wrangle_act.ipynb

{
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   "source": [
    "# Project: Wrangle and Analyze Data from [@WeRateDogs](https://twitter.com/dog_rates)\n",
    "\n",
    "For this project, the goal was to wrangle WeRateDogs Twitter data to create interesting and trustworthy analyses and visualizations. The Twitter archive is great, but it only contains very basic tweet information. Additional gathering, then assessing and cleaning was required for \"Wow!\"-worthy analyses and visualizations.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gathering Data\n",
    "In the cell below, gather **all** three pieces of data for this project and load them in the notebook. **Note:** the methods required to gather each data are different.\n",
    "\n",
    "**`1.`** Directly download the WeRateDogs Twitter archive data (twitter_archive_enhanced.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import os\n",
    "import tweepy\n",
    "import json\n",
    "import credentials\n",
    "import matplotlib.pyplot as plt\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "\n",
       "   rating_denominator     name doggo floofer pupper puppo  \n",
       "0                  10  Phineas  None    None   None  None  \n",
       "1                  10    Tilly  None    None   None  None  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive = pd.read_csv('data/twitter-archive-enhanced.csv')\n",
    "archive.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "archive.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`2.`** Use the Requests library to download the tweet image prediction (image_predictions.tsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_name = 'data'\n",
    "\n",
    "# make directory if it doesn't already exist\n",
    "if not os.path.exists(folder_name):\n",
    "    os.makedirs(folder_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "\n",
    "# create the request\n",
    "response = requests.get(url)\n",
    "\n",
    "# access the content and write it to a file\n",
    "with open(os.path.join(folder_name, url.split('/')[-1]), mode='wb') as file:\n",
    "    file.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_prediction = pd.read_csv('data/image-predictions.tsv', sep='\\t')\n",
    "image_prediction.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`3.`** Use the Tweepy library to query additional data via the Twitter API (tweet_json.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "consumer_key = credentials.API_KEY\n",
    "consumer_secret = credentials.API_SECRET_KEY\n",
    "access_token = credentials.ACCESS_TOKEN\n",
    "access_secret = credentials.ACCESS_TOKEN_SECRET\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True, parser=tweepy.parsers.JSONParser())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'created_at': 'Tue Aug 01 16:23:56 +0000 2017',\n",
       " 'id': 892420643555336193,\n",
       " 'id_str': '892420643555336193',\n",
       " 'text': \"This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU\",\n",
       " 'truncated': False,\n",
       " 'entities': {'hashtags': [],\n",
       "  'symbols': [],\n",
       "  'user_mentions': [],\n",
       "  'urls': [],\n",
       "  'media': [{'id': 892420639486877696,\n",
       "    'id_str': '892420639486877696',\n",
       "    'indices': [86, 109],\n",
       "    'media_url': 'http://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',\n",
       "    'media_url_https': 'https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',\n",
       "    'url': 'https://t.co/MgUWQ76dJU',\n",
       "    'display_url': 'pic.twitter.com/MgUWQ76dJU',\n",
       "    'expanded_url': 'https://twitter.com/dog_rates/status/892420643555336193/photo/1',\n",
       "    'type': 'photo',\n",
       "    'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},\n",
       "     'medium': {'w': 540, 'h': 528, 'resize': 'fit'},\n",
       "     'small': {'w': 540, 'h': 528, 'resize': 'fit'},\n",
       "     'large': {'w': 540, 'h': 528, 'resize': 'fit'}}}]},\n",
       " 'extended_entities': {'media': [{'id': 892420639486877696,\n",
       "    'id_str': '892420639486877696',\n",
       "    'indices': [86, 109],\n",
       "    'media_url': 'http://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',\n",
       "    'media_url_https': 'https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',\n",
       "    'url': 'https://t.co/MgUWQ76dJU',\n",
       "    'display_url': 'pic.twitter.com/MgUWQ76dJU',\n",
       "    'expanded_url': 'https://twitter.com/dog_rates/status/892420643555336193/photo/1',\n",
       "    'type': 'photo',\n",
       "    'sizes': {'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},\n",
       "     'medium': {'w': 540, 'h': 528, 'resize': 'fit'},\n",
       "     'small': {'w': 540, 'h': 528, 'resize': 'fit'},\n",
       "     'large': {'w': 540, 'h': 528, 'resize': 'fit'}}}]},\n",
       " 'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>',\n",
       " 'in_reply_to_status_id': None,\n",
       " 'in_reply_to_status_id_str': None,\n",
       " 'in_reply_to_user_id': None,\n",
       " 'in_reply_to_user_id_str': None,\n",
       " 'in_reply_to_screen_name': None,\n",
       " 'user': {'id': 4196983835,\n",
       "  'id_str': '4196983835',\n",
       "  'name': 'WeRateDogs®',\n",
       "  'screen_name': 'dog_rates',\n",
       "  'location': 'all our links ➜',\n",
       "  'description': 'Your Only Source For Professional Dog Ratings Instagram and Facebook ➜ WeRateDogs partnerships@weratedogs.com | nonprofit: @15outof10 ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀',\n",
       "  'url': 'https://t.co/YPc2Xq4Va2',\n",
       "  'entities': {'url': {'urls': [{'url': 'https://t.co/YPc2Xq4Va2',\n",
       "      'expanded_url': 'http://links.weratedogs.com',\n",
       "      'display_url': 'links.weratedogs.com',\n",
       "      'indices': [0, 23]}]},\n",
       "   'description': {'urls': []}},\n",
       "  'protected': False,\n",
       "  'followers_count': 9257748,\n",
       "  'friends_count': 21,\n",
       "  'listed_count': 7382,\n",
       "  'created_at': 'Sun Nov 15 21:41:29 +0000 2015',\n",
       "  'favourites_count': 146876,\n",
       "  'utc_offset': None,\n",
       "  'time_zone': None,\n",
       "  'geo_enabled': True,\n",
       "  'verified': True,\n",
       "  'statuses_count': 15554,\n",
       "  'lang': None,\n",
       "  'contributors_enabled': False,\n",
       "  'is_translator': False,\n",
       "  'is_translation_enabled': False,\n",
       "  'profile_background_color': '000000',\n",
       "  'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "  'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "  'profile_background_tile': False,\n",
       "  'profile_image_url': 'http://pbs.twimg.com/profile_images/1478141668159148033/IOD8SZvx_normal.jpg',\n",
       "  'profile_image_url_https': 'https://pbs.twimg.com/profile_images/1478141668159148033/IOD8SZvx_normal.jpg',\n",
       "  'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1617810473',\n",
       "  'profile_link_color': 'F5ABB5',\n",
       "  'profile_sidebar_border_color': '000000',\n",
       "  'profile_sidebar_fill_color': '000000',\n",
       "  'profile_text_color': '000000',\n",
       "  'profile_use_background_image': False,\n",
       "  'has_extended_profile': False,\n",
       "  'default_profile': False,\n",
       "  'default_profile_image': False,\n",
       "  'following': False,\n",
       "  'follow_request_sent': False,\n",
       "  'notifications': False,\n",
       "  'translator_type': 'none',\n",
       "  'withheld_in_countries': []},\n",
       " 'geo': None,\n",
       " 'coordinates': None,\n",
       " 'place': None,\n",
       " 'contributors': None,\n",
       " 'is_quote_status': False,\n",
       " 'retweet_count': 7034,\n",
       " 'favorite_count': 33873,\n",
       " 'favorited': False,\n",
       " 'retweeted': False,\n",
       " 'possibly_sensitive': False,\n",
       " 'possibly_sensitive_appealable': False,\n",
       " 'lang': 'en'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the api to get info on a tweet using a tweet ID from the archive dataframe\n",
    "# this will help getting acquainted with the structure of the json\n",
    "tweet = api.get_status(892420643555336193)\n",
    "tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "892420643555336193"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# searching the dict for key info we need\n",
    "# notice the json returns tweet_id as merely id\n",
    "# this will be important when loading the json info into a df\n",
    "tweet['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33873"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# searching the dict for key info we need\n",
    "tweet['favorite_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7034"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# searching the dict for key info we need\n",
    "tweet['retweet_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "0362/2355 | Elapsed Time: 01:30\n",
      "0363/2355 | Error!\n",
      "0364/2355 | Elapsed Time: 01:30\n",
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      "0381/2355 | Elapsed Time: 01:35\n",
      "0382/2355 | Error!\n",
      "0383/2355 | Elapsed Time: 01:35\n",
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      "0504/2355 | Elapsed Time: 02:05\n",
      "0505/2355 | Elapsed Time: 02:05\n",
      "0506/2355 | Error!\n",
      "0507/2355 | Elapsed Time: 02:06\n",
      "0508/2355 | Elapsed Time: 02:06\n",
      "0509/2355 | Elapsed Time: 02:06\n",
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      "0520/2355 | Elapsed Time: 02:09\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0521/2355 | Elapsed Time: 02:09\n",
      "0522/2355 | Elapsed Time: 02:10\n",
      "0523/2355 | Elapsed Time: 02:10\n",
      "0524/2355 | Elapsed Time: 02:10\n",
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      "0526/2355 | Elapsed Time: 02:11\n",
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      "0538/2355 | Elapsed Time: 02:14\n",
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      "0564/2355 | Elapsed Time: 02:21\n",
      "0565/2355 | Elapsed Time: 02:21\n",
      "0566/2355 | Error!\n",
      "0567/2355 | Elapsed Time: 02:21\n",
      "0568/2355 | Elapsed Time: 02:22\n",
      "0569/2355 | Elapsed Time: 02:22\n",
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      "0607/2355 | Elapsed Time: 02:32\n",
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      "0748/2355 | Elapsed Time: 03:07\n",
      "0749/2355 | Elapsed Time: 03:07\n",
      "0750/2355 | Error!\n",
      "0751/2355 | Elapsed Time: 03:07\n",
      "0752/2355 | Elapsed Time: 03:08\n",
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      "0784/2355 | Error!\n",
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      "0815/2355 | Error!\n",
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      "0841/2355 | Error!\n",
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      "0887/2355 | Error!\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Rate limit reached. Sleeping for: 675\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Rate limit reached. Sleeping for: 676\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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   "source": [
    "tweet_ids = archive['tweet_id']\n",
    "tweet_errors = {}\n",
    "\n",
    "# include count to keep track of how many tweets are left\n",
    "count = 0\n",
    "\n",
    "with open ('data/tweet_json.txt', 'w') as file:\n",
    "    # use a timer to keep track of how long it's taking\n",
    "    # it'll likely take around 20-30 minutes\n",
    "    start = timer()\n",
    "    \n",
    "    # iterate through the tweet_ids series\n",
    "    for tweet_id in tweet_ids:\n",
    "        \n",
    "        # if there are deleted tweets, the code will break\n",
    "        # thus a try-except block will come in handy\n",
    "        try:\n",
    "            # use get_status to access tweet info\n",
    "            tweet = api.get_status(tweet_id, tweet_mode='extended')\n",
    "            \n",
    "            # dump the json info onto the new file\n",
    "            json.dump(tweet, file)\n",
    "            \n",
    "            # force the creation of a new line\n",
    "            file.write(\"\\n\")\n",
    "            \n",
    "            # use divmod to get the minutes and seconds elapsed\n",
    "            minutes = divmod((timer() - start), 60)\n",
    "            \n",
    "            # since this will take a long time to run,\n",
    "            # track progress for sanity reasons\n",
    "            print(f'{str(count).zfill(4)}/{tweet_ids.index[-1]} | Elapsed Time: {str(round(minutes[0])).zfill(2)}:{str(round(minutes[1])).zfill(2)}')\n",
    "            \n",
    "            # increase count +1 after every loop\n",
    "            count += 1\n",
    "        \n",
    "        except Exception as e:\n",
    "            tweet_errors[str(count).zfill(4) + \"_\" + str(tweet_id)] = tweet\n",
    "            print(f'{str(count).zfill(4)}/{tweet_ids.index[-1]} | Error!')\n",
    "            count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list to hold the data for each of our tweets\n",
    "data = []\n",
    "\n",
    "# open the file\n",
    "with open('data/tweet_json.txt', 'r') as file:\n",
    "    # iterate through each line\n",
    "    for line in file:\n",
    "        # append each line to the list\n",
    "        data.append(json.loads(line))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>29384</td>\n",
       "      <td>5309</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>22099</td>\n",
