob',\n",
       " 'Bobb',\n",
       " 'Bobbay',\n",
       " 'Bobble',\n",
       " 'Bobby',\n",
       " 'Bode',\n",
       " 'Bodie',\n",
       " 'Bonaparte',\n",
       " 'Bones',\n",
       " 'Bookstore',\n",
       " 'Boomer',\n",
       " 'Boots',\n",
       " 'Boston',\n",
       " 'Bowie',\n",
       " 'Brad',\n",
       " 'Bradlay',\n",
       " 'Bradley',\n",
       " 'Brady',\n",
       " 'Brandi',\n",
       " 'Brandonald',\n",
       " 'Brandy',\n",
       " 'Brat',\n",
       " 'Brian',\n",
       " 'Brockly',\n",
       " 'Brody',\n",
       " 'Bronte',\n",
       " 'Brooks',\n",
       " 'Brownie',\n",
       " 'Bruce',\n",
       " 'Brudge',\n",
       " 'Bruiser',\n",
       " 'Bruno',\n",
       " 'Brutus',\n",
       " 'Bubba',\n",
       " 'Bubbles',\n",
       " 'Buckley',\n",
       " 'Buddah',\n",
       " 'Buddy',\n",
       " 'Bungalo',\n",
       " 'Burt',\n",
       " 'Butter',\n",
       " 'Butters',\n",
       " 'Cal',\n",
       " 'Calbert',\n",
       " 'Cali',\n",
       " 'Callie',\n",
       " 'Calvin',\n",
       " 'Canela',\n",
       " 'Cannon',\n",
       " 'Carbon',\n",
       " 'Carl',\n",
       " 'Carll',\n",
       " 'Carly',\n",
       " 'Carper',\n",
       " 'Carter',\n",
       " 'Caryl',\n",
       " 'Cash',\n",
       " 'Cassie',\n",
       " 'CeCe',\n",
       " 'Cecil',\n",
       " 'Cedrick',\n",
       " 'Cermet',\n",
       " 'Chadrick',\n",
       " 'Champ',\n",
       " 'Charl',\n",
       " 'Charles',\n",
       " 'Charleson',\n",
       " 'Charlie',\n",
       " 'Chase',\n",
       " 'Chaz',\n",
       " 'Cheesy',\n",
       " 'Chef',\n",
       " 'Chelsea',\n",
       " 'Cheryl',\n",
       " 'Chesney',\n",
       " 'Chester',\n",
       " 'Chesterson',\n",
       " 'Chet',\n",
       " 'Chevy',\n",
       " 'Chip',\n",
       " 'Chipson',\n",
       " 'Chloe',\n",
       " 'Chompsky',\n",
       " 'Christoper',\n",
       " 'Chubbs',\n",
       " 'Chuck',\n",
       " 'Chuckles',\n",
       " 'Chuq',\n",
       " 'Churlie',\n",
       " 'Cilantro',\n",
       " 'Clarence',\n",
       " 'Clark',\n",
       " 'Clarkus',\n",
       " 'Clarq',\n",
       " 'Claude',\n",
       " 'Cleopatricia',\n",
       " 'Clifford',\n",
       " 'Clybe',\n",
       " 'Clyde',\n",
       " 'Coco',\n",
       " 'Cody',\n",
       " 'Colby',\n",
       " 'Coleman',\n",
       " 'Colin',\n",
       " 'Combo',\n",
       " 'Comet',\n",
       " 'Cooper',\n",
       " 'Coops',\n",
       " 'Coopson',\n",
       " 'Cora',\n",
       " 'Corey',\n",
       " 'Covach',\n",
       " 'Craig',\n",
       " 'Crawford',\n",
       " 'Creg',\n",
       " 'Crimson',\n",
       " 'Crouton',\n",
       " 'Crumpet',\n",
       " 'Crystal',\n",
       " 'Cuddles',\n",
       " 'Cupcake',\n",
       " 'Cupid',\n",
       " 'Curtis',\n",
       " 'Daisy',\n",
       " 'Dakota',\n",
       " 'Dale',\n",
       " 'Dallas',\n",
       " 'Damon',\n",
       " 'Daniel',\n",
       " 'Danny',\n",
       " 'Dante',\n",
       " 'Darby',\n",
       " 'Darla',\n",
       " 'Darrel',\n",
       " 'Dash',\n",
       " 'Dave',\n",
       " 'Davey',\n",
       " 'Dawn',\n",
       " 'DayZ',\n",
       " 'Deacon',\n",
       " 'Derby',\n",
       " 'Derek',\n",
       " 'Devón',\n",
       " 'Dewey',\n",
       " 'Dex',\n",
       " 'Dexter',\n",
       " 'Dido',\n",
       " 'Dietrich',\n",
       " 'Diogi',\n",
       " 'Divine',\n",
       " 'Dixie',\n",
       " 'Django',\n",
       " 'Dobby',\n",
       " 'Doc',\n",
       " 'DonDon',\n",
       " 'Donny',\n",
       " 'Doobert',\n",
       " 'Dook',\n",
       " 'Dot',\n",
       " 'Dotsy',\n",
       " 'Doug',\n",
       " 'Duchess',\n",
       " 'Duddles',\n",
       " 'Dudley',\n",
       " 'Dug',\n",
       " 'Duke',\n",
       " 'Dunkin',\n",
       " 'Durg',\n",
       " 'Dutch',\n",
       " 'Dwight',\n",
       " 'Dylan',\n",
       " 'Earl',\n",
       " 'Eazy',\n",
       " 'Ebby',\n",
       " 'Ed',\n",
       " 'Edd',\n",
       " 'Edgar',\n",
       " 'Edmund',\n",
       " 'Eevee',\n",
       " 'Einstein',\n",
       " 'Eleanor',\n",
       " 'Eli',\n",
       " 'Ellie',\n",
       " 'Elliot',\n",
       " 'Emanuel',\n",
       " 'Ember',\n",
       " 'Emma',\n",
       " 'Emmie',\n",
       " 'Emmy',\n",
       " 'Enchilada',\n",
       " 'Erik',\n",
       " 'Eriq',\n",
       " 'Ester',\n",
       " 'Eugene',\n",
       " 'Eve',\n",
       " 'Evy',\n",
       " 'Fabio',\n",
       " 'Farfle',\n",
       " 'Ferg',\n",
       " 'Fido',\n",
       " 'Fiji',\n",
       " 'Fillup',\n",
       " 'Filup',\n",
       " 'Finley',\n",
       " 'Finn',\n",
       " 'Finnegus',\n",
       " 'Fiona',\n",
       " 'Fizz',\n",
       " 'Flash',\n",
       " 'Fletcher',\n",
       " 'Florence',\n",
       " 'Flurpson',\n",
       " 'Flávio',\n",
       " 'Frank',\n",
       " 'Frankie',\n",
       " 'Franklin',\n",
       " 'Franq',\n",
       " 'Fred',\n",
       " 'Freddery',\n",
       " 'Frönq',\n",
       " 'Furzey',\n",
       " 'Fwed',\n",
       " 'Fynn',\n",
       " 'Gabe',\n",
       " 'Gary',\n",
       " 'General',\n",
       " 'Genevieve',\n",
       " 'Geno',\n",
       " 'Geoff',\n",
       " 'George',\n",
       " 'Georgie',\n",
       " 'Gerald',\n",
       " 'Gerbald',\n",
       " 'Gert',\n",
       " 'Gidget',\n",
       " 'Gilbert',\n",
       " 'Gin',\n",
       " 'Ginger',\n",
       " 'Gizmo',\n",
       " 'Glacier',\n",
       " 'Glenn',\n",
       " 'Godi',\n",
       " 'Godzilla',\n",
       " 'Goliath',\n",
       " 'Goose',\n",
       " 'Gordon',\n",
       " 'Grady',\n",
       " 'Grey',\n",
       " 'Griffin',\n",
       " 'Griswold',\n",
       " 'Grizz',\n",
       " 'Grizzie',\n",
       " 'Grizzwald',\n",
       " 'Gromit',\n",
       " 'Gunner',\n",
       " 'Gus',\n",
       " 'Gustaf',\n",
       " 'Gustav',\n",
       " 'Gòrdón',\n",
       " 'Hall',\n",
       " 'Halo',\n",
       " 'Hammond',\n",
       " 'Hamrick',\n",
       " 'Hank',\n",
       " 'Hanz',\n",
       " 'Happy',\n",
       " 'Harlso',\n",
       " 'Harnold',\n",
       " 'Harold',\n",
       " 'Harper',\n",
       " 'Harrison',\n",
       " 'Harry',\n",
       " 'Harvey',\n",
       " 'Hazel',\n",
       " 'Hector',\n",
       " 'Heinrich',\n",
       " 'Henry',\n",
       " 'Herald',\n",
       " 'Herb',\n",
       " 'Hercules',\n",
       " 'Herm',\n",
       " 'Hermione',\n",
       " 'Hero',\n",
       " 'Herschel',\n",
       " 'Hobbes',\n",
       " 'Holly',\n",
       " 'Horace',\n",
       " 'Howie',\n",
       " 'Hubertson',\n",
       " 'Huck',\n",
       " 'Humphrey',\n",
       " 'Hunter',\n",
       " 'Hurley',\n",
       " 'Huxley',\n",
       " 'Iggy',\n",
       " 'Ike',\n",
       " 'Indie',\n",
       " 'Iroh',\n",
       " 'Ito',\n",
       " 'Ivar',\n",
       " 'Izzy',\n",
       " 'JD',\n",
       " 'Jack',\n",
       " 'Jackie',\n",
       " 'Jackson',\n",
       " 'Jameson',\n",
       " 'Jamesy',\n",
       " 'Jangle',\n",
       " 'Jareld',\n",
       " 'Jarod',\n",
       " 'Jarvis',\n",
       " 'Jaspers',\n",
       " 'Jax',\n",
       " 'Jay',\n",
       " 'Jaycob',\n",
       " 'Jazz',\n",
       " 'Jazzy',\n",
       " 'Jeb',\n",
       " 'Jebberson',\n",
       " 'Jed',\n",
       " 'Jeffrey',\n",
       " 'Jeffri',\n",
       " 'Jeffrie',\n",
       " 'Jennifur',\n",
       " 'Jeph',\n",
       " 'Jeremy',\n",
       " 'Jerome',\n",
       " 'Jerry',\n",
       " 'Jersey',\n",
       " 'Jesse',\n",
       " 'Jessifer',\n",
       " 'Jessiga',\n",
       " 'Jett',\n",
       " 'Jim',\n",
       " 'Jimbo',\n",
       " 'Jiminus',\n",
       " 'Jiminy',\n",
       " 'Jimison',\n",
       " 'Jimothy',\n",
       " 'Jo',\n",
       " 'Jockson',\n",
       " 'Joey',\n",
       " 'Jomathan',\n",
       " 'Jonah',\n",
       " 'Jordy',\n",
       " 'Josep',\n",
       " 'Joshwa',\n",
       " 'Juckson',\n",
       " 'Julio',\n",
       " 'Julius',\n",
       " 'Juno',\n",
       " 'Kaia',\n",
       " 'Kaiya',\n",
       " 'Kallie',\n",
       " 'Kane',\n",
       " 'Kanu',\n",
       " 'Kara',\n",
       " 'Karl',\n",
       " 'Karll',\n",
       " 'Karma',\n",
       " 'Kathmandu',\n",
       " 'Katie',\n",
       " 'Kawhi',\n",
       " 'Kayla',\n",
       " 'Keet',\n",
       " 'Keith',\n",
       " 'Kellogg',\n",
       " 'Ken',\n",
       " 'Kendall',\n",
       " 'Kenneth',\n",
       " 'Kenny',\n",
       " 'Kenzie',\n",
       " 'Keurig',\n",
       " 'Kevin',\n",
       " 'Kevon',\n",
       " 'Kial',\n",
       " 'Kilo',\n",
       " 'Kingsley',\n",
       " 'Kirby',\n",
       " 'Kirk',\n",
       " 'Klein',\n",
       " 'Klevin',\n",
       " 'Kloey',\n",
       " 'Kobe',\n",
       " 'Koda',\n",
       " 'Kody',\n",
       " 'Koko',\n",
       " 'Kollin',\n",
       " 'Kona',\n",
       " 'Kota',\n",
       " 'Kramer',\n",
       " 'Kreg',\n",
       " 'Kreggory',\n",
       " 'Kulet',\n",
       " 'Kuyu',\n",
       " 'Kyle',\n",
       " 'Kyro',\n",
       " 'Lacy',\n",
       " 'Laela',\n",
       " 'Laika',\n",
       " 'Lambeau',\n",
       " 'Lance',\n",
       " 'Larry',\n",
       " 'Lassie',\n",
       " 'Layla',\n",
       " 'Leela',\n",
       " 'Lennon',\n",
       " 'Lenny',\n",
       " 'Lenox',\n",
       " 'Leo',\n",
       " 'Leonard',\n",
       " 'Leonidas',\n",
       " 'Levi',\n",
       " 'Liam',\n",
       " 'Lilah',\n",
       " 'Lili',\n",
       " 'Lilli',\n",
       " 'Lillie',\n",
       " 'Lilly',\n",
       " 'Lily',\n",
       " 'Lincoln',\n",
       " 'Linda',\n",
       " 'Link',\n",
       " 'Linus',\n",
       " 'Lipton',\n",
       " 'Livvie',\n",
       " 'Lizzie',\n",
       " 'Logan',\n",
       " 'Loki',\n",
       " 'Lola',\n",
       " 'Lolo',\n",
       " 'Longfellow',\n",
       " 'Loomis',\n",
       " 'Lorelei',\n",
       " 'Lorenzo',\n",
       " 'Lou',\n",
       " 'Louie',\n",
       " 'Louis',\n",
       " 'Luca',\n",
       " 'Lucia',\n",
       " 'Lucky',\n",
       " 'Lucy',\n",
       " 'Lugan',\n",
       " 'Lulu',\n",
       " 'Luna',\n",
       " 'Lupe',\n",
       " 'Luther',\n",
       " 'Mabel',\n",
       " 'Mac',\n",
       " 'Mack',\n",
       " 'Maddie',\n",
       " 'Maggie',\n",
       " 'Mairi',\n",
       " 'Maisey',\n",
       " 'Major',\n",
       " 'Maks',\n",
       " 'Malcolm',\n",
       " 'Malikai',\n",
       " 'Margo',\n",
       " 'Mark',\n",
       " 'Marlee',\n",
       " 'Marley',\n",
       " 'Marq',\n",
       " 'Marty',\n",
       " 'Marvin',\n",
       " 'Mary',\n",
       " 'Mason',\n",
       " 'Mattie',\n",
       " 'Maude',\n",
       " 'Mauve',\n",
       " 'Max',\n",
       " 'Maxaroni',\n",
       " 'Maximus',\n",
       " 'Maxwell',\n",
       " 'Maya',\n",
       " 'Meatball',\n",
       " 'Meera',\n",
       " 'Meyer',\n",
       " 'Mia',\n",
       " 'Michelangelope',\n",
       " 'Miguel',\n",
       " 'Mike',\n",
       " 'Miley',\n",
       " 'Milky',\n",
       " 'Millie',\n",
       " 'Milo',\n",
       " 'Mimosa',\n",
       " 'Mingus',\n",
       " 'Mister',\n",
       " 'Misty',\n",
       " 'Mitch',\n",
       " 'Mo',\n",
       " 'Moe',\n",
       " 'Mojo',\n",
       " 'Mollie',\n",
       " 'Molly',\n",
       " 'Mona',\n",
       " 'Monkey',\n",
       " 'Monster',\n",
       " 'Monty',\n",
       " 'Moofasa',\n",
       " 'Mookie',\n",
       " 'Moose',\n",
       " 'Moreton',\n",
       " 'Mosby',\n",
       " 'Murphy',\n",
       " 'Mutt',\n",
       " 'Mya',\n",
       " 'Nala',\n",
       " 'Naphaniel',\n",
       " 'Napolean',\n",
       " 'Nelly',\n",
       " 'Neptune',\n",
       " 'Newt',\n",
       " 'Nico',\n",
       " 'Nida',\n",
       " 'Nigel',\n",
       " 'Nimbus',\n",
       " 'Noah',\n",
       " 'Nollie',\n",
       " 'Noosh',\n",
       " 'Norman',\n",
       " 'Nugget',\n",
       " 'O',\n",
       " 'Oakley',\n",
       " 'Obi',\n",
       " 'Obie',\n",
       " 'Oddie',\n",
       " 'Odie',\n",
       " 'Odin',\n",
       " 'Olaf',\n",
       " 'Ole',\n",
       " 'Olive',\n",
       " 'Oliver',\n",
       " 'Olivia',\n",
       " 'Oliviér',\n",
       " 'Ollie',\n",
       " 'Opal',\n",
       " 'Opie',\n",
       " 'Oreo',\n",
       " 'Orion',\n",
       " 'Oscar',\n",
       " 'Oshie',\n",
       " 'Otis',\n",
       " 'Ozzie',\n",
       " 'Ozzy',\n",
       " 'Pablo',\n",
       " 'Paisley',\n",
       " 'Pancake',\n",
