wrangle_act.ipynb

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   "source": [
    "# Project: Wrangling and Analyze Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import tweepy\n",
    "from tweepy import OAuthHandler\n",
    "import json\n",
    "import re\n",
    "from timeit import default_timer as timer\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Gathering\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",
    "1. Directly download the WeRateDogs Twitter archive data (twitter-archive-enhanced.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [],
   "source": [
    "df_archive = pd.read_csv('twitter-archive-enhanced.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Use the Requests library to download the tweet image prediction (image_predictions.tsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'\n",
    "req = requests.get(url)\n",
    "with open(url.split('/')[-1], mode = 'wb') as file:\n",
    "    file.write(req.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predictions = pd.read_csv('image-predictions.tsv', sep = '\\t')"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Query Twitter API for each tweet in the Twitter archive and save JSON in a text file\n",
    "# These are hidden to comply with Twitter's API terms and conditions\n",
    "consumer_key = 'HIDDEN'\n",
    "consumer_secret = 'HIDDEN'\n",
    "access_token = 'HIDDEN'\n",
    "access_secret = 'HIDDEN'\n",
    "\n",
    "auth = OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True)\n",
    "\n",
    "# NOTE TO STUDENT WITH MOBILE VERIFICATION ISSUES:\n",
    "# df_1 is a DataFrame with the twitter_archive_enhanced.csv file. You may have to\n",
    "# change line 17 to match the name of your DataFrame with twitter_archive_enhanced.csv\n",
    "\n",
    "# NOTE TO REVIEWER: this student had mobile verification issues so the following\n",
    "# Twitter API code was sent to this student from a Udacity instructor\n",
    "# Tweet IDs for which to gather additional data via Twitter's API\n",
    "tweet_ids = df_archive.tweet_id.values\n",
    "len(tweet_ids)\n",
    "\n",
    "# Query Twitter's API for JSON data for each tweet ID in the Twitter archive\n",
    "count = 0\n",
    "fails_dict = {}\n",
    "start = timer()\n",
    "# Save each tweet's returned JSON as a new line in a .txt file\n",
    "with open('tweet_json.txt', 'w') as outfile:\n",
    "    # This loop will likely take 20-30 minutes to run because of Twitter's rate limit\n",
    "    for tweet_id in tweet_ids:\n",
    "        count += 1\n",
    "        print(str(count) + \": \" + str(tweet_id))\n",
    "        try:\n",
    "            tweet = api.get_status(tweet_id, tweet_mode='extended')\n",
    "            print(\"Success\")\n",
    "            json.dump(tweet._json, outfile)\n",
    "            outfile.write('\\n')\n",
    "        except tweepy.TweepError as e:\n",
    "            print(\"Fail\")\n",
    "            fails_dict[tweet_id] = e\n",
    "            pass\n",
    "end = timer()\n",
    "print(end - start)\n",
    "print(fails_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read tweet_json.txt into a dataframe\n",
    "with open('tweet_json.txt', 'r') as file:\n",
    "    tweet_lines = file.readlines()\n",
    "    \n",
    "data = []\n",
    "\n",
    "for tweet_line in tweet_lines:\n",
    "    tweet_data = json.loads(tweet_line)\n",
    "    tweet_id = tweet_data['id']\n",
    "    retweet_count = tweet_data['retweet_count']\n",
    "    favorite_count = tweet_data['favorite_count']\n",
    "    data.append({'tweet_id': tweet_id, 'retweet_count': retweet_count, 'favorite_count': favorite_count})\n",
    "    \n",
    "df_tweets = pd.DataFrame(data, columns = ['tweet_id', 'retweet_count', 'favorite_count'])"
   ]
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   "source": [
    "## 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 5000+ 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": [
    "Before we proceed, we need to note that one of our tasks is to remove retweets, replies, and tweets without images (per project instructions)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with the **df_archive** table. We will use both visual assessment and programmatic assessment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <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>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>puppo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>889278841981685760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-24 00:19:32 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Oliver. You're witnessing one of his m...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/889278841...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Oliver</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>888917238123831296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-23 00:22:39 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Jim. He found a fren. Taught him how t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888917238...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Jim</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>888804989199671297</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-22 16:56:37 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Zeke. He has a new stick. Very proud o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888804989...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Zeke</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>888554962724278272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-22 00:23:06 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Ralphus. He's powering up. Attempting ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888554962...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Ralphus</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\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>20</th>\n",
       "      <td>888078434458587136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-20 16:49:33 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Gerald. He was just told he didn't get...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/888078434...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Gerald</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>887705289381826560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-19 16:06:48 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Jeffrey. He has a monopoly on the pool...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887705289...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Jeffrey</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>887517139158093824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-19 03:39:09 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>I've yet to rate a Venezuelan Hover Wiener. Th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887517139...</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>such</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>887473957103951883</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-19 00:47:34 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Canela. She attempted some fancy porch...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>24</th>\n",