       "      <td>3490</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>36998</td>\n",
       "      <td>7243</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>35376</td>\n",
       "      <td>7784</td>\n",
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       "                   id  favorite_count  retweet_count\n",
       "0  892420643555336193           33873           7034\n",
       "1  892177421306343426           29384           5309\n",
       "2  891815181378084864           22099           3490\n",
       "3  891689557279858688           36998           7243\n",
       "4  891327558926688256           35376           7784"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the dataframe using the list \n",
    "# notice the column names must match the structure of the json\n",
    "# which means that tweet_id is called 'id'\n",
    "fav_rt = pd.DataFrame(data, columns=['id', 'favorite_count', 'retweet_count'])\n",
    "fav_rt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>33873</td>\n",
       "      <td>7034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "             tweet_id  favorite_count  retweet_count\n",
       "0  892420643555336193           33873           7034"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename the id column to 'tweet_id'\n",
    "# to facilitate merging DataFrames later\n",
    "fav_rt.rename(columns={'id': 'tweet_id'}, inplace=True)\n",
    "fav_rt.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
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        "row": 28,
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       "report_default": {
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    }
   },
   "source": [
    "---\n",
    "\n",
    "## Assessing Data\n",
    "In this section, detect and document at least **eight (8) quality issues and two (2) tidiness issue**. You must use **both** visual assessment\n",
    "programmatic assessement to assess the data.\n",
    "\n",
    "**Note:** pay attention to the following key points when you access the data.\n",
    "\n",
    "* You only want original ratings (no retweets) that have images. Though there are 2000+ tweets in the dataset, not all are dog ratings and some are retweets.\n",
    "* Assessing and cleaning the entire dataset completely would require a lot of time, and is not necessary to practice and demonstrate your skills in data wrangling. Therefore, the requirements of this project are only to assess and clean at least 8 quality issues and at least 2 tidiness issues in this dataset.\n",
    "* The fact that the rating numerators are greater than the denominators does not need to be cleaned. This [unique rating system](http://knowyourmeme.com/memes/theyre-good-dogs-brent) is a big part of the popularity of WeRateDogs.\n",
    "* You do not need to gather the tweets beyond August 1st, 2017. You can, but note that you won't be able to gather the image predictions for these tweets since you don't have access to the algorithm used.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visual Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2356 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0     892420643555336193                    NaN                  NaN   \n",
       "1     892177421306343426                    NaN                  NaN   \n",
       "2     891815181378084864                    NaN                  NaN   \n",
       "3     891689557279858688                    NaN                  NaN   \n",
       "4     891327558926688256                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "2351  666049248165822465                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "2355  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "0     2017-08-01 16:23:56 +0000   \n",
       "1     2017-08-01 00:17:27 +0000   \n",
       "2     2017-07-31 00:18:03 +0000   \n",
       "3     2017-07-30 15:58:51 +0000   \n",
       "4     2017-07-29 16:00:24 +0000   \n",
       "...                         ...   \n",
       "2351  2015-11-16 00:24:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "2355  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "0     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "0     This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1     This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2     This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3     This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4     This is Franklin. He would like you to stop ca...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "2351  Here we have a 1949 1st generation vulpix. Enj...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                          NaN                        NaN   \n",
       "1                          NaN                        NaN   \n",
       "2                          NaN                        NaN   \n",
       "3                          NaN                        NaN   \n",
       "4                          NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2351                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "0     https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1     https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2     https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3     https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4     https://twitter.com/dog_rates/status/891327558...                12   \n",
       "...                                                 ...               ...   \n",
       "2351  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator      name doggo floofer pupper puppo  \n",
       "0                     10   Phineas  None    None   None  None  \n",
       "1                     10     Tilly  None    None   None  None  \n",
       "2                     10    Archie  None    None   None  None  \n",
       "3                     10     Darla  None    None   None  None  \n",
       "4                     10  Franklin  None    None   None  None  \n",
       "...                  ...       ...   ...     ...    ...   ...  \n",
       "2351                  10      None  None    None   None  None  \n",
       "2352                  10         a  None    None   None  None  \n",
       "2353                  10         a  None    None   None  None  \n",
       "2354                  10         a  None    None   None  None  \n",
       "2355                  10      None  None    None   None  None  \n",
       "\n",
       "[2356 rows x 17 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# source column includes the HTML tag\n",
    "# There are some invalid entries in the name column, like 'a' or 'None' (instead of null)\n",
    "# The columns holding the information on dog stages are inconsistent (apparently 'None' was used instead of NaN)\n",
    "archive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>33873</td>\n",
       "      <td>7034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>29384</td>\n",
       "      <td>5309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>22099</td>\n",
       "      <td>3490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>36998</td>\n",
       "      <td>7243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>35376</td>\n",
       "      <td>7784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2322</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>89</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2323</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>247</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>100</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>112</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>2296</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2327 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  favorite_count  retweet_count\n",
       "0     892420643555336193           33873           7034\n",
       "1     892177421306343426           29384           5309\n",
       "2     891815181378084864           22099           3490\n",
       "3     891689557279858688           36998           7243\n",
       "4     891327558926688256           35376           7784\n",
       "...                  ...             ...            ...\n",
       "2322  666049248165822465              89             37\n",
       "2323  666044226329800704             247            115\n",
       "2324  666033412701032449             100             36\n",
       "2325  666029285002620928             112             39\n",
       "2326  666020888022790149            2296            423\n",
       "\n",
       "[2327 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are less entries in this table (2327) than in the archive table (2356)\n",
    "# this is probably because some tweets were unreachable via Twitter's API\n",
    "fav_rt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### `archive` table:\n",
    "- `source` column includes the HTML tag (validity issue)\n",
    "- There are some invalid entries in the `name` column, like 'a' or 'None' (instead of null) (accuracy issue)\n",
    "- The columns holding the information on dog stages are inconsistent (apparently 'None' was used instead of NaN). Investigate some more in the programmatic assessment! (validity issue)\n",
    "\n",
    "##### `fav_rt` table:\n",
    "- There are less entries in this table (2327) than in the `archive` table (2356) (completeness issue)\n",
    "\n",
    "### Programmatic Assessment\n",
    "\n",
    "### Quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2356 non-null   int64  \n",
      " 1   in_reply_to_status_id       78 non-null     float64\n",
      " 2   in_reply_to_user_id         78 non-null     float64\n",
      " 3   timestamp                   2356 non-null   object \n",
      " 4   source                      2356 non-null   object \n",
      " 5   text                        2356 non-null   object \n",
      " 6   retweeted_status_id         181 non-null    float64\n",
      " 7   retweeted_status_user_id    181 non-null    float64\n",
      " 8   retweeted_status_timestamp  181 non-null    object \n",
      " 9   expanded_urls               2297 non-null   object \n",
      " 10  rating_numerator            2356 non-null   int64  \n",
      " 11  rating_denominator          2356 non-null   int64  \n",
      " 12  name                        2356 non-null   object \n",
      " 13  doggo                       2356 non-null   object \n",
      " 14  floofer                     2356 non-null   object \n",
      " 15  pupper                      2356 non-null   object \n",
      " 16  puppo                       2356 non-null   object \n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicates\n",
    "sum(archive.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicates\n",
    "sum(fav_rt.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to tweepy's [documentation](https://docs.tweepy.org/en/latest/api.html#tweepy.API.get_status), the parameter `in_reply_to_status_id` is the ID of an existing status (tweet) that the update is in reply to.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
       "      <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>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",
       "  </tbody>\n",
       "</table>\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",
       "\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",
       "\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",
       "\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",
       "\n",
       "    retweeted_status_user_id retweeted_status_timestamp expanded_urls  \\\n",
       "30                       NaN                        NaN           NaN   \n",
       "55                       NaN                        NaN           NaN   \n",
       "64                       NaN                        NaN           NaN   \n",
       "\n",
       "    rating_numerator  rating_denominator  name doggo floofer pupper puppo  \n",
       "30                12                  10  None  None    None   None  None  \n",
       "55                17                  10  None  None    None   None  None  \n",
       "64                14                  10  None  None    None   None  None  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are only 78 entries with this column populated\n",
    "# what do they look like?\n",
    "archive[archive['in_reply_to_status_id'].notna()].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# while this column should be an int, like tweet_id, it's currently a float\n",
    "archive['in_reply_to_status_id'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the same goes for this column\n",
    "archive['in_reply_to_user_id'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# timestamp should be datetime \n",
    "archive['timestamp'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking a closer look to the structure of the source column\n",
    "# instead of the HTML tag, use only the text for the source\n",
    "# in this case, for instance, \"Twitter for iPhone\"\n",
    "archive.at[1, 'source']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV\""
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking a closer look to the structure of the text column\n",
    "# because of the string quotation marks, the url link breaks\n",
    "# if one tries to click on it\n",
    "archive.at[1, 'text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# while this column should be an int, like tweet_id, it's currently a float\n",
    "archive['retweeted_status_id'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# while this column should be an int, like tweet_id, it's currently a float\n",