       " 'Panda',\n",
       " 'Patch',\n",
       " 'Patrick',\n",
       " 'Paull',\n",
       " 'Pavlov',\n",
       " 'Pawnd',\n",
       " 'Peaches',\n",
       " 'Peanut',\n",
       " 'Penelope',\n",
       " 'Penny',\n",
       " 'Pepper',\n",
       " 'Percy',\n",
       " 'Perry',\n",
       " 'Pete',\n",
       " 'Petrick',\n",
       " 'Pherb',\n",
       " 'Phil',\n",
       " 'Philbert',\n",
       " 'Philippe',\n",
       " 'Phineas',\n",
       " 'Phred',\n",
       " 'Pickles',\n",
       " 'Pilot',\n",
       " 'Pinot',\n",
       " 'Pip',\n",
       " 'Piper',\n",
       " 'Pippa',\n",
       " 'Pippin',\n",
       " 'Pipsy',\n",
       " 'Pluto',\n",
       " 'Poppy',\n",
       " 'Pubert',\n",
       " 'Puff',\n",
       " 'Pumpkin',\n",
       " 'Pupcasso',\n",
       " 'Quinn',\n",
       " 'Ralf',\n",
       " 'Ralph',\n",
       " 'Ralpher',\n",
       " 'Ralphie',\n",
       " 'Ralphson',\n",
       " 'Ralphus',\n",
       " 'Ralphy',\n",
       " 'Ralphé',\n",
       " 'Rambo',\n",
       " 'Randall',\n",
       " 'Raphael',\n",
       " 'Rascal',\n",
       " 'Raymond',\n",
       " 'Reagan',\n",
       " 'Reese',\n",
       " 'Reggie',\n",
       " 'Reginald',\n",
       " 'Remington',\n",
       " 'Remus',\n",
       " 'Remy',\n",
       " 'Reptar',\n",
       " 'Rey',\n",
       " 'Rhino',\n",
       " 'Richie',\n",
       " 'Ricky',\n",
       " 'Ridley',\n",
       " 'Riley',\n",
       " 'Rilo',\n",
       " 'Rinna',\n",
       " 'River',\n",
       " 'Rizzo',\n",
       " 'Rizzy',\n",
       " 'Robin',\n",
       " 'Rocco',\n",
       " 'Rocky',\n",
       " 'Rodman',\n",
       " 'Rodney',\n",
       " 'Rolf',\n",
       " 'Romeo',\n",
       " 'Ron',\n",
       " 'Ronduh',\n",
       " 'Ronnie',\n",
       " 'Rontu',\n",
       " 'Rooney',\n",
       " 'Roosevelt',\n",
       " 'Rorie',\n",
       " 'Rory',\n",
       " 'Roscoe',\n",
       " 'Rose',\n",
       " 'Rosie',\n",
       " 'Rover',\n",
       " 'Rubio',\n",
       " 'Ruby',\n",
       " 'Rudy',\n",
       " 'Rueben',\n",
       " 'Ruffles',\n",
       " 'Rufio',\n",
       " 'Rufus',\n",
       " 'Rumble',\n",
       " 'Rumpole',\n",
       " 'Rupert',\n",
       " 'Rusty',\n",
       " 'Sadie',\n",
       " 'Sage',\n",
       " 'Sailer',\n",
       " 'Sailor',\n",
       " 'Sam',\n",
       " 'Sammy',\n",
       " 'Sampson',\n",
       " 'Samsom',\n",
       " 'Samson',\n",
       " 'Sandra',\n",
       " 'Sandy',\n",
       " 'Sansa',\n",
       " 'Sarge',\n",
       " 'Saydee',\n",
       " 'Schnitzel',\n",
       " 'Schnozz',\n",
       " 'Scooter',\n",
       " 'Scott',\n",
       " 'Scout',\n",
       " 'Scruffers',\n",
       " 'Seamus',\n",
       " 'Sebastian',\n",
       " 'Sephie',\n",
       " 'Severus',\n",
       " 'Shadoe',\n",
       " 'Shadow',\n",
       " 'Shaggy',\n",
       " 'Shakespeare',\n",
       " 'Shawwn',\n",
       " 'Shelby',\n",
       " 'Shikha',\n",
       " 'Shiloh',\n",
       " 'Shnuggles',\n",
       " 'Shooter',\n",
       " 'Siba',\n",
       " 'Sid',\n",
       " 'Sierra',\n",
       " 'Simba',\n",
       " 'Skittle',\n",
       " 'Skittles',\n",
       " 'Sky',\n",
       " 'Skye',\n",
       " 'Smiley',\n",
       " 'Smokey',\n",
       " 'Snickers',\n",
       " 'Snicku',\n",
       " 'Snoop',\n",
       " 'Snoopy',\n",
       " 'Sobe',\n",
       " 'Socks',\n",
       " 'Sojourner',\n",
       " 'Solomon',\n",
       " 'Sonny',\n",
       " 'Sophie',\n",
       " 'Sora',\n",
       " 'Spanky',\n",
       " 'Spark',\n",
       " 'Sparky',\n",
       " 'Spencer',\n",
       " 'Sprinkles',\n",
       " 'Sprout',\n",
       " 'Staniel',\n",
       " 'Stanley',\n",
       " 'Stark',\n",
       " 'Stefan',\n",
       " 'Stella',\n",
       " 'Stephan',\n",
       " 'Stephanus',\n",
       " 'Steve',\n",
       " 'Steven',\n",
       " 'Stewie',\n",
       " 'Storkson',\n",
       " 'Stormy',\n",
       " 'Strider',\n",
       " 'Striker',\n",
       " 'Strudel',\n",
       " 'Stu',\n",
       " 'Stuart',\n",
       " 'Stubert',\n",
       " 'Sugar',\n",
       " 'Suki',\n",
       " 'Sully',\n",
       " 'Sundance',\n",
       " 'Sunny',\n",
       " 'Sunshine',\n",
       " 'Superpup',\n",
       " 'Swagger',\n",
       " 'Sweet',\n",
       " 'Sweets',\n",
       " 'Taco',\n",
       " 'Tango',\n",
       " 'Tanner',\n",
       " 'Tassy',\n",
       " 'Tater',\n",
       " 'Tayzie',\n",
       " 'Taz',\n",
       " 'Tebow',\n",
       " 'Ted',\n",
       " 'Tedders',\n",
       " 'Teddy',\n",
       " 'Tedrick',\n",
       " 'Terrance',\n",
       " 'Terrenth',\n",
       " 'Terry',\n",
       " 'Tess',\n",
       " 'Theo',\n",
       " 'Theodore',\n",
       " 'Thor',\n",
       " 'Thumas',\n",
       " 'Tiger',\n",
       " 'Tilly',\n",
       " 'Timber',\n",
       " 'Timison',\n",
       " 'Timmy',\n",
       " 'Timofy',\n",
       " 'Tino',\n",
       " 'Titan',\n",
       " 'Tito',\n",
       " 'Tobi',\n",
       " 'Toby',\n",
       " 'Todo',\n",
       " 'Toffee',\n",
       " 'Tom',\n",
       " 'Tommy',\n",
       " 'Tonks',\n",
       " 'Torque',\n",
       " 'Tove',\n",
       " 'Travis',\n",
       " 'Traviss',\n",
       " 'Trevith',\n",
       " 'Trigger',\n",
       " 'Trip',\n",
       " 'Tripp',\n",
       " 'Trooper',\n",
       " 'Tuck',\n",
       " 'Tucker',\n",
       " 'Tuco',\n",
       " 'Tug',\n",
       " 'Tupawc',\n",
       " 'Tycho',\n",
       " 'Tyr',\n",
       " 'Tyrone',\n",
       " 'Tyrus',\n",
       " 'Ulysses',\n",
       " 'Venti',\n",
       " 'Vince',\n",
       " 'Vincent',\n",
       " 'Vinnie',\n",
       " 'Vinscent',\n",
       " 'Vixen',\n",
       " 'Wafer',\n",
       " 'Waffles',\n",
       " 'Walker',\n",
       " 'Wallace',\n",
       " 'Wally',\n",
       " 'Walter',\n",
       " 'Watson',\n",
       " 'Wesley',\n",
       " 'Wiggles',\n",
       " 'Willem',\n",
       " 'William',\n",
       " 'Willie',\n",
       " 'Willow',\n",
       " 'Willy',\n",
       " 'Wilson',\n",
       " 'Winifred',\n",
       " 'Winnie',\n",
       " 'Winston',\n",
       " 'Wishes',\n",
       " 'Wyatt',\n",
       " 'Yoda',\n",
       " 'Yogi',\n",
       " 'Yukon',\n",
       " 'Zara',\n",
       " 'Zeek',\n",
       " 'Zeke',\n",
       " 'Zeus',\n",
       " 'Ziva',\n",
       " 'Zoe',\n",
       " 'Zoey',\n",
       " 'Zooey',\n",
       " 'Zuzu',\n",
       " 'a',\n",
       " 'actually',\n",
       " 'all',\n",
       " 'an',\n",
       " 'by',\n",
       " 'getting',\n",
       " 'his',\n",
       " 'incredibly',\n",
       " 'infuriating',\n",
       " 'just',\n",
       " 'life',\n",
       " 'light',\n",
       " 'mad',\n",
       " 'my',\n",
       " 'not',\n",
       " 'officially',\n",
       " 'old',\n",
       " 'one',\n",
       " 'quite',\n",
       " 'space',\n",
       " 'such',\n",
       " 'the',\n",
       " 'this',\n",
       " 'unacceptable',\n",
       " 'very']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = tw.name.value_counts().index.to_list()\n",
    "names.sort()\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "bad_names = ['a',\n",
    " 'actually',\n",
    " 'all',\n",
    " 'an',\n",
    " 'by',\n",
    " 'getting',\n",
    " 'his',\n",
    " 'incredibly',\n",
    " 'infuriating',\n",
    " 'just',\n",
    " 'mad',\n",
    " 'my',\n",
    " 'not',\n",
    " 'officially',\n",
    " 'old',\n",
    " 'quite',\n",
    " 'space',\n",
    " 'such',\n",
    " 'the',\n",
    " 'this',\n",
    " 'unacceptable',\n",
    " 'very']\n",
    "\n",
    "tw.loc[tw.name.isin(bad_names), 'name'] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: name, dtype: object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.loc[tw.name.isin(bad_names), 'name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Abby',\n",
       " 'Ace',\n",
       " 'Acro',\n",
       " 'Adele',\n",
       " 'Aiden',\n",
       " 'Aja',\n",
       " 'Akumi',\n",
       " 'Al',\n",
       " 'Albert',\n",
       " 'Albus',\n",
       " 'Aldrick',\n",
       " 'Alejandro',\n",
       " 'Alexander',\n",
       " 'Alexanderson',\n",
       " 'Alf',\n",
       " 'Alfie',\n",
       " 'Alfy',\n",
       " 'Alice',\n",
       " 'Amber',\n",
       " 'Ambrose',\n",
       " 'Amy',\n",
       " 'Amélie',\n",
       " 'Anakin',\n",
       " 'Andru',\n",
       " 'Andy',\n",
       " 'Angel',\n",
       " 'Anna',\n",
       " 'Anthony',\n",
       " 'Antony',\n",
       " 'Apollo',\n",
       " 'Aqua',\n",
       " 'Archie',\n",
       " 'Arlen',\n",
       " 'Arlo',\n",
       " 'Arnie',\n",
       " 'Arnold',\n",
       " 'Arya',\n",
       " 'Ash',\n",
       " 'Asher',\n",
       " 'Ashleigh',\n",
       " 'Aspen',\n",
       " 'Astrid',\n",
       " 'Atlas',\n",
       " 'Atticus',\n",
       " 'Aubie',\n",
       " 'Augie',\n",
       " 'Autumn',\n",
       " 'Ava',\n",
       " 'Axel',\n",
       " 'Bailey',\n",
       " 'Baloo',\n",
       " 'Balto',\n",
       " 'Banditt',\n",
       " 'Banjo',\n",
       " 'Barclay',\n",
       " 'Barney',\n",
       " 'Baron',\n",
       " 'Barry',\n",
       " 'Batdog',\n",
       " 'Bauer',\n",
       " 'Baxter',\n",
       " 'Bayley',\n",
       " 'BeBe',\n",
       " 'Bear',\n",
       " 'Beau',\n",
       " 'Beckham',\n",
       " 'Beebop',\n",
       " 'Beemo',\n",
       " 'Bell',\n",
       " 'Bella',\n",
       " 'Belle',\n",
       " 'Ben',\n",
       " 'Benedict',\n",
       " 'Benji',\n",
       " 'Benny',\n",
       " 'Bentley',\n",
       " 'Berb',\n",
       " 'Berkeley',\n",
       " 'Bernie',\n",
       " 'Bert',\n",
       " 'Bertson',\n",
       " 'Betty',\n",
       " 'Beya',\n",
       " 'Biden',\n",
       " 'Bilbo',\n",
       " 'Billl',\n",
       " 'Billy',\n",
       " 'Binky',\n",
       " 'Birf',\n",
       " 'Bisquick',\n",
       " 'Blakely',\n",
       " 'Blanket',\n",
       " 'Blipson',\n",
       " 'Blitz',\n",
       " 'Bloo',\n",
       " 'Bloop',\n",
       " 'Blu',\n",
       " 'Blue',\n",
       " 'Bluebert',\n",
       " 'Bo',\n",
       " 'Bob',\n",
       " 'Bobb',\n",
       " 'Bobbay',\n",
       " 'Bobble',\n",
       " 'Bobby',\n",
       " 'Bode',\n",
       " 'Bodie',\n",
       " 'Bonaparte',\n",
       " 'Bones',\n",
       " 'Bookstore',\n",
       " 'Boomer',\n",
       " 'Boots',\n",
       " 'Boston',\n",
       " 'Bowie',\n",
       " 'Brad',\n",
       " 'Bradlay',\n",
       " 'Bradley',\n",
       " 'Brady',\n",
       " 'Brandi',\n",
       " 'Brandonald',\n",
       " 'Brandy',\n",
       " 'Brat',\n",
       " 'Brian',\n",
       " 'Brockly',\n",
       " 'Brody',\n",
       " 'Bronte',\n",
       " 'Brooks',\n",
       " 'Brownie',\n",
       " 'Bruce',\n",
       " 'Brudge',\n",
       " 'Bruiser',\n",
       " 'Bruno',\n",
       " 'Brutus',\n",
       " 'Bubba',\n",
       " 'Bubbles',\n",
       " 'Buckley',\n",
       " 'Buddah',\n",
       " 'Buddy',\n",
       " 'Bungalo',\n",
       " 'Burt',\n",
       " 'Butter',\n",
       " 'Butters',\n",
       " 'Cal',\n",
       " 'Calbert',\n",
       " 'Cali',\n",
       " 'Callie',\n",
       " 'Calvin',\n",
       " 'Canela',\n",
       " 'Cannon',\n",
       " 'Carbon',\n",
       " 'Carl',\n",
       " 'Carll',\n",
       " 'Carly',\n",
       " 'Carper',\n",
       " 'Carter',\n",
       " 'Caryl',\n",
       " 'Cash',\n",
       " 'Cassie',\n",
       " 'CeCe',\n",
       " 'Cecil',\n",
       " 'Cedrick',\n",
       " 'Cermet',\n",
       " 'Chadrick',\n",
       " 'Champ',\n",
       " 'Charl',\n",
       " 'Charles',\n",
       " 'Charleson',\n",
       " 'Charlie',\n",
       " 'Chase',\n",
       " 'Chaz',\n",
       " 'Cheesy',\n",
       " 'Chef',\n",
       " 'Chelsea',\n",
       " 'Cheryl',\n",
       " 'Chesney',\n",
       " 'Chester',\n",
       " 'Chesterson',\n",
       " 'Chet',\n",
       " 'Chevy',\n",
       " 'Chip',\n",
       " 'Chipson',\n",
       " 'Chloe',\n",
       " 'Chompsky',\n",
       " 'Christoper',\n",
       " 'Chubbs',\n",
       " 'Chuck',\n",
       " 'Chuckles',\n",
       " 'Chuq',\n",
       " 'Churlie',\n",
       " 'Cilantro',\n",
       " 'Clarence',\n",
       " 'Clark',\n",
       " 'Clarkus',\n",
       " 'Clarq',\n",
       " 'Claude',\n",
       " 'Cleopatricia',\n",
       " 'Clifford',\n",
       " 'Clybe',\n",
       " 'Clyde',\n",
       " 'Coco',\n",
       " 'Cody',\n",