       "      <td>887343217045368832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-18 16:08:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>You may not have known you needed to see this ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887343217...</td>\n",
       "      <td>13</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>25</th>\n",
       "      <td>887101392804085760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-18 00:07:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This... is a Jubilant Antarctic House Bear. We...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/887101392...</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>26</th>\n",
       "      <td>886983233522544640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-17 16:17:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Maya. She's very shy. Rarely leaves he...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886983233...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Maya</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>886736880519319552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-16 23:58:41 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Mingus. He's a wonderful father to his...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.gofundme.com/mingusneedsus,https:/...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Mingus</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>886680336477933568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-16 20:14:00 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Derek. He's late for a dog meeting. 13...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886680336...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Derek</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>886366144734445568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-15 23:25:31 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Roscoe. Another pupper fallen victim t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/886366144...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Roscoe</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</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>2326</th>\n",
       "      <td>666411507551481857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-17 00:24:19 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is quite the dog. Gets really excited whe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666411507...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>quite</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>666407126856765440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-17 00:06:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a southern Vesuvius bumblegruff. Can d...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666407126...</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>2328</th>\n",
       "      <td>666396247373291520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 23:23:41 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Oh goodness. A super rare northeast Qdoba kang...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666396247...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2329</th>\n",
       "      <td>666373753744588802</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 21:54:18 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Those are sunglasses and a jean jacket. 11/10 ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666373753...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>666362758909284353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 21:10:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Unique dog here. Very small. Lives in containe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666362758...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2331</th>\n",
       "      <td>666353288456101888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 20:32:58 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a mixed Asiago from the Galápagos...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666353288...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2332</th>\n",
       "      <td>666345417576210432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 20:01:42 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Look at this jokester thinking seat belt laws ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666345417...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>666337882303524864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 19:31:45 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is an extremely rare horned Parthenon. No...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666337882...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>an</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 16:37:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a funny dog. Weird toes. Won't come do...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666293911...</td>\n",
       "      <td>3</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>2335</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 16:11:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is an Albanian 3 1/2 legged  Episcopalian...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666287406...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>an</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2336</th>\n",
       "      <td>666273097616637952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 15:14:19 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Can take selfies 11/10 https://t.co/ws2AMaNwPW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666273097...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>666268910803644416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 14:57:41 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Very concerned about fellow dog trapped in com...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666268910...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2338</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 04:02:55 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Not familiar with this breed. No tail (weird)....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666104133...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339</th>\n",
       "      <td>666102155909144576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 03:55:04 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Oh my. Here you are seeing an Adobe Setter giv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666102155...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2340</th>\n",
       "      <td>666099513787052032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 03:44:34 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Can stand on stump for what seems like a while...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666099513...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2341</th>\n",
       "      <td>666094000022159362</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 03:22:39 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This appears to be a Mongolian Presbyterian mi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666094000...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2342</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 02:38:37 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a well-established sunblockerspan...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666082916...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2343</th>\n",