    "archive['retweeted_status_user_id'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# timestamp should be datetime \n",
    "archive['retweeted_status_timestamp'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating_numerator\n",
       "0         2\n",
       "1         9\n",
       "2         9\n",
       "3        19\n",
       "4        17\n",
       "5        37\n",
       "6        32\n",
       "7        55\n",
       "8       102\n",
       "9       158\n",
       "10      461\n",
       "11      464\n",
       "12      558\n",
       "13      351\n",
       "14       54\n",
       "15        2\n",
       "17        1\n",
       "20        1\n",
       "24        1\n",
       "26        1\n",
       "27        1\n",
       "44        1\n",
       "45        1\n",
       "50        1\n",
       "60        1\n",
       "75        2\n",
       "80        1\n",
       "84        1\n",
       "88        1\n",
       "99        1\n",
       "121       1\n",
       "143       1\n",
       "144       1\n",
       "165       1\n",
       "182       1\n",
       "204       1\n",
       "420       2\n",
       "666       1\n",
       "960       1\n",
       "1776      1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it seems worth checking the outliers\n",
    "archive.groupby('rating_numerator')['rating_numerator'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>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>979</th>\n",
       "      <td>749981277374128128</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-04 15:00:45 +0000</td>\n",
       "      <td>&lt;a href=\"https://about.twitter.com/products/tw...</td>\n",
       "      <td>This is Atticus. He's quite simply America af....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/749981277...</td>\n",
       "      <td>1776</td>\n",
       "      <td>10</td>\n",
       "      <td>Atticus</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",
       "979  749981277374128128                    NaN                  NaN   \n",
       "\n",
       "                     timestamp  \\\n",
       "979  2016-07-04 15:00:45 +0000   \n",
       "\n",
       "                                                source  \\\n",
       "979  <a href=\"https://about.twitter.com/products/tw...   \n",
       "\n",
       "                                                  text  retweeted_status_id  \\\n",
       "979  This is Atticus. He's quite simply America af....                  NaN   \n",
       "\n",
       "     retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "979                       NaN                        NaN   \n",
       "\n",
       "                                         expanded_urls  rating_numerator  \\\n",
       "979  https://twitter.com/dog_rates/status/749981277...              1776   \n",
       "\n",
       "     rating_denominator     name doggo floofer pupper puppo  \n",
       "979                  10  Atticus  None    None   None  None  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the highest rating numerator\n",
    "archive.query('rating_numerator == 1776')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\"This is Atticus. He's quite simply America af. 1776/10 https://t.co/GRXwMxLBkh\",\n",
       " 'https://twitter.com/dog_rates/status/749981277374128128/photo/1')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in this case, 1776 is a play with the year the United States' \n",
    "# issued its Declaration of Independence \n",
    "archive.at[979, 'text'], archive.at[979, 'expanded_urls']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>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>1120</th>\n",
       "      <td>731156023742988288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-05-13 16:15:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Say hello to this unbelievably well behaved sq...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/731156023...</td>\n",
       "      <td>204</td>\n",
       "      <td>170</td>\n",
       "      <td>this</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",
       "1120  731156023742988288                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "1120  2016-05-13 16:15:54 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "1120  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "1120  Say hello to this unbelievably well behaved sq...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "1120                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "1120  https://twitter.com/dog_rates/status/731156023...               204   \n",
       "\n",
       "      rating_denominator  name doggo floofer pupper puppo  \n",
       "1120                 170  this  None    None   None  None  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking another outlier just to make sure they're ok\n",
    "archive.query('rating_numerator == 204')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Say hello to this unbelievably well behaved squad of doggos. 204/170 would try to pet all at once https://t.co/yGQI3He3xv',\n",
       " 'https://twitter.com/dog_rates/status/731156023742988288/photo/1')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are 17 dogs in this pic, so 204 is the sum of their ratings\n",
    "archive.at[1120, 'text'], archive.at[1120, 'expanded_urls']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating_denominator\n",
       "0         1\n",
       "2         1\n",
       "7         1\n",
       "10     2333\n",
       "11        3\n",
       "15        1\n",
       "16        1\n",
       "20        2\n",
       "40        1\n",
       "50        3\n",
       "70        1\n",
       "80        2\n",
       "90        1\n",
       "110       1\n",
       "120       1\n",
       "130       1\n",
       "150       1\n",
       "170       1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it seems worth checking the outliers\n",
    "archive.groupby('rating_denominator')['rating_numerator'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>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>1635</th>\n",
       "      <td>684222868335505415</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-05 04:00:18 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Someone help the girl is being mugged. Several...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/684222868...</td>\n",
       "      <td>121</td>\n",
       "      <td>110</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "1635  684222868335505415                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "1635  2016-01-05 04:00:18 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "1635  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "1635  Someone help the girl is being mugged. Several...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "1635                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "1635  https://twitter.com/dog_rates/status/684222868...               121   \n",
       "\n",
       "      rating_denominator  name doggo floofer pupper puppo  \n",
       "1635                 110  None  None    None   None  None  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the outliers\n",
    "archive.query('rating_denominator == 110')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Someone help the girl is being mugged. Several are distracting her while two steal her shoes. Clever puppers 121/110 https://t.co/1zfnTJLt55',\n",
       " 'https://twitter.com/dog_rates/status/684222868335505415/photo/1')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# whenever there are more than one dog at the pic,\n",
    "# the denominator is multiplied by that quantity\n",
    "# for instance:\n",
    "archive.at[1635, 'text'], archive.at[1635, 'expanded_urls']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Phineas', 'Tilly', 'Archie', 'Darla', 'Franklin', 'None', 'Jax',\n",
       "       'Zoey', 'Cassie', 'Koda', 'Bruno', 'Ted', 'Stuart', 'Oliver',\n",
       "       'Jim', 'Zeke', 'Ralphus', 'Canela', 'Gerald', 'Jeffrey', 'such',\n",
       "       'Maya', 'Mingus', 'Derek', 'Roscoe', 'Waffles', 'Jimbo', 'Maisey',\n",
       "       'Lilly', 'Earl', 'Lola', 'Kevin', 'Yogi', 'Noah', 'Bella',\n",
       "       'Grizzwald', 'Rusty', 'Gus', 'Stanley', 'Alfy', 'Koko', 'Rey',\n",
       "       'Gary', 'a', 'Elliot', 'Louis', 'Jesse', 'Romeo', 'Bailey',\n",
       "       'Duddles', 'Jack', 'Emmy', 'Steven', 'Beau', 'Snoopy', 'Shadow',\n",
       "       'Terrance', 'Aja', 'Penny', 'Dante', 'Nelly', 'Ginger', 'Benedict',\n",
       "       'Venti', 'Goose', 'Nugget', 'Cash', 'Coco', 'Jed', 'Sebastian',\n",
       "       'Walter', 'Sierra', 'Monkey', 'Harry', 'Kody', 'Lassie', 'Rover',\n",
       "       'Napolean', 'Dawn', 'Boomer', 'Cody', 'Rumble', 'Clifford',\n",
       "       'quite', 'Dewey', 'Scout', 'Gizmo', 'Cooper', 'Harold', 'Shikha',\n",
       "       'Jamesy', 'Lili', 'Sammy', 'Meatball', 'Paisley', 'Albus',\n",
       "       'Neptune', 'Quinn', 'Belle', 'Zooey', 'Dave', 'Jersey', 'Hobbes',\n",
       "       'Burt', 'Lorenzo', 'Carl', 'Jordy', 'Milky', 'Trooper', 'Winston',\n",
       "       'Sophie', 'Wyatt', 'Rosie', 'Thor', 'Oscar', 'Luna', 'Callie',\n",
       "       'Cermet', 'George', 'Marlee', 'Arya', 'Einstein', 'Alice',\n",
       "       'Rumpole', 'Benny', 'Aspen', 'Jarod', 'Wiggles', 'General',\n",
       "       'Sailor', 'Astrid', 'Iggy', 'Snoop', 'Kyle', 'Leo', 'Riley',\n",
       "       'Gidget', 'Noosh', 'Odin', 'Jerry', 'Charlie', 'Georgie', 'Rontu',\n",
       "       'Cannon', 'Furzey', 'Daisy', 'Tuck', 'Barney', 'Vixen', 'Jarvis',\n",
       "       'Mimosa', 'Pickles', 'Bungalo', 'Brady', 'Margo', 'Sadie', 'Hank',\n",
       "       'Tycho', 'Stephan', 'Indie', 'Winnie', 'Bentley', 'Ken', 'Max',\n",
       "       'Maddie', 'Pipsy', 'Monty', 'Sojourner', 'Odie', 'Arlo', 'Sunny',\n",
       "       'Vincent', 'Lucy', 'Clark', 'Mookie', 'Meera', 'Buddy', 'Ava',\n",
       "       'Rory', 'Eli', 'Ash', 'Tucker', 'Tobi', 'Chester', 'Wilson',\n",
       "       'Sunshine', 'Lipton', 'Gabby', 'Bronte', 'Poppy', 'Rhino',\n",
       "       'Willow', 'not', 'Orion', 'Eevee', 'Smiley', 'Logan', 'Moreton',\n",
       "       'Klein', 'Miguel', 'Emanuel', 'Kuyu', 'Dutch', 'Pete', 'Scooter',\n",
       "       'Reggie', 'Kyro', 'Samson', 'Loki', 'Mia', 'Malcolm', 'Dexter',\n",
       "       'Alfie', 'Fiona', 'one', 'Mutt', 'Bear', 'Doobert', 'Beebop',\n",
       "       'Alexander', 'Sailer', 'Brutus', 'Kona', 'Boots', 'Ralphie',\n",
       "       'Phil', 'Cupid', 'Pawnd', 'Pilot', 'Ike', 'Mo', 'Toby', 'Sweet',\n",
       "       'Pablo', 'Nala', 'Balto', 'Crawford', 'Gabe', 'Mattie', 'Jimison',\n",
       "       'Hercules', 'Duchess', 'Harlso', 'Sampson', 'Sundance', 'Luca',\n",
       "       'Flash', 'Finn', 'Peaches', 'Howie', 'Jazzy', 'Anna', 'Bo',\n",
       "       'Seamus', 'Wafer', 'Chelsea', 'Tom', 'Moose', 'Florence', 'Autumn',\n",
       "       'Dido', 'Eugene', 'Herschel', 'Strudel', 'Tebow', 'Chloe', 'Betty',\n",
       "       'Timber', 'Binky', 'Dudley', 'Comet', 'Larry', 'Levi', 'Akumi',\n",
       "       'Titan', 'Olivia', 'Alf', 'Oshie', 'Bruce', 'Chubbs', 'Sky',\n",
       "       'Atlas', 'Eleanor', 'Layla', 'Rocky', 'Baron', 'Tyr', 'Bauer',\n",
       "       'Swagger', 'Brandi', 'Mary', 'Moe', 'Halo', 'Augie', 'Craig',\n",
       "       'Sam', 'Hunter', 'Pavlov', 'Maximus', 'Wallace', 'Ito', 'Milo',\n",
       "       'Ollie', 'Cali', 'Lennon', 'incredibly', 'Major', 'Duke',\n",
       "       'Reginald', 'Sansa', 'Shooter', 'Django', 'Diogi', 'Sonny',\n",
       "       'Philbert', 'Marley', 'Severus', 'Ronnie', 'Anakin', 'Bones',\n",
       "       'Mauve', 'Chef', 'Doc', 'Sobe', 'Longfellow', 'Mister', 'Iroh',\n",
       "       'Baloo', 'Stubert', 'Paull', 'Timison', 'Davey', 'Pancake',\n",
       "       'Tyrone', 'Snicku', 'Ruby', 'Brody', 'Rizzy', 'Mack', 'Butter',\n",
       "       'Nimbus', 'Laika', 'Dobby', 'Juno', 'Maude', 'Lily', 'Newt',\n",
       "       'Benji', 'Nida', 'Robin', 'Monster', 'BeBe', 'Remus', 'Mabel',\n",
       "       'Misty', 'Happy', 'Mosby', 'Maggie', 'Leela', 'Ralphy', 'Brownie',\n",
       "       'Meyer', 'Stella', 'mad', 'Frank', 'Tonks', 'Lincoln', 'Oakley',\n",
       "       'Dale', 'Rizzo', 'Arnie', 'Pinot', 'Dallas', 'Hero', 'Frankie',\n",
       "       'Stormy', 'Mairi', 'Loomis', 'Godi', 'Kenny', 'Deacon', 'Timmy',\n",
       "       'Harper', 'Chipson', 'Combo', 'Dash', 'Bell', 'Hurley', 'Jay',\n",
       "       'Mya', 'Strider', 'an', 'Wesley', 'Solomon', 'Huck', 'very', 'O',\n",
       "       'Blue', 'Finley', 'Sprinkles', 'Heinrich', 'Shakespeare', 'Fizz',\n",
       "       'Chip', 'Grey', 'Roosevelt', 'Gromit', 'Willem', 'Dakota', 'Dixie',\n",
       "       'Al', 'Jackson', 'just', 'Carbon', 'DonDon', 'Kirby', 'Lou',\n",
       "       'Nollie', 'Chevy', 'Tito', 'Louie', 'Rupert', 'Rufus', 'Brudge',\n",
       "       'Shadoe', 'Colby', 'Angel', 'Brat', 'Tove', 'my', 'Aubie', 'Kota',\n",
       "       'Eve', 'Glenn', 'Shelby', 'Sephie', 'Bonaparte', 'Albert',\n",
       "       'Wishes', 'Rose', 'Theo', 'Rocco', 'Fido', 'Emma', 'Spencer',\n",
       "       'Lilli', 'Boston', 'Brandonald', 'Corey', 'Leonard', 'Chompsky',\n",
       "       'Beckham', 'Devón', 'Gert', 'Watson', 'Rubio', 'Keith', 'Dex',\n",
       "       'Carly', 'Ace', 'Tayzie', 'Grizzie', 'Fred', 'Gilbert', 'Zoe',\n",
       "       'Stewie', 'Calvin', 'Lilah', 'Spanky', 'Jameson', 'Piper',\n",
       "       'Atticus', 'Blu', 'Dietrich', 'Divine', 'Tripp', 'his', 'Cora',\n",
       "       'Huxley', 'Keurig', 'Bookstore', 'Linus', 'Abby', 'Shaggy',\n",