       " 'Colby',\n",
       " 'Coleman',\n",
       " 'Colin',\n",
       " 'Combo',\n",
       " 'Comet',\n",
       " 'Cooper',\n",
       " 'Coops',\n",
       " 'Coopson',\n",
       " 'Cora',\n",
       " 'Corey',\n",
       " 'Covach',\n",
       " 'Craig',\n",
       " 'Crawford',\n",
       " 'Creg',\n",
       " 'Crimson',\n",
       " 'Crouton',\n",
       " 'Crumpet',\n",
       " 'Crystal',\n",
       " 'Cuddles',\n",
       " 'Cupcake',\n",
       " 'Cupid',\n",
       " 'Curtis',\n",
       " 'Daisy',\n",
       " 'Dakota',\n",
       " 'Dale',\n",
       " 'Dallas',\n",
       " 'Damon',\n",
       " 'Daniel',\n",
       " 'Danny',\n",
       " 'Dante',\n",
       " 'Darby',\n",
       " 'Darla',\n",
       " 'Darrel',\n",
       " 'Dash',\n",
       " 'Dave',\n",
       " 'Davey',\n",
       " 'Dawn',\n",
       " 'DayZ',\n",
       " 'Deacon',\n",
       " 'Derby',\n",
       " 'Derek',\n",
       " 'Devón',\n",
       " 'Dewey',\n",
       " 'Dex',\n",
       " 'Dexter',\n",
       " 'Dido',\n",
       " 'Dietrich',\n",
       " 'Diogi',\n",
       " 'Divine',\n",
       " 'Dixie',\n",
       " 'Django',\n",
       " 'Dobby',\n",
       " 'Doc',\n",
       " 'DonDon',\n",
       " 'Donny',\n",
       " 'Doobert',\n",
       " 'Dook',\n",
       " 'Dot',\n",
       " 'Dotsy',\n",
       " 'Doug',\n",
       " 'Duchess',\n",
       " 'Duddles',\n",
       " 'Dudley',\n",
       " 'Dug',\n",
       " 'Duke',\n",
       " 'Dunkin',\n",
       " 'Durg',\n",
       " 'Dutch',\n",
       " 'Dwight',\n",
       " 'Dylan',\n",
       " 'Earl',\n",
       " 'Eazy',\n",
       " 'Ebby',\n",
       " 'Ed',\n",
       " 'Edd',\n",
       " 'Edgar',\n",
       " 'Edmund',\n",
       " 'Eevee',\n",
       " 'Einstein',\n",
       " 'Eleanor',\n",
       " 'Eli',\n",
       " 'Ellie',\n",
       " 'Elliot',\n",
       " 'Emanuel',\n",
       " 'Ember',\n",
       " 'Emma',\n",
       " 'Emmie',\n",
       " 'Emmy',\n",
       " 'Enchilada',\n",
       " 'Erik',\n",
       " 'Eriq',\n",
       " 'Ester',\n",
       " 'Eugene',\n",
       " 'Eve',\n",
       " 'Evy',\n",
       " 'Fabio',\n",
       " 'Farfle',\n",
       " 'Ferg',\n",
       " 'Fido',\n",
       " 'Fiji',\n",
       " 'Fillup',\n",
       " 'Filup',\n",
       " 'Finley',\n",
       " 'Finn',\n",
       " 'Finnegus',\n",
       " 'Fiona',\n",
       " 'Fizz',\n",
       " 'Flash',\n",
       " 'Fletcher',\n",
       " 'Florence',\n",
       " 'Flurpson',\n",
       " 'Flávio',\n",
       " 'Frank',\n",
       " 'Frankie',\n",
       " 'Franklin',\n",
       " 'Franq',\n",
       " 'Fred',\n",
       " 'Freddery',\n",
       " 'Frönq',\n",
       " 'Furzey',\n",
       " 'Fwed',\n",
       " 'Fynn',\n",
       " 'Gabe',\n",
       " 'Gary',\n",
       " 'General',\n",
       " 'Genevieve',\n",
       " 'Geno',\n",
       " 'Geoff',\n",
       " 'George',\n",
       " 'Georgie',\n",
       " 'Gerald',\n",
       " 'Gerbald',\n",
       " 'Gert',\n",
       " 'Gidget',\n",
       " 'Gilbert',\n",
       " 'Gin',\n",
       " 'Ginger',\n",
       " 'Gizmo',\n",
       " 'Glacier',\n",
       " 'Glenn',\n",
       " 'Godi',\n",
       " 'Godzilla',\n",
       " 'Goliath',\n",
       " 'Goose',\n",
       " 'Gordon',\n",
       " 'Grady',\n",
       " 'Grey',\n",
       " 'Griffin',\n",
       " 'Griswold',\n",
       " 'Grizz',\n",
       " 'Grizzie',\n",
       " 'Grizzwald',\n",
       " 'Gromit',\n",
       " 'Gunner',\n",
       " 'Gus',\n",
       " 'Gustaf',\n",
       " 'Gustav',\n",
       " 'Gòrdón',\n",
       " 'Hall',\n",
       " 'Halo',\n",
       " 'Hammond',\n",
       " 'Hamrick',\n",
       " 'Hank',\n",
       " 'Hanz',\n",
       " 'Happy',\n",
       " 'Harlso',\n",
       " 'Harnold',\n",
       " 'Harold',\n",
       " 'Harper',\n",
       " 'Harrison',\n",
       " 'Harry',\n",
       " 'Harvey',\n",
       " 'Hazel',\n",
       " 'Hector',\n",
       " 'Heinrich',\n",
       " 'Henry',\n",
       " 'Herald',\n",
       " 'Herb',\n",
       " 'Hercules',\n",
       " 'Herm',\n",
       " 'Hermione',\n",
       " 'Hero',\n",
       " 'Herschel',\n",
       " 'Hobbes',\n",
       " 'Holly',\n",
       " 'Horace',\n",
       " 'Howie',\n",
       " 'Hubertson',\n",
       " 'Huck',\n",
       " 'Humphrey',\n",
       " 'Hunter',\n",
       " 'Hurley',\n",
       " 'Huxley',\n",
       " 'Iggy',\n",
       " 'Ike',\n",
       " 'Indie',\n",
       " 'Iroh',\n",
       " 'Ito',\n",
       " 'Ivar',\n",
       " 'Izzy',\n",
       " 'JD',\n",
       " 'Jack',\n",
       " 'Jackie',\n",
       " 'Jackson',\n",
       " 'Jameson',\n",
       " 'Jamesy',\n",
       " 'Jangle',\n",
       " 'Jareld',\n",
       " 'Jarod',\n",
       " 'Jarvis',\n",
       " 'Jaspers',\n",
       " 'Jax',\n",
       " 'Jay',\n",
       " 'Jaycob',\n",
       " 'Jazz',\n",
       " 'Jazzy',\n",
       " 'Jeb',\n",
       " 'Jebberson',\n",
       " 'Jed',\n",
       " 'Jeffrey',\n",
       " 'Jeffri',\n",
       " 'Jeffrie',\n",
       " 'Jennifur',\n",
       " 'Jeph',\n",
       " 'Jeremy',\n",
       " 'Jerome',\n",
       " 'Jerry',\n",
       " 'Jersey',\n",
       " 'Jesse',\n",
       " 'Jessifer',\n",
       " 'Jessiga',\n",
       " 'Jett',\n",
       " 'Jim',\n",
       " 'Jimbo',\n",
       " 'Jiminus',\n",
       " 'Jiminy',\n",
       " 'Jimison',\n",
       " 'Jimothy',\n",
       " 'Jo',\n",
       " 'Jockson',\n",
       " 'Joey',\n",
       " 'Jomathan',\n",
       " 'Jonah',\n",
       " 'Jordy',\n",
       " 'Josep',\n",
       " 'Joshwa',\n",
       " 'Juckson',\n",
       " 'Julio',\n",
       " 'Julius',\n",
       " 'Juno',\n",
       " 'Kaia',\n",
       " 'Kaiya',\n",
       " 'Kallie',\n",
       " 'Kane',\n",
       " 'Kanu',\n",
       " 'Kara',\n",
       " 'Karl',\n",
       " 'Karll',\n",
       " 'Karma',\n",
       " 'Kathmandu',\n",
       " 'Katie',\n",
       " 'Kawhi',\n",
       " 'Kayla',\n",
       " 'Keet',\n",
       " 'Keith',\n",
       " 'Kellogg',\n",
       " 'Ken',\n",
       " 'Kendall',\n",
       " 'Kenneth',\n",
       " 'Kenny',\n",
       " 'Kenzie',\n",
       " 'Keurig',\n",
       " 'Kevin',\n",
       " 'Kevon',\n",
       " 'Kial',\n",
       " 'Kilo',\n",
       " 'Kingsley',\n",
       " 'Kirby',\n",
       " 'Kirk',\n",
       " 'Klein',\n",
       " 'Klevin',\n",
       " 'Kloey',\n",
       " 'Kobe',\n",
       " 'Koda',\n",
       " 'Kody',\n",
       " 'Koko',\n",
       " 'Kollin',\n",
       " 'Kona',\n",
       " 'Kota',\n",
       " 'Kramer',\n",
       " 'Kreg',\n",
       " 'Kreggory',\n",
       " 'Kulet',\n",
       " 'Kuyu',\n",
       " 'Kyle',\n",
       " 'Kyro',\n",
       " 'Lacy',\n",
       " 'Laela',\n",
       " 'Laika',\n",
       " 'Lambeau',\n",
       " 'Lance',\n",
       " 'Larry',\n",
       " 'Lassie',\n",
       " 'Layla',\n",
       " 'Leela',\n",
       " 'Lennon',\n",
       " 'Lenny',\n",
       " 'Lenox',\n",
       " 'Leo',\n",
       " 'Leonard',\n",
       " 'Leonidas',\n",
       " 'Levi',\n",
       " 'Liam',\n",
       " 'Lilah',\n",
       " 'Lili',\n",
       " 'Lilli',\n",
       " 'Lillie',\n",
       " 'Lilly',\n",
       " 'Lily',\n",
       " 'Lincoln',\n",
       " 'Linda',\n",
       " 'Link',\n",
       " 'Linus',\n",
       " 'Lipton',\n",
       " 'Livvie',\n",
       " 'Lizzie',\n",
       " 'Logan',\n",
       " 'Loki',\n",
       " 'Lola',\n",
       " 'Lolo',\n",
       " 'Longfellow',\n",
       " 'Loomis',\n",
       " 'Lorelei',\n",
       " 'Lorenzo',\n",
       " 'Lou',\n",
       " 'Louie',\n",
       " 'Louis',\n",
       " 'Luca',\n",
       " 'Lucia',\n",
       " 'Lucky',\n",
       " 'Lucy',\n",
       " 'Lugan',\n",
       " 'Lulu',\n",
       " 'Luna',\n",
       " 'Lupe',\n",
       " 'Luther',\n",
       " 'Mabel',\n",
       " 'Mac',\n",
       " 'Mack',\n",
       " 'Maddie',\n",
       " 'Maggie',\n",
       " 'Mairi',\n",
       " 'Maisey',\n",
       " 'Major',\n",
       " 'Maks',\n",
       " 'Malcolm',\n",
       " 'Malikai',\n",
       " 'Margo',\n",
       " 'Mark',\n",
       " 'Marlee',\n",
       " 'Marley',\n",
       " 'Marq',\n",
       " 'Marty',\n",
       " 'Marvin',\n",
       " 'Mary',\n",
       " 'Mason',\n",
       " 'Mattie',\n",
       " 'Maude',\n",
       " 'Mauve',\n",
       " 'Max',\n",
       " 'Maxaroni',\n",
       " 'Maximus',\n",
       " 'Maxwell',\n",
       " 'Maya',\n",
       " 'Meatball',\n",
       " 'Meera',\n",
       " 'Meyer',\n",
       " 'Mia',\n",
       " 'Michelangelope',\n",
       " 'Miguel',\n",
       " 'Mike',\n",
       " 'Miley',\n",
       " 'Milky',\n",
       " 'Millie',\n",
       " 'Milo',\n",
       " 'Mimosa',\n",
       " 'Mingus',\n",
       " 'Mister',\n",
       " 'Misty',\n",
       " 'Mitch',\n",
       " 'Mo',\n",
       " 'Moe',\n",
       " 'Mojo',\n",
       " 'Mollie',\n",
       " 'Molly',\n",
       " 'Mona',\n",
       " 'Monkey',\n",
       " 'Monster',\n",
       " 'Monty',\n",
       " 'Moofasa',\n",
       " 'Mookie',\n",
       " 'Moose',\n",
       " 'Moreton',\n",
       " 'Mosby',\n",
       " 'Murphy',\n",
       " 'Mutt',\n",
       " 'Mya',\n",
       " 'Nala',\n",
       " 'Naphaniel',\n",
       " 'Napolean',\n",
       " 'Nelly',\n",
       " 'Neptune',\n",
       " 'Newt',\n",
       " 'Nico',\n",
       " 'Nida',\n",
       " 'Nigel',\n",
       " 'Nimbus',\n",
       " 'Noah',\n",
       " 'Nollie',\n",
       " 'Noosh',\n",
       " 'Norman',\n",
       " 'Nugget',\n",
       " 'O',\n",
       " 'Oakley',\n",
       " 'Obi',\n",
       " 'Obie',\n",
       " 'Oddie',\n",
       " 'Odie',\n",
       " 'Odin',\n",
       " 'Olaf',\n",
       " 'Ole',\n",
       " 'Olive',\n",
       " 'Oliver',\n",
       " 'Olivia',\n",
       " 'Oliviér',\n",
       " 'Ollie',\n",
       " 'Opal',\n",
       " 'Opie',\n",
       " 'Oreo',\n",
       " 'Orion',\n",
       " 'Oscar',\n",
       " 'Oshie',\n",
       " 'Otis',\n",
       " 'Ozzie',\n",
       " 'Ozzy',\n",
       " 'Pablo',\n",
       " 'Paisley',\n",
       " 'Pancake',\n",
       " 'Panda',\n",
       " 'Patch',\n",
       " 'Patrick',\n",
       " 'Paull',\n",
       " 'Pavlov',\n",
       " 'Pawnd',\n",
       " 'Peaches',\n",
       " 'Peanut',\n",
       " 'Penelope',\n",
       " 'Penny',\n",
       " 'Pepper',\n",
       " 'Percy',\n",
       " 'Perry',\n",
       " 'Pete',\n",
       " 'Petrick',\n",
       " 'Pherb',\n",
       " 'Phil',\n",
       " 'Philbert',\n",
       " 'Philippe',\n",
       " 'Phineas',\n",
       " 'Phred',\n",
       " 'Pickles',\n",
       " 'Pilot',\n",
       " 'Pinot',\n",
       " 'Pip',\n",
       " 'Piper',\n",
       " 'Pippa',\n",
       " 'Pippin',\n",
       " 'Pipsy',\n",
       " 'Pluto',\n",
       " 'Poppy',\n",
       " 'Pubert',\n",
       " 'Puff',\n",
       " 'Pumpkin',\n",
       " 'Pupcasso',\n",
       " 'Quinn',\n",
       " 'Ralf',\n",
       " 'Ralph',\n",
       " 'Ralpher',\n",
       " 'Ralphie',\n",
       " 'Ralphson',\n",
       " 'Ralphus',\n",
       " 'Ralphy',\n",
       " 'Ralphé',\n",
       " 'Rambo',\n",
       " 'Randall',\n",
       " 'Raphael',\n",
       " 'Rascal',\n",
       " 'Raymond',\n",
       " 'Reagan',\n",
       " 'Reese',\n",
       " 'Reggie',\n",
       " 'Reginald',\n",
       " 'Remington',\n",
       " 'Remus',\n",
       " 'Remy',\n",
       " 'Reptar',\n",
       " 'Rey',\n",
       " 'Rhino',\n",
       " 'Richie',\n",
       " 'Ricky',\n",
       " 'Ridley',\n",
       " 'Riley',\n",
       " 'Rilo',\n",
       " 'Rinna',\n",
       " 'River',\n",
       " 'Rizzo',\n",
       " 'Rizzy',\n",
       " 'Robin',\n",
       " 'Rocco',\n",
       " 'Rocky',\n",
       " 'Rodman',\n",
       " 'Rodney',\n",
       " 'Rolf',\n",
       " 'Romeo',\n",
       " 'Ron',\n",
       " 'Ronduh',\n",
       " 'Ronnie',\n",
       " 'Rontu',\n",
       " 'Rooney',\n",
       " 'Roosevelt',\n",
       " 'Rorie',\n",
       " 'Rory',\n",
       " 'Roscoe',\n",
       " 'Rose',\n",
       " 'Rosie',\n",
       " 'Rover',\n",
       " 'Rubio',\n",
       " 'Ruby',\n",
       " 'Rudy',\n",
       " 'Rueben',\n",
       " 'Ruffles',\n",
       " 'Rufio',\n",
       " 'Rufus',\n",
       " 'Rumble',\n",
       " 'Rumpole',\n",
       " 'Rupert',\n",
       " 'Rusty',\n",
       " 'Sadie',\n",
       " 'Sage',\n",
       " 'Sailer',\n",
       " 'Sailor',\n",
       " 'Sam',\n",
       " 'Sammy',\n",
       " 'Sampson',\n",
       " 'Samsom',\n",
       " 'Samson',\n",
       " 'Sandra',\n",
       " 'Sandy',\n",
       " 'Sansa',\n",
       " 'Sarge',\n",
       " 'Saydee',\n",
       " 'Schnitzel',\n",
       " 'Schnozz',\n",
       " 'Scooter',\n",
       " 'Scott',\n",
       " 'Scout',\n",
       " 'Scruffers',\n",
       " 'Seamus',\n",
       " 'Sebastian',\n",
       " 'Sephie',\n",
       " 'Severus',\n",
       " 'Shadoe',\n",
       " 'Shadow',\n",
       " 'Shaggy',\n",
       " 'Shakespeare',\n",
       " 'Shawwn',\n",
       " 'Shelby',\n",
       " 'Shikha',\n",
       " 'Shiloh',\n",
       " 'Shnuggles',\n",
       " 'Shooter',\n",
       " 'Siba',\n",
       " 'Sid',\n",
       " 'Sierra',\n",
       " 'Simba',\n",
       " 'Skittle',\n",
       " 'Skittles',\n",
       " 'Sky',\n",
       " 'Skye',\n",
       " 'Smiley',\n",
       " 'Smokey',\n",
       " 'Snickers',\n",
       " 'Snicku',\n",
       " 'Snoop',\n",
       " 'Snoopy',\n",
       " 'Sobe',\n",
       " 'Socks',\n",
       " 'Sojourner',\n",
       " 'Solomon',\n",
       " 'Sonny',\n",
       " 'Sophie',\n",
       " 'Sora',\n",
       " 'Spanky',\n",
       " 'Spark',\n",
       " 'Sparky',\n",
       " 'Spencer',\n",
       " 'Sprinkles',\n",
       " 'Sprout',\n",
       " 'Staniel',\n",
       " 'Stanley',\n",
       " 'Stark',\n",
       " 'Stefan',\n",
       " 'Stella',\n",
       " 'Stephan',\n",
       " 'Stephanus',\n",
       " 'Steve',\n",
       " 'Steven',\n",
       " 'Stewie',\n",
       " 'Storkson',\n",
       " 'Stormy',\n",
       " 'Strider',\n",
       " 'Striker',\n",
       " 'Strudel',\n",
       " 'Stu',\n",
       " 'Stuart',\n",
       " 'Stubert',\n",
       " 'Sugar',\n",
       " 'Suki',\n",
       " 'Sully',\n",
       " 'Sundance',\n",
       " 'Sunny',\n",
       " 'Sunshine',\n",
       " 'Superpup',\n",
       " 'Swagger',\n",
       " 'Sweet',\n",
       " 'Sweets',\n",
       " 'Taco',\n",
       " 'Tango',\n",
       " 'Tanner',\n",
       " 'Tassy',\n",
       " 'Tater',\n",
       " 'Tayzie',\n",
       " 'Taz',\n",
       " 'Tebow',\n",
       " 'Ted',\n",
       " 'Tedders',\n",
       " 'Teddy',\n",
       " 'Tedrick',\n",
       " 'Terrance',\n",
       " 'Terrenth',\n",
       " 'Terry',\n",
       " 'Tess',\n",
       " 'Theo',\n",
       " 'Theodore',\n",
       " 'Thor',\n",
       " 'Thumas',\n",
       " 'Tiger',\n",
       " 'Tilly',\n",
       " 'Timber',\n",
       " 'Timison',\n",
       " 'Timmy',\n",
       " 'Timofy',\n",
       " 'Tino',\n",
       " 'Titan',\n",
       " 'Tito',\n",
       " 'Tobi',\n",
       " 'Toby',\n",
       " 'Todo',\n",
       " 'Toffee',\n",
       " 'Tom',\n",
       " 'Tommy',\n",
       " 'Tonks',\n",
       " 'Torque',\n",
       " 'Tove',\n",
       " 'Travis',\n",
       " 'Traviss',\n",
       " 'Trevith',\n",
       " 'Trigger',\n",
       " 'Trip',\n",
       " 'Tripp',\n",
       " 'Trooper',\n",
       " 'Tuck',\n",
       " 'Tucker',\n",
       " 'Tuco',\n",
       " 'Tug',\n",
       " 'Tupawc',\n",
       " 'Tycho',\n",
       " 'Tyr',\n",
       " 'Tyrone',\n",
       " 'Tyrus',\n",
       " 'Ulysses',\n",
       " 'Venti',\n",
       " 'Vince',\n",
       " 'Vincent',\n",
       " 'Vinnie',\n",
       " 'Vinscent',\n",
       " 'Vixen',\n",
       " 'Wafer',\n",
       " 'Waffles',\n",
       " 'Walker',\n",
       " 'Wallace',\n",
       " 'Wally',\n",
       " 'Walter',\n",
       " 'Watson',\n",
       " 'Wesley',\n",
       " 'Wiggles',\n",
       " 'Willem',\n",
       " 'William',\n",
       " 'Willie',\n",
       " 'Willow',\n",
       " 'Willy',\n",
       " 'Wilson',\n",
       " 'Winifred',\n",
       " 'Winnie',\n",
       " 'Winston',\n",
       " 'Wishes',\n",
       " 'Wyatt',\n",
       " 'Yoda',\n",
       " 'Yogi',\n",
       " 'Yukon',\n",
       " 'Zara',\n",
       " 'Zeek',\n",
       " 'Zeke',\n",
       " 'Zeus',\n",
       " 'Ziva',\n",
       " 'Zoe',\n",
       " 'Zoey',\n",
       " 'Zooey',\n",
       " 'Zuzu',\n",
       " 'life',\n",
       " 'light',\n",
       " 'one']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "updated_names = tw.name.value_counts().index.to_list()\n",
    "updated_names.sort()\n",
    "updated_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# black = NaNs\n",
    "plt.figure(figsize = (12,8))\n",
    "sns.heatmap(tw.notna())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## only_dogs table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "remove all 324 data points that are definitely not dogs (all p's=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_dogs = df_pred.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop all rows that are not dogs\n",
    "condition = ((only_dogs.p1_dog==False) & (only_dogs.p2_dog==False) & (only_dogs.p3_dog==False))\n",
    "only_dogs.drop(only_dogs[condition].index, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1751, 12)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_dogs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only_dogs.p1.value_counts().index.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "215"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_dogs.p1.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "remove underscores and capitalise dog breeds (p1,p2 and p3 columns)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_dogs.p1 = only_dogs.p1.str.replace('_', ' ').str.title()\n",
    "only_dogs.p2 = only_dogs.p2.str.replace('_', ' ').str.title()\n",
    "only_dogs.p3 = only_dogs.p3.str.replace('_', ' ').str.title()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1358     Golden Retriever\n",
      "1302      German Shepherd\n",
      "1767     Golden Retriever\n",
      "802     Shetland Sheepdog\n",
      "1252               Kelpie\n",
      "Name: p1, dtype: object\n",
      "1067                  Miniature Poodle\n",
      "667                          Chihuahua\n",
      "336                             Basset\n",
      "1535                          Cardigan\n",
      "1824    American Staffordshire Terrier\n",
      "Name: p2, dtype: object\n",
      "251         Norfolk Terrier\n",
      "172                 Basenji\n",
      "499                Pekinese\n",
      "1449             Bloodhound\n",
      "1099    American Black Bear\n",
      "Name: p3, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(only_dogs.p1.sample(5))\n",
    "print(only_dogs.p2.sample(5))\n",
    "print(only_dogs.p3.sample(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from what i know abt ML 100% confidence is not valid so i will remove any data point with 1 confidence "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "technically this is only a test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "no data point in the table with 1 confidence. It was removed when we dropped objects that are not dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, jpg_url, img_num, p1, p1_conf, p1_dog, p2, p2_conf, p2_dog, p3, p3_conf, p3_dog]\n",
       "Index: []"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_dogs.loc[(only_dogs.p1_conf==1) |(only_dogs.p2_conf==1)|(only_dogs.p3_conf == 1), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id    0\n",
       "jpg_url     0\n",
       "img_num     0\n",
       "p1          0\n",
       "p1_conf     0\n",
       "p1_dog      0\n",
       "p2          0\n",
       "p2_conf     0\n",
       "p2_dog      0\n",
       "p3          0\n",
       "p3_conf     0\n",
       "p3_dog      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_dogs.isna().sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "no duplicates in this table either"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Master Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "join all 3 tables to get a complete table with all relavant info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2097 entries, 0 to 2355\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            2097 non-null   int64              \n",
      " 1   timestamp           2097 non-null   datetime64[ns, UTC]\n",
      " 2   source              2097 non-null   object             \n",
      " 3   text                2097 non-null   object             \n",
      " 4   expanded_urls       2094 non-null   object             \n",
      " 5   rating_numerator    2097 non-null   int64              \n",
      " 6   rating_denominator  2097 non-null   int64              \n",
      " 7   name                1396 non-null   object             \n",
      " 8   dog_stage           336 non-null    object             \n",
      "dtypes: datetime64[ns, UTC](1), int64(3), object(5)\n",
      "memory usage: 163.8+ KB\n"
     ]
    }
   ],
   "source": [
    "tw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1751 entries, 0 to 2073\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  1751 non-null   int64  \n",
      " 1   jpg_url   1751 non-null   object \n",
      " 2   img_num   1751 non-null   int64  \n",
      " 3   p1        1751 non-null   object \n",
      " 4   p1_conf   1751 non-null   float64\n",
      " 5   p1_dog    1751 non-null   bool   \n",
      " 6   p2        1751 non-null   object \n",
      " 7   p2_conf   1751 non-null   float64\n",
      " 8   p2_dog    1751 non-null   bool   \n",
      " 9   p3        1751 non-null   object \n",
      " 10  p3_conf   1751 non-null   float64\n",
      " 11  p3_dog    1751 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 141.9+ KB\n"
     ]
    }
   ],
   "source": [
    "only_dogs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2090 entries, 0 to 2089\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   tweet_id        2090 non-null   int64\n",
      " 1   favorite_count  2090 non-null   int64\n",
      " 2   retweet_count   2090 non-null   int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 49.1 KB\n"
     ]
    }
   ],
   "source": [
    "tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tw is: 2097 rows\n",
      "only_dogs is: 1751 rows\n",
      "tweets is: 2090 rows\n"
     ]
    }
   ],
   "source": [
    "print(f\"tw is: {tw.shape[0]} rows\")\n",
    "print(f\"only_dogs is: {only_dogs.shape[0]} rows\")\n",
    "print(f\"tweets is: {tweets.shape[0]} rows\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "master = only_dogs.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw_copy = tw.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_copy = tweets.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "master = master.merge(tw_copy,how='inner', on='tweet_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1666 entries, 0 to 1665\n",
      "Data columns (total 20 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            1666 non-null   int64              \n",
      " 1   jpg_url             1666 non-null   object             \n",
      " 2   img_num             1666 non-null   int64              \n",
      " 3   p1                  1666 non-null   object             \n",
      " 4   p1_conf             1666 non-null   float64            \n",
      " 5   p1_dog              1666 non-null   bool               \n",
      " 6   p2                  1666 non-null   object             \n",
      " 7   p2_conf             1666 non-null   float64            \n",
      " 8   p2_dog              1666 non-null   bool               \n",
      " 9   p3                  1666 non-null   object             \n",
      " 10  p3_conf             1666 non-null   float64            \n",
      " 11  p3_dog              1666 non-null   bool               \n",
      " 12  timestamp           1666 non-null   datetime64[ns, UTC]\n",
      " 13  source              1666 non-null   object             \n",
      " 14  text                1666 non-null   object             \n",
      " 15  expanded_urls       1666 non-null   object             \n",
      " 16  rating_numerator    1666 non-null   int64              \n",
      " 17  rating_denominator  1666 non-null   int64              \n",
      " 18  name                1190 non-null   object             \n",
      " 19  dog_stage           257 non-null    object             \n",
      "dtypes: bool(3), datetime64[ns, UTC](1), float64(3), int64(4), object(9)\n",
      "memory usage: 239.2+ KB\n"
     ]