       "       'Shiloh', 'Gustav', 'Arlen', 'Percy', 'Lenox', 'Sugar', 'Harvey',\n",
       "       'Blanket', 'actually', 'Geno', 'Stark', 'Beya', 'Kilo', 'Kayla',\n",
       "       'Maxaroni', 'Doug', 'Edmund', 'Aqua', 'Theodore', 'Chase',\n",
       "       'getting', 'Rorie', 'Simba', 'Charles', 'Bayley', 'Axel',\n",
       "       'Storkson', 'Remy', 'Chadrick', 'Kellogg', 'Buckley', 'Livvie',\n",
       "       'Terry', 'Hermione', 'Ralpher', 'Aldrick', 'this', 'unacceptable',\n",
       "       'Rooney', 'Crystal', 'Ziva', 'Stefan', 'Pupcasso', 'Puff',\n",
       "       'Flurpson', 'Coleman', 'Enchilada', 'Raymond', 'all', 'Rueben',\n",
       "       'Cilantro', 'Karll', 'Sprout', 'Blitz', 'Bloop', 'Lillie',\n",
       "       'Ashleigh', 'Kreggory', 'Sarge', 'Luther', 'Ivar', 'Jangle',\n",
       "       'Schnitzel', 'Panda', 'Berkeley', 'Ralphé', 'Charleson', 'Clyde',\n",
       "       'Harnold', 'Sid', 'Pippa', 'Otis', 'Carper', 'Bowie',\n",
       "       'Alexanderson', 'Suki', 'Barclay', 'Skittle', 'Ebby', 'Flávio',\n",
       "       'Smokey', 'Link', 'Jennifur', 'Ozzy', 'Bluebert', 'Stephanus',\n",
       "       'Bubbles', 'old', 'Zeus', 'Bertson', 'Nico', 'Michelangelope',\n",
       "       'Siba', 'Calbert', 'Curtis', 'Travis', 'Thumas', 'Kanu', 'Lance',\n",
       "       'Opie', 'Kane', 'Olive', 'Chuckles', 'Staniel', 'Sora', 'Beemo',\n",
       "       'Gunner', 'infuriating', 'Lacy', 'Tater', 'Olaf', 'Cecil', 'Vince',\n",
       "       'Karma', 'Billy', 'Walker', 'Rodney', 'Klevin', 'Malikai',\n",
       "       'Bobble', 'River', 'Jebberson', 'Remington', 'Farfle', 'Jiminus',\n",
       "       'Clarkus', 'Finnegus', 'Cupcake', 'Kathmandu', 'Ellie', 'Katie',\n",
       "       'Kara', 'Adele', 'Zara', 'Ambrose', 'Jimothy', 'Bode', 'Terrenth',\n",
       "       'Reese', 'Chesterson', 'Lucia', 'Bisquick', 'Ralphson', 'Socks',\n",
       "       'Rambo', 'Rudy', 'Fiji', 'Rilo', 'Bilbo', 'Coopson', 'Yoda',\n",
       "       'Millie', 'Chet', 'Crouton', 'Daniel', 'Kaia', 'Murphy', 'Dotsy',\n",
       "       'Eazy', 'Coops', 'Fillup', 'Miley', 'Charl', 'Reagan', 'Yukon',\n",
       "       'CeCe', 'Cuddles', 'Claude', 'Jessiga', 'Carter', 'Ole', 'Pherb',\n",
       "       'Blipson', 'Reptar', 'Trevith', 'Berb', 'Bob', 'Colin', 'Brian',\n",
       "       'Oliviér', 'Grady', 'Kobe', 'Freddery', 'Bodie', 'Dunkin', 'Wally',\n",
       "       'Tupawc', 'Amber', 'Edgar', 'Teddy', 'Kingsley', 'Brockly',\n",
       "       'Richie', 'Molly', 'Vinscent', 'Cedrick', 'Hazel', 'Lolo', 'Eriq',\n",
       "       'Phred', 'the', 'Oddie', 'Maxwell', 'Geoff', 'Covach', 'Durg',\n",
       "       'Fynn', 'Ricky', 'Herald', 'Lucky', 'Ferg', 'Trip', 'Clarence',\n",
       "       'Hamrick', 'Brad', 'Pubert', 'Frönq', 'Derby', 'Lizzie', 'Ember',\n",
       "       'Blakely', 'Opal', 'Marq', 'Kramer', 'Barry', 'Gordon', 'Baxter',\n",
       "       'Mona', 'Horace', 'Crimson', 'Birf', 'Hammond', 'Lorelei', 'Marty',\n",
       "       'Brooks', 'Petrick', 'Hubertson', 'Gerbald', 'Oreo', 'Bruiser',\n",
       "       'Perry', 'Bobby', 'Jeph', 'Obi', 'Tino', 'Kulet', 'Sweets', 'Lupe',\n",
       "       'Tiger', 'Jiminy', 'Griffin', 'Banjo', 'Brandy', 'Lulu', 'Darrel',\n",
       "       'Taco', 'Joey', 'Patrick', 'Kreg', 'Todo', 'Tess', 'Ulysses',\n",
       "       'Toffee', 'Apollo', 'Asher', 'Glacier', 'Chuck', 'Champ', 'Ozzie',\n",
       "       'Griswold', 'Cheesy', 'Moofasa', 'Hector', 'Goliath', 'Kawhi',\n",
       "       'by', 'Emmie', 'Penelope', 'Willie', 'Rinna', 'Mike', 'William',\n",
       "       'Dwight', 'Evy', 'officially', 'Rascal', 'Linda', 'Tug', 'Tango',\n",
       "       'Grizz', 'Jerome', 'Crumpet', 'Jessifer', 'Izzy', 'Ralph', 'Sandy',\n",
       "       'Humphrey', 'Tassy', 'Juckson', 'Chuq', 'Tyrus', 'Karl',\n",
       "       'Godzilla', 'Vinnie', 'Kenneth', 'Herm', 'Bert', 'Striker',\n",
       "       'Donny', 'Pepper', 'Bernie', 'Buddah', 'Lenny', 'Arnold', 'Zuzu',\n",
       "       'Mollie', 'Laela', 'Tedders', 'Superpup', 'Rufio', 'Jeb', 'Rodman',\n",
       "       'Jonah', 'Chesney', 'life', 'Henry', 'Bobbay', 'Mitch', 'Kaiya',\n",
       "       'Acro', 'Aiden', 'Obie', 'Dot', 'Shnuggles', 'Kendall', 'Jeffri',\n",
       "       'Steve', 'Mac', 'Fletcher', 'Kenzie', 'Pumpkin', 'Schnozz',\n",
       "       'Gustaf', 'Cheryl', 'Ed', 'Leonidas', 'Norman', 'Caryl', 'Scott',\n",
       "       'Taz', 'Darby', 'Jackie', 'light', 'Jazz', 'Franq', 'Pippin',\n",
       "       'Rolf', 'Snickers', 'Ridley', 'Cal', 'Bradley', 'Bubba', 'Tuco',\n",
       "       'Patch', 'Mojo', 'Batdog', 'Dylan', 'space', 'Mark', 'JD',\n",
       "       'Alejandro', 'Scruffers', 'Pip', 'Julius', 'Tanner', 'Sparky',\n",
       "       'Anthony', 'Holly', 'Jett', 'Amy', 'Sage', 'Andy', 'Mason',\n",
       "       'Trigger', 'Antony', 'Creg', 'Traviss', 'Gin', 'Jeffrie', 'Danny',\n",
       "       'Ester', 'Pluto', 'Bloo', 'Edd', 'Willy', 'Herb', 'Damon',\n",
       "       'Peanut', 'Nigel', 'Butters', 'Sandra', 'Fabio', 'Randall', 'Liam',\n",
       "       'Tommy', 'Ben', 'Raphael', 'Julio', 'Andru', 'Kloey', 'Shawwn',\n",
       "       'Skye', 'Kollin', 'Ronduh', 'Billl', 'Saydee', 'Dug', 'Tessa',\n",
       "       'Sully', 'Kirk', 'Ralf', 'Clarq', 'Jaspers', 'Samsom', 'Harrison',\n",
       "       'Chaz', 'Jeremy', 'Jaycob', 'Lambeau', 'Ruffles', 'Amélie', 'Bobb',\n",
       "       'Banditt', 'Kevon', 'Winifred', 'Hanz', 'Churlie', 'Zeek',\n",
       "       'Timofy', 'Maks', 'Jomathan', 'Kallie', 'Marvin', 'Spark',\n",
       "       'Gòrdón', 'Jo', 'DayZ', 'Jareld', 'Torque', 'Ron', 'Skittles',\n",
       "       'Cleopatricia', 'Erik', 'Stu', 'Tedrick', 'Filup', 'Kial',\n",
       "       'Naphaniel', 'Dook', 'Hall', 'Philippe', 'Biden', 'Fwed',\n",
       "       'Genevieve', 'Joshwa', 'Bradlay', 'Clybe', 'Keet', 'Carll',\n",
       "       'Jockson', 'Josep', 'Lugan', 'Christoper'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# investigating the name column\n",
    "# there seems to be some inaccurate entries like 'a',\n",
    "# 'such', 'by'\n",
    "archive['name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using a regular expression to find how many rows contain\n",
    "# invalid names\n",
    "archive[archive['name'].str.contains(r'^[a-z]')]['name'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "745"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# in the visual assessment I could see there were entries \n",
    "# listed as 'None' instead of proper 'NaN's\n",
    "archive.query('name == \"None\"')['name'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['None', 'doggo'], dtype=object),\n",
       " array(['None', 'pupper'], dtype=object),\n",
       " array(['None', 'puppo'], dtype=object),\n",
       " array(['None', 'floofer'], dtype=object))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# erroneous data type, string instead of bool\n",
    "archive['doggo'].unique(), archive['pupper'].unique(), archive['puppo'].unique(), archive['floofer'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2259, 2099, 2326, 2346]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[archive.query('doggo == \"None\"')['doggo'].count(), \n",
    "archive.query('pupper == \"None\"')['pupper'].count(), \n",
    "archive.query('puppo == \"None\"')['puppo'].count(),\n",
    "archive.query('floofer == \"None\"')['floofer'].count()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
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       "  </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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In the dog breed columns (p1, p2 and p3),\n",
    "# the dog breeds names are separated by '_'\n",
    "# instead of space ' ' \n",
    "# and not all of them are capitalized\n",
    "image_prediction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "image_prediction.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recap: Quality Issues\n",
    "##### `archive` table:\n",
    "- `in_reply_to_status_id`: erroneous datatype (float, should be int)\n",
    "- `in_reply_to_user_id`: erroneous datatype (float, should be int)\n",
    "- `timestamp`: erroneus datatype (object, should be datetime) \n",
    "- `source`: instead of the HTML tag, use just the text for the source\n",
    "- `retweeted_status_id`: erroneous datatype (float, should be int) \n",
    "- `retweeted_status_user_id`: erroneous datatype (float, should be int)\n",
    "- `retweeted_status_timestamp`: erroneus datatype (object, should be datetime)\n",
    "- `name`: there are 109 rows with invalid, lowercase words instead of names. Drop those rows\n",
    "- `name`: there are 705 rows in which a dog name wasn't listed but instead of a null value, there is a None string as an entry instead\n",
    "- `text`: because of the string quotation marks, the url link breaks if one tries to click on it\n",
    "- `doggo`: erroneous data type, string instead of bool\n",
    "- `pupper`: erroneous data type, string instead of bool\n",
    "- `puppo`: erroneous data type, string instead of bool\n",
    "- `floofer`: erroneous data type, string instead of bool\n",
    "- Retweets should be dropped\n",
    "\n",
    "\n",
    "##### `image_prediction` table:\n",
    "- `p1`, `p2`, `p3`: dog breeds are separated by '_' instead of spaces ' '\n",
    "- `p1`, `p2`, `p3`: some dog breed are not capitalized\n",
    "- The neural network apparently identified that some images are not from dogs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidiness:\n",
    "\n",
    "There are three requirements for tidy data:\n",
    "- Each variable forms a column\n",
    "- Each observation forms a row\n",
    "- Each type of observational unit forms a table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>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>19</th>\n",
       "      <td>888202515573088257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-21 01:02:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is Canela. She attempted s...</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-07-19 00:47:34 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887473957...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Canela</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>886054160059072513</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-15 02:45:48 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @Athletics: 12/10 #BATP https://t.co/WxwJmv...</td>\n",
       "      <td>8.860537e+17</td>\n",
       "      <td>1.960740e+07</td>\n",
       "      <td>2017-07-15 02:44:07 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886053434...</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>36</th>\n",
       "      <td>885311592912609280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-13 01:35:06 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @dog_rates: This is Lilly. She just paralle...</td>\n",
       "      <td>8.305833e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>2017-02-12 01:04:29 +0000</td>\n",
       "      <td>https://twitter.com/dog_rates/status/830583320...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Lilly</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",
       "19  888202515573088257                    NaN                  NaN   \n",
       "32  886054160059072513                    NaN                  NaN   \n",
       "36  885311592912609280                    NaN                  NaN   \n",
       "\n",
       "                    timestamp  \\\n",
       "19  2017-07-21 01:02:36 +0000   \n",
       "32  2017-07-15 02:45:48 +0000   \n",
       "36  2017-07-13 01:35:06 +0000   \n",
       "\n",
       "                                               source  \\\n",
       "19  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "32  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "36  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                 text  retweeted_status_id  \\\n",
       "19  RT @dog_rates: This is Canela. She attempted s...         8.874740e+17   \n",
       "32  RT @Athletics: 12/10 #BATP https://t.co/WxwJmv...         8.860537e+17   \n",
       "36  RT @dog_rates: This is Lilly. She just paralle...         8.305833e+17   \n",
       "\n",
       "    retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "19              4.196984e+09  2017-07-19 00:47:34 +0000   \n",
       "32              1.960740e+07  2017-07-15 02:44:07 +0000   \n",
       "36              4.196984e+09  2017-02-12 01:04:29 +0000   \n",
       "\n",
       "                                        expanded_urls  rating_numerator  \\\n",
       "19  https://twitter.com/dog_rates/status/887473957...                13   \n",
       "32  https://twitter.com/dog_rates/status/886053434...                12   \n",
       "36  https://twitter.com/dog_rates/status/830583320...                13   \n",
       "\n",
       "    rating_denominator    name doggo floofer pupper puppo  \n",
       "19                  10  Canela  None    None   None  None  \n",
       "32                  10    None  None    None   None  None  \n",
       "36                  10   Lilly  None    None   None  None  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are only 181 entries with this column populated\n",
    "# what do they look like?\n",
    "archive[archive['retweeted_status_id'].notna()].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recap: tidiness issues\n",
    "- Four variables in one column in the `archive` table (`dog_stage`, i.e., doggo, floofer, pupper, and puppo)\n",
    "- The dataset could be tidily represented with the columns `favorite_count` and `retweet_count` in the `archive` table\n",