    }
   ],
   "source": [
    "master.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "master = master.merge(tweets_copy,how='inner', on='tweet_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1659 entries, 0 to 1658\n",
      "Data columns (total 22 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            1659 non-null   int64              \n",
      " 1   jpg_url             1659 non-null   object             \n",
      " 2   img_num             1659 non-null   int64              \n",
      " 3   p1                  1659 non-null   object             \n",
      " 4   p1_conf             1659 non-null   float64            \n",
      " 5   p1_dog              1659 non-null   bool               \n",
      " 6   p2                  1659 non-null   object             \n",
      " 7   p2_conf             1659 non-null   float64            \n",
      " 8   p2_dog              1659 non-null   bool               \n",
      " 9   p3                  1659 non-null   object             \n",
      " 10  p3_conf             1659 non-null   float64            \n",
      " 11  p3_dog              1659 non-null   bool               \n",
      " 12  timestamp           1659 non-null   datetime64[ns, UTC]\n",
      " 13  source              1659 non-null   object             \n",
      " 14  text                1659 non-null   object             \n",
      " 15  expanded_urls       1659 non-null   object             \n",
      " 16  rating_numerator    1659 non-null   int64              \n",
      " 17  rating_denominator  1659 non-null   int64              \n",
      " 18  name                1183 non-null   object             \n",
      " 19  dog_stage           256 non-null    object             \n",
      " 20  favorite_count      1659 non-null   int64              \n",
      " 21  retweet_count       1659 non-null   int64              \n",
      "dtypes: bool(3), datetime64[ns, UTC](1), float64(3), int64(6), object(9)\n",
      "memory usage: 264.1+ KB\n"
     ]
    }
   ],
   "source": [
    "master.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'jpg_url', 'img_num', 'p1', 'p1_conf', 'p1_dog', 'p2',\n",
       "       'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog', 'timestamp', 'source',\n",
       "       'text', 'expanded_urls', 'rating_numerator', 'rating_denominator',\n",
       "       'name', 'dog_stage', 'favorite_count', 'retweet_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop all unimportant columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "master = master.drop(['jpg_url', 'img_num', 'source','rating_denominator'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>674788554665512960</td>\n",
       "      <td>Miniature Poodle</td>\n",
       "      <td>0.349561</td>\n",
       "      <td>True</td>\n",
       "      <td>Toy Poodle</td>\n",
       "      <td>0.154711</td>\n",
       "      <td>True</td>\n",
       "      <td>Maltese Dog</td>\n",
       "      <td>0.134229</td>\n",
       "      <td>True</td>\n",
       "      <td>2015-12-10 03:11:43+00:00</td>\n",
       "      <td>Say hello to Maggie. She's a Western Septic Do...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/674788554...</td>\n",
       "      <td>9</td>\n",
       "      <td>Maggie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>761</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id                p1   p1_conf  p1_dog          p2  \\\n",
       "341  674788554665512960  Miniature Poodle  0.349561    True  Toy Poodle   \n",
       "\n",
       "      p2_conf  p2_dog           p3   p3_conf  p3_dog  \\\n",
       "341  0.154711    True  Maltese Dog  0.134229    True   \n",
       "\n",
       "                    timestamp  \\\n",
       "341 2015-12-10 03:11:43+00:00   \n",
       "\n",
       "                                                  text  \\\n",
       "341  Say hello to Maggie. She's a Western Septic Do...   \n",
       "\n",
       "                                         expanded_urls  rating_numerator  \\\n",
       "341  https://twitter.com/dog_rates/status/674788554...                 9   \n",
       "\n",
       "       name dog_stage  favorite_count  retweet_count  \n",
       "341  Maggie       NaN             761            194  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# master.iloc[1567, 12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if any of `p1_dog` or `p1_dog` or `p1_dog` == True set the respecrive p1/p2/p3 as the dog breed for that datapoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "mask1 = (master.p1_dog == True)\n",
    "master['breed'] = master.loc[mask1]['p1']\n",
    "\n",
    "\n",
    "mask2 = ((master.p1_dog == False) & (master.p2_dog == True))\n",
    "master['breed2'] = master.loc[mask2]['p2']\n",
    "\n",
    "\n",
    "mask3 = ((master.p1_dog == False) & (master.p2_dog == False) & (master.p3_dog == True))\n",
    "master['breed3'] = master.loc[mask3]['p3']\n",
    "\n",
    "\n",
    "# master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>...</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>breed</th>\n",
       "      <th>breed2</th>\n",
       "      <th>breed3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1654</th>\n",
       "      <td>891087950875897856</td>\n",
       "      <td>Chesapeake Bay Retriever</td>\n",
       "      <td>0.425595</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish Terrier</td>\n",
       "      <td>0.116317</td>\n",
       "      <td>True</td>\n",
       "      <td>Indian Elephant</td>\n",
       "      <td>0.076902</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>Here we have a majestic great white breaching ...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891087950...</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18501</td>\n",
       "      <td>2743</td>\n",
       "      <td>Chesapeake Bay Retriever</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>891327558926688256</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>...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36721</td>\n",
       "      <td>8173</td>\n",
       "      <td>Basset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>891689557279858688</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>...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>Darla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38467</td>\n",
       "      <td>7595</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>891815181378084864</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>...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>Archie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22884</td>\n",
       "      <td>3645</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>892177421306343426</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>...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30437</td>\n",
       "      <td>5518</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                        p1   p1_conf  p1_dog  \\\n",
       "1654  891087950875897856  Chesapeake Bay Retriever  0.425595    True   \n",
       "1655  891327558926688256                    Basset  0.555712    True   \n",
       "1656  891689557279858688               Paper Towel  0.170278   False   \n",
       "1657  891815181378084864                 Chihuahua  0.716012    True   \n",
       "1658  892177421306343426                 Chihuahua  0.323581    True   \n",
       "\n",
       "                      p2   p2_conf  p2_dog                           p3  \\\n",
       "1654       Irish Terrier  0.116317    True              Indian Elephant   \n",
       "1655    English Springer  0.225770    True  German Short-Haired Pointer   \n",
       "1656  Labrador Retriever  0.168086    True                      Spatula   \n",
       "1657            Malamute  0.078253    True                       Kelpie   \n",
       "1658            Pekinese  0.090647    True                     Papillon   \n",
       "\n",
       "       p3_conf  p3_dog  ...  \\\n",
       "1654  0.076902   False  ...   \n",
       "1655  0.175219    True  ...   \n",
       "1656  0.040836   False  ...   \n",
       "1657  0.031379    True  ...   \n",
       "1658  0.068957    True  ...   \n",
       "\n",
       "                                                   text  \\\n",
       "1654  Here we have a majestic great white breaching ...   \n",
       "1655  This is Franklin. He would like you to stop ca...   \n",
       "1656  This is Darla. She commenced a snooze mid meal...   \n",
       "1657  This is Archie. He is a rare Norwegian Pouncin...   \n",
       "1658  This is Tilly. She's just checking pup on you....   \n",
       "\n",
       "                                          expanded_urls rating_numerator  \\\n",
       "1654  https://twitter.com/dog_rates/status/891087950...               13   \n",
       "1655  https://twitter.com/dog_rates/status/891327558...               12   \n",
       "1656  https://twitter.com/dog_rates/status/891689557...               13   \n",
       "1657  https://twitter.com/dog_rates/status/891815181...               12   \n",
       "1658  https://twitter.com/dog_rates/status/892177421...               13   \n",
       "\n",
       "          name dog_stage favorite_count  retweet_count  \\\n",
       "1654       NaN       NaN          18501           2743   \n",
       "1655  Franklin       NaN          36721           8173   \n",
       "1656     Darla       NaN          38467           7595   \n",
       "1657    Archie       NaN          22884           3645   \n",
       "1658     Tilly       NaN          30437           5518   \n",
       "\n",
       "                         breed              breed2 breed3  \n",
       "1654  Chesapeake Bay Retriever                 NaN    NaN  \n",
       "1655                    Basset                 NaN    NaN  \n",
       "1656                       NaN  Labrador Retriever    NaN  \n",
       "1657                 Chihuahua                 NaN    NaN  \n",
       "1658                 Chihuahua                 NaN    NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mask = ((master.p1_dog == True) & (master.p2_dog == True)& (master.p3_dog == True))\n",
    "\n",
    "# master.loc[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1659 entries, 0 to 1658\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype              \n",
      "---  ------            --------------  -----              \n",
      " 0   tweet_id          1659 non-null   int64              \n",
      " 1   p1                1659 non-null   object             \n",
      " 2   p1_conf           1659 non-null   float64            \n",
      " 3   p1_dog            1659 non-null   bool               \n",
      " 4   p2                1659 non-null   object             \n",
      " 5   p2_conf           1659 non-null   float64            \n",
      " 6   p2_dog            1659 non-null   bool               \n",
      " 7   p3                1659 non-null   object             \n",
      " 8   p3_conf           1659 non-null   float64            \n",
      " 9   p3_dog            1659 non-null   bool               \n",
      " 10  timestamp         1659 non-null   datetime64[ns, UTC]\n",
      " 11  text              1659 non-null   object             \n",
      " 12  expanded_urls     1659 non-null   object             \n",
      " 13  rating_numerator  1659 non-null   int64              \n",
      " 14  name              1183 non-null   object             \n",
      " 15  dog_stage         256 non-null    object             \n",
      " 16  favorite_count    1659 non-null   int64              \n",
      " 17  retweet_count     1659 non-null   int64              \n",
      " 18  breed             1456 non-null   object             \n",
      " 19  breed2            143 non-null    object             \n",
      " 20  breed3            60 non-null     object             \n",
      "dtypes: bool(3), datetime64[ns, UTC](1), float64(3), int64(4), object(10)\n",
      "memory usage: 251.1+ KB\n"
     ]
    }
   ],
   "source": [
    "master.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "master.breed.replace(np.nan, '', inplace=True)\n",
    "master.breed2.replace(np.nan, '', inplace=True)\n",