    "- The dataset could be tidily represented joining the `image_prediction` table into the `archive` table to create one master dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compiled list of issues\n",
    "\n",
    "### Quality:\n",
    "\n",
    "##### `archive` table:\n",
    "1. `source` column includes the HTML tag\n",
    "2. Erroneus datatypes (`timestamp`, `in_reply_to_status_id`, `in_reply_to_user_id`, `retweeted_status_user_id`, `retweeted_status_id`, `retweeted_status_timestamp`)\n",
    "3. `name` column has 109 rows with invalid, lower case words\n",
    "4. `name` column has 705 rows in which a dog name wasn't listed but instead of a null value, there is a None string as an entry instead\n",
    "5. In the `text` column, because of the string quotation marks, the url link breaks if one tries to click on it\n",
    "6. `doggo`, `pupper`, `puppo`, `floofer`: columns have rows in which a dog stage wasn't listed but instead of a null value, there is a None string as an entry instead\n",
    "7. `doggo`, `pupper`, `puppo`, `floofer`: erroneous data type, string instead of bool\n",
    "8. Retweets should be dropped\n",
    "\n",
    "##### `image_prediction` table:\n",
    "\n",
    "9. `p1`, `p2`, `p3`: dog breeds are separated by '_' instead of spaces ' '\n",
    "10. `p1`, `p2`, `p3`: some dog breed are not capitalized\n",
    "11. The neural network apparently identified that some images are not from dogs\n",
    "\n",
    "### Tidiness:\n",
    "1. The dataset could be tidily represented with the columns `favorite_count` and `retweet_count` in the `archive` table\n",
    "2. Four variables in one column in the `archive` table (`dog_stage`, i.e., doggo, floofer, pupper, and puppo)\n",
    "3. The dataset could be tidily represented joining the `image_prediction` table into the `archive` table to create one master dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 32,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "---\n",
    "## Cleaning Data\n",
    "In this section, clean **all** of the issues you documented while assessing. \n",
    "\n",
    "**Note:** Make a copy of the original data before cleaning. Cleaning includes merging individual pieces of data according to the rules of [tidy data](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html). The result should be a high-quality and tidy master pandas DataFrame (or DataFrames, if appropriate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_copy = archive.copy()\n",
    "fav_rt_copy = fav_rt.copy()\n",
    "image_prediction_copy = image_prediction.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Address Tidiness:\n",
    "\n",
    "1. The dataset could be tidily represented with the columns `favorite_count` and `retweet_count` in the `archive` table\n",
    "2. Four variables in one column in the `archive` table (`dog_stage`, i.e., doggo, floofer, pupper, and puppo)\n",
    "3. The dataset could be tidily represented joining the `image_prediction` table into the `archive` table to create one master dataset\n",
    "\n",
    "### Tidiness Issue #1:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: \n",
    "\n",
    "Use [`df.merge`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html) to merge the tables `archive_copy` and `fav_rt_copy`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# right joining the tables will ensure that the info on\n",
    "# those 29 tweets that were unreachable via the API \n",
    "# will be dropped from the archive_copy df\n",
    "archive_copy = archive_copy.merge(fav_rt_copy, how='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>674042553264685056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-08 01:47:22 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Yea I can't handle the cuteness anymore. Curls...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/674042553...</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",
       "      <td>638</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "1900  674042553264685056                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "1900  2015-12-08 01:47:22 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "1900  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "1900  Yea I can't handle the cuteness anymore. Curls...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "1900                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "1900  https://twitter.com/dog_rates/status/674042553...                12   \n",
       "\n",
       "      rating_denominator  name doggo floofer pupper puppo  favorite_count  \\\n",
       "1900                  10  None  None    None   None  None             638   \n",
       "\n",
       "      retweet_count  \n",
       "1900            111  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking to see if it worked\n",
    "archive_copy.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2327 entries, 0 to 2326\n",
      "Data columns (total 19 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   tweet_id                    2327 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                   2327 non-null   object \n",
      " 4   source                      2327 non-null   object \n",
      " 5   text                        2327 non-null   object \n",
      " 6   retweeted_status_id         160 non-null    float64\n",
      " 7   retweeted_status_user_id    160 non-null    float64\n",
      " 8   retweeted_status_timestamp  160 non-null    object \n",
      " 9   expanded_urls               2269 non-null   object \n",
      " 10  rating_numerator            2327 non-null   int64  \n",
      " 11  rating_denominator          2327 non-null   int64  \n",
      " 12  name                        2327 non-null   object \n",
      " 13  doggo                       2327 non-null   object \n",
      " 14  floofer                     2327 non-null   object \n",
      " 15  pupper                      2327 non-null   object \n",
      " 16  puppo                       2327 non-null   object \n",
      " 17  favorite_count              2327 non-null   int64  \n",
      " 18  retweet_count               2327 non-null   int64  \n",
      "dtypes: float64(4), int64(5), object(10)\n",
      "memory usage: 363.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking to see if it worked\n",
    "archive_copy.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidiness Issue #2:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "#### Define: \n",
    "\n",
    "Create a function to extract the dog stage from each of the four dog stage columns (i.e., `doggo`, `puppo`, `pupper`, and `floofer`). Then, use [`df.apply`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html) to apply the function and create the `dog_stage` column. \n",
    "\n",
    "#### Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: I couldn't make `pd.melt` work, since there are many rows without any classification for dog stage at all. Dropping them would mean losing a huge chunk of the dataset that is perfectly valid for other analises. Instead, I found help at [this discussion](https://knowledge.udacity.com/questions/80210) from the Udacity forum. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# id_vars = ['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', 'favorite_count', 'retweet_count']\n",
    "# value_vars = ['doggo', 'floofer', 'pupper', 'puppo']\n",
    "# var_name = 'to_drop'\n",
    "# value_name = 'dog_stage'\n",
    "# archive_copy = pd.melt(archive_copy, id_vars=id_vars, value_vars=value_vars, var_name=var_name, value_name=value_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create function to extract the dog stage from the four columns\n",
    "def select_dog_stage(df):\n",
    "    if df['doggo'] == 'doggo':\n",
    "        return 'doggo'\n",
    "    elif df['floofer'] == 'floofer':\n",
    "        return 'floofer'\n",
    "    elif df['pupper'] == 'pupper':\n",
    "        return 'pupper'\n",
    "    elif df['puppo'] == 'puppo':\n",
    "        return 'puppo'\n",
    "    else:\n",
    "        return pd.NA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply function\n",
    "archive_copy['dog_stage'] = archive_copy.apply(lambda x: select_dog_stage(x), axis=1)\n",
    "\n",
    "# drop columns since they won't be useful anymore\n",
    "archive_copy.drop(['doggo', 'floofer', 'pupper', 'puppo'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<NA>, 'doggo', 'puppo', 'pupper', 'floofer'], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if the column has the expected values\n",
    "# for the dog stage\n",
    "archive_copy['dog_stage'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 2327 entries, 0 to 2326\n",
      "Series name: dog_stage\n",
      "Non-Null Count  Dtype \n",
      "--------------  ----- \n",
      "375 non-null    object\n",
      "dtypes: object(1)\n",
      "memory usage: 36.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# check to see if it worked\n",
    "archive_copy['dog_stage'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>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>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>890240255349198849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-26 15:59:51 +0000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>28002</td>\n",
       "      <td>6117</td>\n",
       "      <td>doggo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>889665388333682689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-25 01:55:32 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here's a puppo that seems to be on the fence a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889665388...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>42129</td>\n",
       "      <td>8371</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>889531135344209921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-24 17:02:04 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Stuart. He's sporting his favorite fan...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889531135...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Stuart</td>\n",
       "      <td>13372</td>\n",
       "      <td>1885</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "9   890240255349198849                    NaN                  NaN   \n",
       "12  889665388333682689                    NaN                  NaN   \n",
       "14  889531135344209921                    NaN                  NaN   \n",
       "\n",
       "                    timestamp  \\\n",
       "9   2017-07-26 15:59:51 +0000   \n",
       "12  2017-07-25 01:55:32 +0000   \n",
       "14  2017-07-24 17:02:04 +0000   \n",
       "\n",
       "                                               source  \\\n",
       "9   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "12  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "14  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                 text  retweeted_status_id  \\\n",
       "9   This is Cassie. She is a college pup. Studying...                  NaN   \n",
       "12  Here's a puppo that seems to be on the fence a...                  NaN   \n",
       "14  This is Stuart. He's sporting his favorite fan...                  NaN   \n",
       "\n",
       "    retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "9                        NaN                        NaN   \n",
       "12                       NaN                        NaN   \n",
       "14                       NaN                        NaN   \n",
       "\n",
       "                                        expanded_urls  rating_numerator  \\\n",
       "9   https://twitter.com/dog_rates/status/890240255...                14   \n",
       "12  https://twitter.com/dog_rates/status/889665388...                13   \n",
       "14  https://twitter.com/dog_rates/status/889531135...                13   \n",
       "\n",
       "    rating_denominator    name  favorite_count  retweet_count dog_stage  \n",
       "9                   10  Cassie           28002           6117     doggo  \n",
       "12                  10    None           42129           8371     puppo  \n",
       "14                  10  Stuart           13372           1885     puppo  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to see if it worked\n",
    "archive_copy[archive_copy['dog_stage'].notna()].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Address Quality:\n",
    "\n",
    "##### `archive` table:\n",
    "1. `source` column includes the HTML tag\n",
    "2. Erroneus datatypes (`timestamp`, `in_reply_to_status_id`, `in_reply_to_user_id`, `retweeted_status_user_id`, `retweeted_status_id`, `retweeted_status_timestamp`)\n",
    "3. `name` column has 109 rows with invalid, lower case words\n",
    "4. `name` column has 705 rows in which a dog name wasn't listed but instead of a null value, there is a None string as an entry instead\n",
    "5. In the `text` column, because of the string quotation marks, the url link breaks if one tries to click on it\n",
    "6. `doggo`, `pupper`, `puppo`, `floofer`: columns have rows in which a dog stage wasn't listed but instead of a null value, there is a None string as an entry instead\n",
    "7. `doggo`, `pupper`, `puppo`, `floofer`: erroneous data type, string instead of bool\n",
    "8. Retweets should be dropped\n",
    "\n",
    "##### `image_prediction` table:\n",
    "\n",
    "9. `p1`, `p2`, `p3`: dog breeds are separated by '_' instead of spaces ' '\n",
    "10. `p1`, `p2`, `p3`: some dog breed are not capitalized\n",
    "\n",
    "### Quality Issue #1:\n",
    "\n",
    "#### Define: \n",
    "\n",
    "Use a regular expression to leave just the text for the HTML tag in the `source` column\n",
    "\n",