    "master.breed3.replace(np.nan, '', inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "master.breed = master.breed + master.breed2 + master.breed3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "      <th>...</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>breed</th>\n",
       "      <th>breed2</th>\n",
       "      <th>breed3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1654</th>\n",
       "      <td>891087950875897856</td>\n",
       "      <td>Chesapeake Bay Retriever</td>\n",
       "      <td>0.425595</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish Terrier</td>\n",
       "      <td>0.116317</td>\n",
       "      <td>True</td>\n",
       "      <td>Indian Elephant</td>\n",
       "      <td>0.076902</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>Here we have a majestic great white breaching ...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891087950...</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18501</td>\n",
       "      <td>2743</td>\n",
       "      <td>Chesapeake Bay Retriever</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>891327558926688256</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>...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36721</td>\n",
       "      <td>8173</td>\n",
       "      <td>Basset</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>891689557279858688</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>...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>Darla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38467</td>\n",
       "      <td>7595</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>891815181378084864</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>...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>Archie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22884</td>\n",
       "      <td>3645</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>892177421306343426</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>...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30437</td>\n",
       "      <td>5518</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                        p1   p1_conf  p1_dog  \\\n",
       "1654  891087950875897856  Chesapeake Bay Retriever  0.425595    True   \n",
       "1655  891327558926688256                    Basset  0.555712    True   \n",
       "1656  891689557279858688               Paper Towel  0.170278   False   \n",
       "1657  891815181378084864                 Chihuahua  0.716012    True   \n",
       "1658  892177421306343426                 Chihuahua  0.323581    True   \n",
       "\n",
       "                      p2   p2_conf  p2_dog                           p3  \\\n",
       "1654       Irish Terrier  0.116317    True              Indian Elephant   \n",
       "1655    English Springer  0.225770    True  German Short-Haired Pointer   \n",
       "1656  Labrador Retriever  0.168086    True                      Spatula   \n",
       "1657            Malamute  0.078253    True                       Kelpie   \n",
       "1658            Pekinese  0.090647    True                     Papillon   \n",
       "\n",
       "       p3_conf  p3_dog  ...  \\\n",
       "1654  0.076902   False  ...   \n",
       "1655  0.175219    True  ...   \n",
       "1656  0.040836   False  ...   \n",
       "1657  0.031379    True  ...   \n",
       "1658  0.068957    True  ...   \n",
       "\n",
       "                                                   text  \\\n",
       "1654  Here we have a majestic great white breaching ...   \n",
       "1655  This is Franklin. He would like you to stop ca...   \n",
       "1656  This is Darla. She commenced a snooze mid meal...   \n",
       "1657  This is Archie. He is a rare Norwegian Pouncin...   \n",
       "1658  This is Tilly. She's just checking pup on you....   \n",
       "\n",
       "                                          expanded_urls rating_numerator  \\\n",
       "1654  https://twitter.com/dog_rates/status/891087950...               13   \n",
       "1655  https://twitter.com/dog_rates/status/891327558...               12   \n",
       "1656  https://twitter.com/dog_rates/status/891689557...               13   \n",
       "1657  https://twitter.com/dog_rates/status/891815181...               12   \n",
       "1658  https://twitter.com/dog_rates/status/892177421...               13   \n",
       "\n",
       "          name dog_stage favorite_count  retweet_count  \\\n",
       "1654       NaN       NaN          18501           2743   \n",
       "1655  Franklin       NaN          36721           8173   \n",
       "1656     Darla       NaN          38467           7595   \n",
       "1657    Archie       NaN          22884           3645   \n",
       "1658     Tilly       NaN          30437           5518   \n",
       "\n",
       "                         breed              breed2 breed3  \n",
       "1654  Chesapeake Bay Retriever                             \n",
       "1655                    Basset                             \n",
       "1656        Labrador Retriever  Labrador Retriever         \n",
       "1657                 Chihuahua                             \n",
       "1658                 Chihuahua                             \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1659 entries, 0 to 1658\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype              \n",
      "---  ------            --------------  -----              \n",
      " 0   tweet_id          1659 non-null   int64              \n",
      " 1   p1                1659 non-null   object             \n",
      " 2   p1_conf           1659 non-null   float64            \n",
      " 3   p1_dog            1659 non-null   bool               \n",
      " 4   p2                1659 non-null   object             \n",
      " 5   p2_conf           1659 non-null   float64            \n",
      " 6   p2_dog            1659 non-null   bool               \n",
      " 7   p3                1659 non-null   object             \n",
      " 8   p3_conf           1659 non-null   float64            \n",
      " 9   p3_dog            1659 non-null   bool               \n",
      " 10  timestamp         1659 non-null   datetime64[ns, UTC]\n",
      " 11  text              1659 non-null   object             \n",
      " 12  expanded_urls     1659 non-null   object             \n",
      " 13  rating_numerator  1659 non-null   int64              \n",
      " 14  name              1183 non-null   object             \n",
      " 15  dog_stage         256 non-null    object             \n",
      " 16  favorite_count    1659 non-null   int64              \n",
      " 17  retweet_count     1659 non-null   int64              \n",
      " 18  breed             1659 non-null   object             \n",
      " 19  breed2            1659 non-null   object             \n",
      " 20  breed3            1659 non-null   object             \n",
      "dtypes: bool(3), datetime64[ns, UTC](1), float64(3), int64(4), object(10)\n",
      "memory usage: 251.1+ KB\n"
     ]
    }
   ],
   "source": [
    "master.info()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'p1', 'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3',\n",
       "       'p3_conf', 'p3_dog', 'timestamp', 'text', 'expanded_urls',\n",
       "       'rating_numerator', 'name', 'dog_stage', 'favorite_count',\n",
       "       'retweet_count', 'breed', 'breed2', 'breed3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the rest of the irrelevant columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "master.drop(['p1', 'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3',\n",
    "       'p3_conf', 'p3_dog', 'breed2', 'breed3'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>breed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>671542985629241344</td>\n",
       "      <td>2015-12-01 04:14:59+00:00</td>\n",
       "      <td>This is JD (stands for \"just dog\"). He's like ...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/671542985...</td>\n",
       "      <td>10</td>\n",
       "      <td>JD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019</td>\n",
       "      <td>521</td>\n",
       "      <td>Shetland Sheepdog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>767122157629476866</td>\n",
       "      <td>2016-08-20 22:12:29+00:00</td>\n",
       "      <td>This is Rupert. You betrayed him with bath tim...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/767122157...</td>\n",
       "      <td>13</td>\n",
       "      <td>Rupert</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9975</td>\n",
       "      <td>2792</td>\n",
       "      <td>Toy Poodle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1422</th>\n",
       "      <td>829141528400556032</td>\n",
       "      <td>2017-02-08 01:35:19+00:00</td>\n",
       "      <td>This is Malcolm. He goes from sneaky tongue sl...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/829141528...</td>\n",
       "      <td>12</td>\n",
       "      <td>Malcolm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23876</td>\n",
       "      <td>7130</td>\n",
       "      <td>Golden Retriever</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                 timestamp  \\\n",
       "232   671542985629241344 2015-12-01 04:14:59+00:00   \n",
       "1116  767122157629476866 2016-08-20 22:12:29+00:00   \n",
       "1422  829141528400556032 2017-02-08 01:35:19+00:00   \n",
       "\n",
       "                                                   text  \\\n",
       "232   This is JD (stands for \"just dog\"). He's like ...   \n",
       "1116  This is Rupert. You betrayed him with bath tim...   \n",
       "1422  This is Malcolm. He goes from sneaky tongue sl...   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "232   https://twitter.com/dog_rates/status/671542985...                10   \n",
       "1116  https://twitter.com/dog_rates/status/767122157...                13   \n",
       "1422  https://twitter.com/dog_rates/status/829141528...                12   \n",
       "\n",
       "         name dog_stage  favorite_count  retweet_count              breed  \n",
       "232        JD       NaN            1019            521  Shetland Sheepdog  \n",
       "1116   Rupert       NaN            9975           2792         Toy Poodle  \n",
       "1422  Malcolm       NaN           23876           7130   Golden Retriever  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is the end of the cleaning process!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Insights & Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save master dataframe without index column\n",
    "master.to_csv(\"twitter_archive_master.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.read_csv(\"twitter_archive_master.csv\", )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>breed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2015-11-15 22:32:08+00:00</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2338</td>\n",
       "      <td>445</td>\n",
       "      <td>Welsh Springer Spaniel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>2015-11-15 23:05:30+00:00</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118</td>\n",
       "      <td>41</td>\n",
       "      <td>Redbone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>2015-11-15 23:21:54+00:00</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "      <td>39</td>\n",
       "      <td>German Shepherd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>2015-11-16 00:04:52+00:00</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>261</td>\n",
       "      <td>122</td>\n",
       "      <td>Rhodesian Ridgeback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>2015-11-16 00:24:50+00:00</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>38</td>\n",
       "      <td>Miniature Pinscher</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                  timestamp  \\\n",
       "0  666020888022790149  2015-11-15 22:32:08+00:00   \n",
       "1  666029285002620928  2015-11-15 23:05:30+00:00   \n",
       "2  666033412701032449  2015-11-15 23:21:54+00:00   \n",
       "3  666044226329800704  2015-11-16 00:04:52+00:00   \n",