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check an entry just to see the structure of the column \n",
    "# to build to regex\n",
    "archive_copy.at[1, 'source']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use str.extract to get just the text for the tag\n",
    "archive_copy['source'] = archive_copy['source'].str.extract(r'>([A-Z].*[a-z])<')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Twitter for iPhone', 'Twitter Web Client', 'Vine - Make a Scene',\n",
       "       'TweetDeck'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to see if it worked\n",
    "archive_copy['source'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #2:\n",
    "\n",
    "#### Define: \n",
    "\n",
    "Deal with various columns in erroneous datatypes:\n",
    "\n",
    "- `timestamp`: use [`pd.datetime`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html) to convert the column from `object` to `datetime`.\n",
    "- `retweeted_status_timestamp`: use [`pd.datetime`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html) to convert the column from `object` to `datetime`.\n",
    "- `in_reply_to_status_id`: use [`pd.astype`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.astype.html) to convert column from `float64` to `Int64`.\n",
    "- `in_reply_to_user_id`: use [`pd.astype`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.astype.html) to convert column from `float64` to `Int64`.\n",
    "- `retweeted_status_user_id`: use [`pd.astype`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.astype.html) to convert column from `float64` to `Int64`.\n",
    "- `retweeted_status_id`: use [`pd.astype`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.astype.html) to convert column from `float64` to `Int64`.\n",
    "\n",
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(dtype('O'), dtype('O'))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# currently, they're both objects (strings)\n",
    "archive_copy['timestamp'].dtype, archive_copy['retweeted_status_timestamp'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert them to datetime\n",
    "archive_copy['timestamp'] = pd.to_datetime(archive_copy['timestamp'])\n",
    "archive_copy['retweeted_status_timestamp'] = pd.to_datetime(archive_copy['retweeted_status_timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[dtype('float64'), dtype('float64'), dtype('float64'), dtype('float64')]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# currently, they're all floats\n",
    "[archive_copy['in_reply_to_status_id'].dtype, \n",
    "archive_copy['in_reply_to_user_id'].dtype, \n",
    "archive_copy['retweeted_status_user_id'].dtype, \n",
    "archive_copy['retweeted_status_id'].dtype]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> [Note 1](https://stackoverflow.com/questions/39690742/convert-float-to-int-and-leave-nulls): Stack Overflow discussion on converting float to intergers leaving null values.\n",
    "\n",
    "> [Note 2](https://pandas.pydata.org/docs/user_guide/integer_na.html): on converting from float to interger if your series contains null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert from float to ints\n",
    "# use dtype = Int64\n",
    "# all NA-like values will be replaced with pandas.NA\n",
    "# otherwise, casting won't work\n",
    "archive_copy = archive_copy.astype({'in_reply_to_status_id': 'Int64', \n",
    "                    'in_reply_to_user_id': 'Int64',\n",
    "                    'retweeted_status_user_id': 'Int64',\n",
    "                    'retweeted_status_id': 'Int64'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime64[ns, UTC], datetime64[ns, UTC])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to see if it worked \n",
    "archive_copy['timestamp'].dtype, archive_copy['retweeted_status_timestamp'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Int64Dtype(), Int64Dtype(), Int64Dtype(), Int64Dtype()]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to see if it worked\n",
    "[archive_copy['in_reply_to_status_id'].dtype, \n",
    "archive_copy['in_reply_to_user_id'].dtype, \n",
    "archive_copy['retweeted_status_user_id'].dtype, \n",
    "archive_copy['retweeted_status_id'].dtype]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>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>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2017-08-01 16:23:56+00:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaT</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>33873</td>\n",
       "      <td>7034</td>\n",
       "      <td>&lt;NA&gt;</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                   <NA>                 <NA>   \n",
       "\n",
       "                  timestamp              source  \\\n",
       "0 2017-08-01 16:23:56+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                 <NA>   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                      <NA>                        NaT   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "\n",
       "   rating_denominator     name  favorite_count  retweet_count dog_stage  \n",
       "0                  10  Phineas           33873           7034      <NA>  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive_copy.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2327 entries, 0 to 2326\n",
      "Data columns (total 16 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2327 non-null   int64              \n",
      " 1   in_reply_to_status_id       78 non-null     Int64              \n",
      " 2   in_reply_to_user_id         78 non-null     Int64              \n",
      " 3   timestamp                   2327 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2327 non-null   object             \n",
      " 5   text                        2327 non-null   object             \n",
      " 6   retweeted_status_id         160 non-null    Int64              \n",
      " 7   retweeted_status_user_id    160 non-null    Int64              \n",
      " 8   retweeted_status_timestamp  160 non-null    datetime64[ns, UTC]\n",
      " 9   expanded_urls               2269 non-null   object             \n",
      " 10  rating_numerator            2327 non-null   int64              \n",
      " 11  rating_denominator          2327 non-null   int64              \n",
      " 12  name                        2327 non-null   object             \n",
      " 13  favorite_count              2327 non-null   int64              \n",
      " 14  retweet_count               2327 non-null   int64              \n",
      " 15  dog_stage                   375 non-null    object             \n",
      "dtypes: Int64(4), datetime64[ns, UTC](2), int64(5), object(5)\n",
      "memory usage: 382.7+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_copy.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #3:\n",
    "\n",
    "#### Define:\n",
    "\n",
    "Find the rows in which invalid, lowercase words were used in the `name` column. Change those to pd.NA. \n",
    "\n",
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 2327 entries, 0 to 2326\n",
      "Series name: name\n",
      "Non-Null Count  Dtype \n",
      "--------------  ----- \n",
      "2327 non-null   object\n",
      "dtypes: object(1)\n",
      "memory usage: 100.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# current situation of the column\n",
    "archive_copy['name'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get those indices\n",
    "indices = archive_copy[archive_copy['name'].str.contains(r'^[a-z]')]['name'].index\n",
    "\n",
    "# change them to pd.NA\n",
    "archive_copy.loc[indices, 'name'] = pd.NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 2327 entries, 0 to 2326\n",
      "Series name: name\n",
      "Non-Null Count  Dtype \n",
      "--------------  ----- \n",
      "2219 non-null   object\n",
      "dtypes: object(1)\n",
      "memory usage: 100.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# check to see if it worked \n",
    "archive_copy['name'].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #4: \n",
    "\n",
    "#### Define: \n",
    "\n",
    "Find the rows in which a dog name wasn't listed but instead of a null value, there is a `None` string as an entry instead. Change those to pd.NA.\n",
    "\n",
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = archive_copy.query('name == \"None\"').index\n",
    "\n",
    "# change them to pd.NA\n",
    "archive_copy.loc[indices, 'name'] = pd.NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 2327 entries, 0 to 2326\n",
      "Series name: name\n",
      "Non-Null Count  Dtype \n",
      "--------------  ----- \n",
      "1487 non-null   object\n",
      "dtypes: object(1)\n",
      "memory usage: 100.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# check to see if it worked \n",
    "archive_copy['name'].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #5:\n",
    "\n",
    "#### Define: \n",
    "\n",
    "For every entry in the `text` column, add a trailing space. This way, if you click on the link, it won't assume the quotation mark belongs to the url and it'll open just fine. \n",
    "\n",
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV\""
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the structure of the column\n",
    "archive_copy.at[1, 'text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "archive_copy['text'] = archive_copy['text'] + \" \""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV \""
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to see if it worked \n",
    "archive_copy.at[1, 'text']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issues #6 and #7\n",
    "\n",
    "6. `doggo`, `pupper`, `puppo`, `floofer`: columns have rows in which a dog stage wasn't listed but instead of a null value, there is a None string as an entry instead\n",
    "7. `doggo`, `pupper`, `puppo`, `floofer`: erroneous data type, string instead of bool\n",
    "\n",
    "In the Tidiness Issue #2, these columns were repurposed as a single `dog_stage` column and the null values were already converted to `pd.NA`. Therefore, both of these issues were already addressed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #8:\n",
    "\n",
    "#### Define: \n",
    "\n",
    "First, use [`pd.index`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.index.html) to get the indices for the rows representing retweets. Then, drop those rows. Finally, since those columns won't be useful anymore, drop them.\n",
    "\n",
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2327 entries, 0 to 2326\n",
      "Data columns (total 16 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2327 non-null   int64              \n",
      " 1   in_reply_to_status_id       78 non-null     Int64              \n",
      " 2   in_reply_to_user_id         78 non-null     Int64              \n",
      " 3   timestamp                   2327 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2327 non-null   object             \n",
      " 5   text                        2327 non-null   object             \n",
      " 6   retweeted_status_id         160 non-null    Int64              \n",
      " 7   retweeted_status_user_id    160 non-null    Int64              \n",
      " 8   retweeted_status_timestamp  160 non-null    datetime64[ns, UTC]\n",
      " 9   expanded_urls               2269 non-null   object             \n",
      " 10  rating_numerator            2327 non-null   int64              \n",
      " 11  rating_denominator          2327 non-null   int64              \n",
      " 12  name                        1487 non-null   object             \n",
      " 13  favorite_count              2327 non-null   int64              \n",
      " 14  retweet_count               2327 non-null   int64              \n",
      " 15  dog_stage                   375 non-null    object             \n",
      "dtypes: Int64(4), datetime64[ns, UTC](2), int64(5), object(5)\n",
      "memory usage: 382.7+ KB\n"
     ]
    }
   ],
   "source": [
    "archive_copy.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the indices from the retweets\n",
    "indices = archive_copy[archive_copy['retweeted_status_id'].notna()].index\n",
    "\n",
    "# drop them\n",
    "archive_copy = archive_copy.drop(indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2167 entries, 0 to 2326\n",
      "Data columns (total 16 columns):\n",
      " #   Column                      Non-Null Count  Dtype              \n",
      "---  ------                      --------------  -----              \n",
      " 0   tweet_id                    2167 non-null   int64              \n",
      " 1   in_reply_to_status_id       78 non-null     Int64              \n",
      " 2   in_reply_to_user_id         78 non-null     Int64              \n",
      " 3   timestamp                   2167 non-null   datetime64[ns, UTC]\n",
      " 4   source                      2167 non-null   object             \n",
      " 5   text                        2167 non-null   object             \n",
      " 6   retweeted_status_id         0 non-null      Int64              \n",
      " 7   retweeted_status_user_id    0 non-null      Int64              \n",
      " 8   retweeted_status_timestamp  0 non-null      datetime64[ns, UTC]\n",
      " 9   expanded_urls               2109 non-null   object             \n",
      " 10  rating_numerator            2167 non-null   int64              \n",
      " 11  rating_denominator          2167 non-null   int64              \n",
      " 12  name                        1384 non-null   object             \n",
      " 13  favorite_count              2167 non-null   int64              \n",
      " 14  retweet_count               2167 non-null   int64              \n",