       "4  666049248165822465  2015-11-16 00:24:50+00:00   \n",
       "\n",
       "                                                text  \\\n",
       "0  Here we have a Japanese Irish Setter. Lost eye...   \n",
       "1  This is a western brown Mitsubishi terrier. Up...   \n",
       "2  Here is a very happy pup. Big fan of well-main...   \n",
       "3  This is a purebred Piers Morgan. Loves to Netf...   \n",
       "4  Here we have a 1949 1st generation vulpix. Enj...   \n",
       "\n",
       "                                       expanded_urls  rating_numerator name  \\\n",
       "0  https://twitter.com/dog_rates/status/666020888...                 8  NaN   \n",
       "1  https://twitter.com/dog_rates/status/666029285...                 7  NaN   \n",
       "2  https://twitter.com/dog_rates/status/666033412...                 9  NaN   \n",
       "3  https://twitter.com/dog_rates/status/666044226...                 6  NaN   \n",
       "4  https://twitter.com/dog_rates/status/666049248...                 5  NaN   \n",
       "\n",
       "  dog_stage  favorite_count  retweet_count                   breed  \n",
       "0       NaN            2338            445  Welsh Springer Spaniel  \n",
       "1       NaN             118             41                 Redbone  \n",
       "2       NaN             107             39         German Shepherd  \n",
       "3       NaN             261            122     Rhodesian Ridgeback  \n",
       "4       NaN              94             38      Miniature Pinscher  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Dog Breed')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10 most tweeted abt dog breeds\n",
    "master['breed'].value_counts()[:10].plot(kind='barh')\n",
    "plt.title(\"10 most tweeted abt dog breeds\")\n",
    "plt.xlabel(\"Tweet Count\")\n",
    "plt.ylabel(\"Dog Breed\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Dog Breed')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10 least tweeted abt dog breeds\n",
    "master['breed'].value_counts()[-10:].plot(kind='barh')\n",
    "plt.title(\"10 least tweeted abt dog breeds\")\n",
    "plt.xlabel(\"Tweet Count\")\n",
    "plt.ylabel(\"Dog Breed\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Golden Retriever\n",
       "dtype: object"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.breed.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Golden Retriever        156\n",
       "Labrador Retriever      104\n",
       "Pembroke                 94\n",
       "Chihuahua                90\n",
       "Pug                      62\n",
       "                       ... \n",
       "Irish Wolfhound           1\n",
       "Silky Terrier             1\n",
       "Clumber                   1\n",
       "Bouvier Des Flandres      1\n",
       "Standard Schnauzer        1\n",
       "Name: breed, Length: 113, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.breed.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Golden Retriever is the most rated/popular dog breed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "113 unique dog breeds in this data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.breed.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12     419\n",
       "10     357\n",
       "11     352\n",
       "13     217\n",
       "9      132\n",
       "8       68\n",
       "7       32\n",
       "14      21\n",
       "6       16\n",
       "5       14\n",
       "4        7\n",
       "3        5\n",
       "2        2\n",
       "144      1\n",
       "1        1\n",
       "165      1\n",
       "24       1\n",
       "26       1\n",
       "27       1\n",
       "44       1\n",
       "45       1\n",
       "50       1\n",
       "60       1\n",
       "75       1\n",
       "80       1\n",
       "84       1\n",
       "88       1\n",
       "99       1\n",
       "121      1\n",
       "0        1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.rating_numerator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = master.groupby(by='breed').mean().copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bedlington Terrier</th>\n",
       "      <td>7.843395e+17</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>22468.833333</td>\n",
       "      <td>7063.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saluki</th>\n",
       "      <td>8.315403e+17</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>21633.500000</td>\n",
       "      <td>4354.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French Bulldog</th>\n",
       "      <td>7.851394e+17</td>\n",
       "      <td>11.166667</td>\n",
       "      <td>18243.766667</td>\n",
       "      <td>4643.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bouvier Des Flandres</th>\n",
       "      <td>8.612885e+17</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16010.000000</td>\n",
       "      <td>3776.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghan Hound</th>\n",
       "      <td>8.041621e+17</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>15317.666667</td>\n",
       "      <td>5034.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tibetan Terrier</th>\n",
       "      <td>6.973258e+17</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>1631.000000</td>\n",
       "      <td>470.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Irish Wolfhound</th>\n",
       "      <td>6.717356e+17</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1348.000000</td>\n",
       "      <td>697.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japanese Spaniel</th>\n",
       "      <td>6.773010e+17</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1174.000000</td>\n",
       "      <td>382.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Groenendael</th>\n",
       "      <td>6.812840e+17</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1020.000000</td>\n",
       "      <td>235.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brabancon Griffon</th>\n",
       "      <td>6.725689e+17</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>781.000000</td>\n",
       "      <td>245.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          tweet_id  rating_numerator  favorite_count  \\\n",
       "breed                                                                  \n",
       "Bedlington Terrier    7.843395e+17         10.333333    22468.833333   \n",
       "Saluki                8.315403e+17         12.500000    21633.500000   \n",
       "French Bulldog        7.851394e+17         11.166667    18243.766667   \n",
       "Bouvier Des Flandres  8.612885e+17         13.000000    16010.000000   \n",
       "Afghan Hound          8.041621e+17          9.666667    15317.666667   \n",
       "...                            ...               ...             ...   \n",
       "Tibetan Terrier       6.973258e+17          9.250000     1631.000000   \n",
       "Irish Wolfhound       6.717356e+17         11.000000     1348.000000   \n",
       "Japanese Spaniel      6.773010e+17          5.000000     1174.000000   \n",
       "Groenendael           6.812840e+17         10.000000     1020.000000   \n",
       "Brabancon Griffon     6.725689e+17         10.000000      781.000000   \n",
       "\n",
       "                      retweet_count  \n",
       "breed                                \n",
       "Bedlington Terrier      7063.166667  \n",
       "Saluki                  4354.250000  \n",
       "French Bulldog          4643.000000  \n",
       "Bouvier Des Flandres    3776.000000  \n",
       "Afghan Hound            5034.333333  \n",
       "...                             ...  \n",
       "Tibetan Terrier          470.500000  \n",
       "Irish Wolfhound          697.000000  \n",
       "Japanese Spaniel         382.000000  \n",
       "Groenendael              235.500000  \n",
       "Brabancon Griffon        245.666667  \n",
       "\n",
       "[113 rows x 4 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sort_values(by='favorite_count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "on average, Bedlington Terrier has highest favorites count while Brabancon Griffon has lowest favorite count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bedlington Terrier</th>\n",
       "      <td>7.843395e+17</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>22468.833333</td>\n",
       "      <td>7063.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghan Hound</th>\n",
       "      <td>8.041621e+17</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>15317.666667</td>\n",
       "      <td>5034.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Poodle</th>\n",
       "      <td>7.406170e+17</td>\n",
       "      <td>10.727273</td>\n",
       "      <td>12449.181818</td>\n",
       "      <td>4714.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French Bulldog</th>\n",
       "      <td>7.851394e+17</td>\n",
       "      <td>11.166667</td>\n",
       "      <td>18243.766667</td>\n",
       "      <td>4643.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English Springer</th>\n",
       "      <td>7.153015e+17</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>12634.300000</td>\n",
       "      <td>4628.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Entlebucher</th>\n",
       "      <td>6.798621e+17</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2374.000000</td>\n",
       "      <td>591.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tibetan Terrier</th>\n",
       "      <td>6.973258e+17</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>1631.000000</td>\n",
       "      <td>470.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japanese Spaniel</th>\n",
       "      <td>6.773010e+17</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1174.000000</td>\n",
       "      <td>382.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brabancon Griffon</th>\n",
       "      <td>6.725689e+17</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>781.000000</td>\n",
       "      <td>245.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Groenendael</th>\n",
       "      <td>6.812840e+17</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1020.000000</td>\n",
       "      <td>235.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        tweet_id  rating_numerator  favorite_count  \\\n",
       "breed                                                                \n",
       "Bedlington Terrier  7.843395e+17         10.333333    22468.833333   \n",
       "Afghan Hound        8.041621e+17          9.666667    15317.666667   \n",
       "Standard Poodle     7.406170e+17         10.727273    12449.181818   \n",
       "French Bulldog      7.851394e+17         11.166667    18243.766667   \n",
       "English Springer    7.153015e+17         10.500000    12634.300000   \n",
       "...                          ...               ...             ...   \n",
       "Entlebucher         6.798621e+17         11.000000     2374.000000   \n",
       "Tibetan Terrier     6.973258e+17          9.250000     1631.000000   \n",
       "Japanese Spaniel    6.773010e+17          5.000000     1174.000000   \n",
       "Brabancon Griffon   6.725689e+17         10.000000      781.000000   \n",
       "Groenendael         6.812840e+17         10.000000     1020.000000   \n",
       "\n",
       "                    retweet_count  \n",
       "breed                              \n",
       "Bedlington Terrier    7063.166667  \n",
       "Afghan Hound          5034.333333  \n",
       "Standard Poodle       4714.909091  \n",
       "French Bulldog        4643.000000  \n",
       "English Springer      4628.700000  \n",
       "...                           ...  \n",
       "Entlebucher            591.000000  \n",
       "Tibetan Terrier        470.500000  \n",
       "Japanese Spaniel       382.000000  \n",
       "Brabancon Griffon      245.666667  \n",
       "Groenendael            235.500000  \n",
       "\n",
       "[113 rows x 4 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sort_values(by='retweet_count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "on average, Bedlington Terrier has highest retweet count while Brabancon Griffon has retweet count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\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>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clumber</th>\n",