      " 15  dog_stage                   343 non-null    object             \n",
      "dtypes: Int64(4), datetime64[ns, UTC](2), int64(5), object(5)\n",
      "memory usage: 296.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# check to see if it worked\n",
    "archive_copy.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the columns related to the retweets, since they \n",
    "# won't be used anymore\n",
    "archive_copy = archive_copy.drop(['retweeted_status_id', \n",
    "                                  'retweeted_status_user_id',\n",
    "                                  'retweeted_status_timestamp'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',\n",
       "       'source', 'text', 'expanded_urls', 'rating_numerator',\n",
       "       'rating_denominator', 'name', 'favorite_count', 'retweet_count',\n",
       "       'dog_stage'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to see if it worked\n",
    "archive_copy.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issues #9 and #10:\n",
    "\n",
    "#### Define:\n",
    "Use [`str.replace`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.replace.html) and [`str.title`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.title.html#pandas.Series.str.title) to replace the underscores ('_') with spaces (' ') and convert the first character of each word to uppercase, respectively. \n",
    "\n",
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog      p2   p2_conf  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True  collie  0.156665   \n",
       "\n",
       "   p2_dog                 p3   p3_conf  p3_dog  \n",
       "0    True  Shetland_sheepdog  0.061428    True  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_prediction_copy.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use str.replace to replace and str.title\n",
    "image_prediction_copy['p1'] = image_prediction_copy['p1'].str.replace('_', ' ').str.title()\n",
    "image_prediction_copy['p2'] = image_prediction_copy['p2'].str.replace('_', ' ').str.title()\n",
    "image_prediction_copy['p3'] = image_prediction_copy['p3'].str.replace('_', ' ').str.title()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to see if it worked\n",
    "image_prediction_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Issue #11:\n",
    "\n",
    "#### Define:\n",
    "First, use [`pd.index`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.index.html) to get the indices for the rows in which none of the predictions (`p1`, `p2`, `p3`) seem to come from dogs. Then, drop those rows.\n",
    "\n",
    "\n",
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   tweet_id  2075 non-null   int64  \n",
      " 1   jpg_url   2075 non-null   object \n",
      " 2   img_num   2075 non-null   int64  \n",
      " 3   p1        2075 non-null   object \n",
      " 4   p1_conf   2075 non-null   float64\n",
      " 5   p1_dog    2075 non-null   bool   \n",
      " 6   p2        2075 non-null   object \n",
      " 7   p2_conf   2075 non-null   float64\n",
      " 8   p2_dog    2075 non-null   bool   \n",
      " 9   p3        2075 non-null   object \n",
      " 10  p3_conf   2075 non-null   float64\n",
      " 11  p3_dog    2075 non-null   bool   \n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# check the original composition of the table\n",
    "# before dropping any rows\n",
    "image_prediction_copy.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>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>6</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Box Turtle</td>\n",
       "      <td>0.933012</td>\n",
       "      <td>False</td>\n",
       "      <td>Mud Turtle</td>\n",
       "      <td>0.045885</td>\n",
       "      <td>False</td>\n",
       "      <td>Terrapin</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Shopping Cart</td>\n",
       "      <td>0.962465</td>\n",
       "      <td>False</td>\n",
       "      <td>Shopping Basket</td>\n",
       "      <td>0.014594</td>\n",
       "      <td>False</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.007959</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Hen</td>\n",
       "      <td>0.965932</td>\n",
       "      <td>False</td>\n",
       "      <td>Cock</td>\n",
       "      <td>0.033919</td>\n",
       "      <td>False</td>\n",
       "      <td>Partridge</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>666268910803644416</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Desktop Computer</td>\n",
       "      <td>0.086502</td>\n",
       "      <td>False</td>\n",
       "      <td>Desk</td>\n",
       "      <td>0.085547</td>\n",
       "      <td>False</td>\n",
       "      <td>Bookcase</td>\n",
       "      <td>0.079480</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Three-Toed Sloth</td>\n",
       "      <td>0.914671</td>\n",
       "      <td>False</td>\n",
       "      <td>Otter</td>\n",
       "      <td>0.015250</td>\n",
       "      <td>False</td>\n",
       "      <td>Great Grey Owl</td>\n",
       "      <td>0.013207</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id                                          jpg_url  \\\n",
       "6   666051853826850816  https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg   \n",
       "8   666057090499244032  https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg   \n",
       "17  666104133288665088  https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg   \n",
       "18  666268910803644416  https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg   \n",
       "21  666293911632134144  https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg   \n",
       "\n",
       "    img_num                p1   p1_conf  p1_dog               p2   p2_conf  \\\n",
       "6         1        Box Turtle  0.933012   False       Mud Turtle  0.045885   \n",
       "8         1     Shopping Cart  0.962465   False  Shopping Basket  0.014594   \n",
       "17        1               Hen  0.965932   False             Cock  0.033919   \n",
       "18        1  Desktop Computer  0.086502   False             Desk  0.085547   \n",
       "21        1  Three-Toed Sloth  0.914671   False            Otter  0.015250   \n",
       "\n",
       "    p2_dog                p3   p3_conf  p3_dog  \n",
       "6    False          Terrapin  0.017885   False  \n",
       "8    False  Golden Retriever  0.007959    True  \n",
       "17   False         Partridge  0.000052   False  \n",
       "18   False          Bookcase  0.079480   False  \n",
       "21   False    Great Grey Owl  0.013207   False  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there might be cases in which the first identified object \n",
    "# in the image is not a dog, but that doesn't mean a dog\n",
    "# won't show up in the second or third option\n",
    "image_prediction_copy.query('p1_dog == False').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop only the rows in which p1_dog,\n",
    "# p2_dog and p3_dog are false\n",
    "\n",
    "# get the indices\n",
    "indices = image_prediction_copy.query('p1_dog == False and p2_dog == False and p3_dog == False').index\n",
    "\n",
    "# drop the rows\n",
    "image_prediction_copy = image_prediction_copy.drop(indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": [
    "# check to see if it worked\n",
    "image_prediction_copy.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that every quality issue was addressed, all that's left is Tidiness Issue #3, i.e., joining the `image_prediction` table and the `archive` table to create one big, clean, dataset. \n",
    "\n",
    "### Tidiness Issue #3:\n",
    "\n",
    "3. The dataset could be tidily represented joining the `image_prediction` table into the `archive` table to create one master dataset\n",
    "\n",
    "#### Define: Use [`df.merge`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html) to merge the tables `archive_copy` and `image_prediction_copy`. \n",
    "\n",
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inner merge will ensure that only tweets that went through\n",
    "# the image prediction neural network are kept\n",
    "# and also the tweets of the archive table\n",
    "# in other words, the intersection of keys from both dataframes\n",
    "master = archive_copy.merge(image_prediction_copy, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of the master dataset\n",
    "master_clean = master.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1678 entries, 0 to 1677\n",
      "Data columns (total 24 columns):\n",
      " #   Column                 Non-Null Count  Dtype              \n",
      "---  ------                 --------------  -----              \n",
      " 0   tweet_id               1678 non-null   int64              \n",
      " 1   in_reply_to_status_id  20 non-null     Int64              \n",
      " 2   in_reply_to_user_id    20 non-null     Int64              \n",
      " 3   timestamp              1678 non-null   datetime64[ns, UTC]\n",
      " 4   source                 1678 non-null   object             \n",
      " 5   text                   1678 non-null   object             \n",
      " 6   expanded_urls          1678 non-null   object             \n",
      " 7   rating_numerator       1678 non-null   int64              \n",
      " 8   rating_denominator     1678 non-null   int64              \n",
      " 9   name                   1180 non-null   object             \n",
      " 10  favorite_count         1678 non-null   int64              \n",
      " 11  retweet_count          1678 non-null   int64              \n",
      " 12  dog_stage              259 non-null    object             \n",
      " 13  jpg_url                1678 non-null   object             \n",
      " 14  img_num                1678 non-null   int64              \n",
      " 15  p1                     1678 non-null   object             \n",
      " 16  p1_conf                1678 non-null   float64            \n",
      " 17  p1_dog                 1678 non-null   bool               \n",
      " 18  p2                     1678 non-null   object             \n",
      " 19  p2_conf                1678 non-null   float64            \n",
      " 20  p2_dog                 1678 non-null   bool               \n",
      " 21  p3                     1678 non-null   object             \n",
      " 22  p3_conf                1678 non-null   float64            \n",
      " 23  p3_dog                 1678 non-null   bool               \n",
      "dtypes: Int64(2), bool(3), datetime64[ns, UTC](1), float64(3), int64(6), object(9)\n",
      "memory usage: 296.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# check to see if it worked\n",
    "master_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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",
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       "      <th>timestamp</th>\n",
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       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>...</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>Twitter for iPhone</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>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>Papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892177421306343426                   <NA>                 <NA>   \n",
       "\n",
       "                  timestamp              source  \\\n",
       "0 2017-08-01 00:17:27+00:00  Twitter for iPhone   \n",
       "\n",
       "                                                text  \\\n",
       "0  This is Tilly. She's just checking pup on you....   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "\n",
       "   rating_denominator   name  ...  img_num         p1   p1_conf p1_dog  \\\n",
       "0                  10  Tilly  ...        1  Chihuahua  0.323581   True   \n",
       "\n",
       "         p2   p2_conf  p2_dog        p3   p3_conf  p3_dog  \n",
       "0  Pekinese  0.090647    True  Papillon  0.068957    True  \n",
       "\n",
       "[1 rows x 24 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_clean.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before moving on to store the dataset, all that's left is dropping the columns that won't be used in analysis.\n",
    "\n",
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_clean.drop(['in_reply_to_status_id', 'in_reply_to_user_id', 'source', 'expanded_urls', 'jpg_url', 'img_num'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "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>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>dog_stage</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>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>29384</td>\n",
       "      <td>5309</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>Papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03+00:00</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>22099</td>\n",
       "      <td>3490</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>Malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>Kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                 timestamp  \\\n",
       "0  892177421306343426 2017-08-01 00:17:27+00:00   \n",
       "1  891815181378084864 2017-07-31 00:18:03+00:00   \n",
       "\n",
       "                                                text  rating_numerator  \\\n",
       "0  This is Tilly. She's just checking pup on you....                13   \n",
       "1  This is Archie. He is a rare Norwegian Pouncin...                12   \n",
       "\n",
       "   rating_denominator    name  favorite_count  retweet_count dog_stage  \\\n",
       "0                  10   Tilly           29384           5309      <NA>   \n",