       "      <td>7.780270e+17</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>6456.000000</td>\n",
       "      <td>1570.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soft-Coated Wheaten Terrier</th>\n",
       "      <td>7.044397e+17</td>\n",
       "      <td>21.357143</td>\n",
       "      <td>3026.214286</td>\n",
       "      <td>898.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Highland White Terrier</th>\n",
       "      <td>7.235322e+17</td>\n",
       "      <td>14.687500</td>\n",
       "      <td>5413.562500</td>\n",
       "      <td>1254.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Great Pyrenees</th>\n",
       "      <td>7.390970e+17</td>\n",
       "      <td>14.666667</td>\n",
       "      <td>10967.133333</td>\n",
       "      <td>3804.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chow</th>\n",
       "      <td>7.473842e+17</td>\n",
       "      <td>14.166667</td>\n",
       "      <td>8460.770833</td>\n",
       "      <td>2356.770833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ibizan Hound</th>\n",
       "      <td>6.844888e+17</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>2858.750000</td>\n",
       "      <td>1250.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tibetan Terrier</th>\n",
       "      <td>6.973258e+17</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>1631.000000</td>\n",
       "      <td>470.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scotch Terrier</th>\n",
       "      <td>6.892838e+17</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3154.000000</td>\n",
       "      <td>1060.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walker Hound</th>\n",
       "      <td>7.333173e+17</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5803.600000</td>\n",
       "      <td>2256.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japanese Spaniel</th>\n",
       "      <td>6.773010e+17</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1174.000000</td>\n",
       "      <td>382.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 tweet_id  rating_numerator  favorite_count  \\\n",
       "breed                                                                         \n",
       "Clumber                      7.780270e+17         27.000000     6456.000000   \n",
       "Soft-Coated Wheaten Terrier  7.044397e+17         21.357143     3026.214286   \n",
       "West Highland White Terrier  7.235322e+17         14.687500     5413.562500   \n",
       "Great Pyrenees               7.390970e+17         14.666667    10967.133333   \n",
       "Chow                         7.473842e+17         14.166667     8460.770833   \n",
       "...                                   ...               ...             ...   \n",
       "Ibizan Hound                 6.844888e+17          9.250000     2858.750000   \n",
       "Tibetan Terrier              6.973258e+17          9.250000     1631.000000   \n",
       "Scotch Terrier               6.892838e+17          9.000000     3154.000000   \n",
       "Walker Hound                 7.333173e+17          9.000000     5803.600000   \n",
       "Japanese Spaniel             6.773010e+17          5.000000     1174.000000   \n",
       "\n",
       "                             retweet_count  \n",
       "breed                                       \n",
       "Clumber                        1570.000000  \n",
       "Soft-Coated Wheaten Terrier     898.000000  \n",
       "West Highland White Terrier    1254.000000  \n",
       "Great Pyrenees                 3804.666667  \n",
       "Chow                           2356.770833  \n",
       "...                                    ...  \n",
       "Ibizan Hound                   1250.250000  \n",
       "Tibetan Terrier                 470.500000  \n",
       "Scotch Terrier                 1060.000000  \n",
       "Walker Hound                   2256.800000  \n",
       "Japanese Spaniel                382.000000  \n",
       "\n",
       "[113 rows x 4 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.sort_values(by='rating_numerator', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "on average, Clumber is highest rated (27/10) while Japanese Spaniel is lowest rated (5/10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25,10))\n",
    "sns.distplot(master.favorite_count).set_title(\"Avg Tweet Count\")\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8390.63471971067"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.favorite_count.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25,10))\n",
    "sns.distplot(master.retweet_count, norm_hist=True).set_title(\"Avg Retweet Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2425.4653405666063"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.retweet_count.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Applications/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25,10))\n",
    "sns.distplot(master.rating_numerator).set_title(\"Avg Rating\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.384569017480409"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.rating_numerator.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "average rating for any dog is 11.4 out of 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_df = master.groupby(by='name').mean().copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stephan</th>\n",
       "      <td>8.071068e+17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>116832.0</td>\n",
       "      <td>54622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamesy</th>\n",
       "      <td>8.664507e+17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>113216.0</td>\n",
       "      <td>31853.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duddles</th>\n",
       "      <td>8.794158e+17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>96570.0</td>\n",
       "      <td>39349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aja</th>\n",
       "      <td>8.777365e+17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>72366.0</td>\n",
       "      <td>16709.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lilly</th>\n",
       "      <td>8.305833e+17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>65288.0</td>\n",
       "      <td>16270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lugan</th>\n",
       "      <td>6.667393e+17</td>\n",
       "      <td>10.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jockson</th>\n",
       "      <td>6.668044e+17</td>\n",
       "      <td>8.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Keet</th>\n",
       "      <td>6.670622e+17</td>\n",
       "      <td>10.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fwed</th>\n",
       "      <td>6.673934e+17</td>\n",
       "      <td>8.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christoper</th>\n",
       "      <td>6.666914e+17</td>\n",
       "      <td>8.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>832 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  rating_numerator  favorite_count  retweet_count\n",
       "name                                                                     \n",
       "Stephan     8.071068e+17              13.0        116832.0        54622.0\n",
       "Jamesy      8.664507e+17              13.0        113216.0        31853.0\n",
       "Duddles     8.794158e+17              13.0         96570.0        39349.0\n",
       "Aja         8.777365e+17              13.0         72366.0        16709.0\n",
       "Lilly       8.305833e+17              13.0         65288.0        16270.0\n",
       "...                  ...               ...             ...            ...\n",
       "Lugan       6.667393e+17              10.0           218.0           60.0\n",
       "Jockson     6.668044e+17               8.0           216.0           84.0\n",
       "Keet        6.670622e+17              10.0           203.0           47.0\n",
       "Fwed        6.673934e+17               8.0           178.0           51.0\n",
       "Christoper  6.666914e+17               8.0           171.0           44.0\n",
       "\n",
       "[832 rows x 4 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_df.sort_values(by='favorite_count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stephan is the most favorited dog name while Christoper is the least"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Stephan</th>\n",
       "      <td>8.071068e+17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>116832.0</td>\n",
       "      <td>54622.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duddles</th>\n",
       "      <td>8.794158e+17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>96570.0</td>\n",
       "      <td>39349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jamesy</th>\n",
       "      <td>8.664507e+17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>113216.0</td>\n",
       "      <td>31853.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hurley</th>\n",
       "      <td>6.794628e+17</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30277.0</td>\n",
       "      <td>17916.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aja</th>\n",
       "      <td>8.777365e+17</td>\n",
       "      <td>13.0</td>\n",
       "      <td>72366.0</td>\n",
       "      <td>16709.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marvin</th>\n",
       "      <td>6.685678e+17</td>\n",
       "      <td>11.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kollin</th>\n",
       "      <td>6.695676e+17</td>\n",
       "      <td>9.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Keet</th>\n",
       "      <td>6.670622e+17</td>\n",
       "      <td>10.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Christoper</th>\n",
       "      <td>6.666914e+17</td>\n",
       "      <td>8.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bloo</th>\n",
       "      <td>6.706761e+17</td>\n",
       "      <td>8.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>832 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  rating_numerator  favorite_count  retweet_count\n",
       "name                                                                     \n",
       "Stephan     8.071068e+17              13.0        116832.0        54622.0\n",
       "Duddles     8.794158e+17              13.0         96570.0        39349.0\n",
       "Jamesy      8.664507e+17              13.0        113216.0        31853.0\n",
       "Hurley      6.794628e+17              11.0         30277.0        17916.0\n",
       "Aja         8.777365e+17              13.0         72366.0        16709.0\n",
       "...                  ...               ...             ...            ...\n",
       "Marvin      6.685678e+17              11.0           222.0           49.0\n",
       "Kollin      6.695676e+17               9.0           219.0           48.0\n",
       "Keet        6.670622e+17              10.0           203.0           47.0\n",
       "Christoper  6.666914e+17               8.0           171.0           44.0\n",
       "Bloo        6.706761e+17               8.0           229.0           37.0\n",
       "\n",
       "[832 rows x 4 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_df.sort_values(by='retweet_count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stephan is the most retweeted dog name while Bloo is the least"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Dog Name')"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "master['name'].value_counts()[:10].plot(kind='barh')\n",
    "plt.title(\"10 most tweeted dog names\")\n",
    "plt.xlabel(\"Tweet Count\")\n",
    "plt.ylabel(\"Dog Name\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Dog Name')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "master['name'].value_counts()[-10:].plot(kind='barh')\n",
    "plt.title(\"10 least tweeted dog names\")\n",
    "plt.xlabel(\"Tweet Count\")\n",
    "plt.ylabel(\"Dog Name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}