       "1                  10  Archie           22099           3490      <NA>   \n",
       "\n",
       "          p1   p1_conf  p1_dog        p2   p2_conf  p2_dog        p3  \\\n",
       "0  Chihuahua  0.323581    True  Pekinese  0.090647    True  Papillon   \n",
       "1  Chihuahua  0.716012    True  Malamute  0.078253    True    Kelpie   \n",
       "\n",
       "    p3_conf  p3_dog  \n",
       "0  0.068957    True  \n",
       "1  0.031379    True  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_clean.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1678 entries, 0 to 1677\n",
      "Data columns (total 18 columns):\n",
      " #   Column              Non-Null Count  Dtype              \n",
      "---  ------              --------------  -----              \n",
      " 0   tweet_id            1678 non-null   int64              \n",
      " 1   timestamp           1678 non-null   datetime64[ns, UTC]\n",
      " 2   text                1678 non-null   object             \n",
      " 3   rating_numerator    1678 non-null   int64              \n",
      " 4   rating_denominator  1678 non-null   int64              \n",
      " 5   name                1180 non-null   object             \n",
      " 6   favorite_count      1678 non-null   int64              \n",
      " 7   retweet_count       1678 non-null   int64              \n",
      " 8   dog_stage           259 non-null    object             \n",
      " 9   p1                  1678 non-null   object             \n",
      " 10  p1_conf             1678 non-null   float64            \n",
      " 11  p1_dog              1678 non-null   bool               \n",
      " 12  p2                  1678 non-null   object             \n",
      " 13  p2_conf             1678 non-null   float64            \n",
      " 14  p2_dog              1678 non-null   bool               \n",
      " 15  p3                  1678 non-null   object             \n",
      " 16  p3_conf             1678 non-null   float64            \n",
      " 17  p3_dog              1678 non-null   bool               \n",
      "dtypes: bool(3), datetime64[ns, UTC](1), float64(3), int64(5), object(6)\n",
      "memory usage: 214.7+ KB\n"
     ]
    }
   ],
   "source": [
    "master_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Storing Data\n",
    "Save gathered, assessed, and cleaned master dataset to a CSV file named \"twitter_archive_master.csv\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_clean.to_csv('data/twitter_archive_master.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Analyzing and Visualizing Data\n",
    "In this section, analyze and visualize your wrangled data. You must produce at least **three (3) insights and one (1) visualization.**\n",
    "\n",
    "### Insight #1: What are the 5 most common dog breeds in terms of sum of favorite tweets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>dog_stage</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>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27+00:00</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>29384</td>\n",
       "      <td>5309</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.090647</td>\n",
       "      <td>True</td>\n",
       "      <td>Papillon</td>\n",
       "      <td>0.068957</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                 timestamp  \\\n",
       "0  892177421306343426 2017-08-01 00:17:27+00:00   \n",
       "\n",
       "                                                text  rating_numerator  \\\n",
       "0  This is Tilly. She's just checking pup on you....                13   \n",
       "\n",
       "   rating_denominator   name  favorite_count  retweet_count dog_stage  \\\n",
       "0                  10  Tilly           29384           5309      <NA>   \n",
       "\n",
       "          p1   p1_conf  p1_dog        p2   p2_conf  p2_dog        p3  \\\n",
       "0  Chihuahua  0.323581    True  Pekinese  0.090647    True  Papillon   \n",
       "\n",
       "    p3_conf  p3_dog  \n",
       "0  0.068957    True  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the structure of the master dataset\n",
    "master_clean.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p1\n",
       "Golden Retriever      1478937\n",
       "Labrador Retriever     912568\n",
       "Pembroke               869427\n",
       "Chihuahua              599794\n",
       "Samoyed                448868\n",
       "                       ...   \n",
       "Ox                        498\n",
       "Seashore                  440\n",
       "Window Shade              297\n",
       "Piggy Bank                288\n",
       "Hay                       214\n",
       "Name: favorite_count, Length: 214, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by the dog breed and sum the number of times \n",
    "# a given tweet with that breed was favorited \n",
    "top_breeds = master_clean.groupby('p1')['favorite_count'].sum().sort_values(ascending=False)\n",
    "top_breeds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization #1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a function to generate bar charts and avoid repeating code unnecessarily\n",
    "def chart(input):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax = input.plot(kind='bar')\n",
    "    ax.yaxis.set_major_formatter('{x:,.0f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting bar chart for insight #1\n",
    "chart(top_breeds.iloc[:5])\n",
    "plt.title('Top 5 Most Favorited Breeds', fontsize=16)\n",
    "plt.xlabel('Breeds', fontsize=14);\n",
    "plt.ylabel('Sum of Favorited Tweets', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight #2: Does the dog stage influence the amount of interations a tweet receive (favorites and retweets)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a column called 'interactions'\n",
    "# representing the amount of times a tweet was \n",
    "# both favorited and retweeted\n",
    "master_clean['interactions'] = master_clean['favorite_count'] + master_clean['retweet_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>dog_stage</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>interactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>821044531881721856</td>\n",
       "      <td>2017-01-16 17:20:45+00:00</td>\n",
       "      <td>This is Flash. He went way too hard celebratin...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Flash</td>\n",
       "      <td>11986</td>\n",
       "      <td>2133</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Old English Sheepdog</td>\n",
       "      <td>0.14802</td>\n",
       "      <td>True</td>\n",
       "      <td>Airedale</td>\n",
       "      <td>0.133534</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan Mastiff</td>\n",
       "      <td>0.120903</td>\n",
       "      <td>True</td>\n",
       "      <td>14119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id                 timestamp  \\\n",
       "286  821044531881721856 2017-01-16 17:20:45+00:00   \n",
       "\n",
       "                                                  text  rating_numerator  \\\n",
       "286  This is Flash. He went way too hard celebratin...                12   \n",
       "\n",
       "     rating_denominator   name  favorite_count  retweet_count dog_stage  \\\n",
       "286                  10  Flash           11986           2133      <NA>   \n",
       "\n",
       "                       p1  p1_conf  p1_dog        p2   p2_conf  p2_dog  \\\n",
       "286  Old English Sheepdog  0.14802    True  Airedale  0.133534    True   \n",
       "\n",
       "                  p3   p3_conf  p3_dog  interactions  \n",
       "286  Tibetan Mastiff  0.120903    True         14119  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to see if it worked\n",
    "master_clean.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# capitalize the dog stage for style\n",
    "master_clean['dog_stage'] = master_clean['dog_stage'].str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dog_stage\n",
       "Doggo      1469942\n",
       "Pupper     1422722\n",
       "Puppo       521621\n",
       "Floofer     106739\n",
       "Name: interactions, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by the dog stage and get the sum of interactions\n",
    "rating_influence = master_clean.groupby(['dog_stage'])['interactions'].sum().sort_values(ascending=False)\n",
    "rating_influence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization #2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting bar chart for insight #2\n",
    "chart(rating_influence)\n",
    "plt.title('Interactions by Dog Stage', fontsize=16)\n",
    "plt.xlabel('Dog Stage', fontsize=14);\n",
    "plt.ylabel('Amount of Interactions', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight #3: Does the average rating increase through time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: I used [this](https://towardsdatascience.com/how-to-group-data-by-different-time-intervals-using-python-pandas-eb7134f9b9b0) Medium post to figure out how to group by month. Then, I went to the [`pd.resample`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.resample.html) documentation and found out how to group by trimesters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2015-11-30 00:00:00+00:00     9.448889\n",
       "2016-02-29 00:00:00+00:00    10.252896\n",
       "2016-05-31 00:00:00+00:00    10.695000\n",
       "2016-08-31 00:00:00+00:00    10.710526\n",
       "2016-11-30 00:00:00+00:00    12.062112\n",
       "2017-02-28 00:00:00+00:00    12.148810\n",
       "2017-05-31 00:00:00+00:00    12.428571\n",
       "2017-08-31 00:00:00+00:00    12.586207\n",
       "Freq: 3M, Name: rating_numerator, dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first, query just the rows in which the rating denominator is 10\n",
    "# i.e., the tweets with only one dog in the picture,\n",
    "# to remove the outliers\n",
    "# then, resample by the trimester\n",
    "# finally, get the mean rating numerator for each trimester\n",
    "trimesters = master_clean.query('rating_denominator == 10').resample('3M', on='timestamp')['rating_numerator'].mean()\n",
    "trimesters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization #3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting line chart\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(trimesters, 'o-')\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(10)\n",
    "plt.title('Rating Average Over Time', fontsize=16)\n",
    "plt.xlabel('Trimester', fontsize=14);\n",
    "plt.ylabel('Rating Average', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:\n",
    "**`1.`** The top 5 most favorited breeds are: Golden Retriever, Labrador Retriever, Pembroke, Chihuahua and Samoyed. This insight was only possible after piercing together and cleaning the master dataset. The main pieces of information needed for this insight were: number of times a tweet was favorited and dog breed. Both of those columns were originally in separate datasets.\n",
    "\n",
    "**`2.`** According to WeRateDog's [dogtionary](https://video.udacity-data.com/topher/2017/October/59e04ceb_dogtionary-combined/dogtionary-combined.png), the four dog stages are: doggo, pupper, puppo and floofer. One might think that puppers (small dogs, usually younger) get the most interactions, but the amount of interactions puppers received are almost the same as doggos (a big pupper, usually older). In a distant third, comes the puppos (a transitional phase between puppo and doggos). Lastly, the floofers (any dogs with seemingly excess fur). As a limitation, it's worth mentioning that only a small number of the sample had the dog stage. This insight was only possible after tidying the four dog stage columns and turning them into one.\n",
    "\n",
    "**`3.`** Although we only have tweet info from 2016 and 2017, the data seem to suggest the average rating for dogs is increasing through time. Without converting the timestamp column to datetime, this analysis wouldn't be possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Visualizations\n",
    "\n",
    "#### Insight #1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting bar chart for insight #1\n",
    "chart(top_breeds.iloc[:5])\n",
    "plt.title('Top 5 Most Favorited Breeds', fontsize=16)\n",
    "plt.xlabel('Breeds', fontsize=14);\n",
    "plt.ylabel('Sum of Favorited Tweets', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Insight #2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting bar chart for insight #2\n",
    "chart(rating_influence)\n",
    "plt.title('Interactions by Dog Stage', fontsize=16)\n",
    "plt.xlabel('Dog Stage', fontsize=14);\n",
    "plt.ylabel('Amount of Interactions', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Insight #3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting line chart\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(trimesters, 'o-')\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(10)\n",
    "plt.title('Rating Average Over Time', fontsize=16)\n",
    "plt.xlabel('Trimester', fontsize=14);\n",
    "plt.ylabel('Rating Average', fontsize=14);"
   ]
  }
 ],
 "metadata": {
  "extensions": {
   "jupyter_dashboards": {
    "activeView": "report_default",
    "version": 1,
    "views": {
     "grid_default": {
      "cellMargin": 10,
      "defaultCellHeight": 20,
      "maxColumns": 12,
      "name": "grid",
      "type": "grid"
     },
     "report_default": {
      "name": "report",
      "type": "report"
     }
    }
   }
  },
  "kernelspec": {
   "display_name": "Python [conda env:dand] *",
   "language": "python",
   "name": "conda-env-dand-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}