       "      <td>666073100786774016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:59:36 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Let's hope this flight isn't Malaysian (lol). ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666073100...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2344</th>\n",
       "      <td>666071193221509120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:52:02 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a northern speckled Rhododendron....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666071193...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2345</th>\n",
       "      <td>666063827256086533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:22:45 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is the happiest dog you will ever see. Ve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666063827...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>the</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2346</th>\n",
       "      <td>666058600524156928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 01:01:59 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is the Rand Paul of retrievers folks! He'...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666058600...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>the</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:55:59 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>My oh my. This is a rare blond Canadian terrie...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666057090...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:49:46 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a Siberian heavily armored polar bear ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666055525...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:35:11 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is an odd dog. Hard on the outside but lo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666051853...</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>an</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:30:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a truly beautiful English Wilson Staff...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666050758...</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:24:50 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a 1949 1st generation vulpix. Enj...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666049248...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-16 00:04:52 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a purebred Piers Morgan. Loves to Netf...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666044226...</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:21:54 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here is a very happy pup. Big fan of well-main...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666033412...</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 23:05:30 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is a western brown Mitsubishi terrier. Up...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666029285...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2355</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-15 22:32:08 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Here we have a Japanese Irish Setter. Lost eye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/666020888...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2356 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
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       "23    887473957103951883                    NaN                  NaN   \n",
       "24    887343217045368832                    NaN                  NaN   \n",
       "25    887101392804085760                    NaN                  NaN   \n",
       "26    886983233522544640                    NaN                  NaN   \n",
       "27    886736880519319552                    NaN                  NaN   \n",
       "28    886680336477933568                    NaN                  NaN   \n",
       "29    886366144734445568                    NaN                  NaN   \n",
       "...                  ...                    ...                  ...   \n",
       "2326  666411507551481857                    NaN                  NaN   \n",
       "2327  666407126856765440                    NaN                  NaN   \n",
       "2328  666396247373291520                    NaN                  NaN   \n",
       "2329  666373753744588802                    NaN                  NaN   \n",
       "2330  666362758909284353                    NaN                  NaN   \n",
       "2331  666353288456101888                    NaN                  NaN   \n",
       "2332  666345417576210432                    NaN                  NaN   \n",
       "2333  666337882303524864                    NaN                  NaN   \n",
       "2334  666293911632134144                    NaN                  NaN   \n",
       "2335  666287406224695296                    NaN                  NaN   \n",
       "2336  666273097616637952                    NaN                  NaN   \n",
       "2337  666268910803644416                    NaN                  NaN   \n",
       "2338  666104133288665088                    NaN                  NaN   \n",
       "2339  666102155909144576                    NaN                  NaN   \n",
       "2340  666099513787052032                    NaN                  NaN   \n",
       "2341  666094000022159362                    NaN                  NaN   \n",
       "2342  666082916733198337                    NaN                  NaN   \n",
       "2343  666073100786774016                    NaN                  NaN   \n",
       "2344  666071193221509120                    NaN                  NaN   \n",
       "2345  666063827256086533                    NaN                  NaN   \n",
       "2346  666058600524156928                    NaN                  NaN   \n",
       "2347  666057090499244032                    NaN                  NaN   \n",
       "2348  666055525042405380                    NaN                  NaN   \n",
       "2349  666051853826850816                    NaN                  NaN   \n",
       "2350  666050758794694657                    NaN                  NaN   \n",
       "2351  666049248165822465                    NaN                  NaN   \n",
       "2352  666044226329800704                    NaN                  NaN   \n",
       "2353  666033412701032449                    NaN                  NaN   \n",
       "2354  666029285002620928                    NaN                  NaN   \n",
       "2355  666020888022790149                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "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",
       "5     2017-07-29 00:08:17 +0000   \n",
       "6     2017-07-28 16:27:12 +0000   \n",
       "7     2017-07-28 00:22:40 +0000   \n",
       "8     2017-07-27 16:25:51 +0000   \n",
       "9     2017-07-26 15:59:51 +0000   \n",
       "10    2017-07-26 00:31:25 +0000   \n",
       "11    2017-07-25 16:11:53 +0000   \n",
       "12    2017-07-25 01:55:32 +0000   \n",
       "13    2017-07-25 00:10:02 +0000   \n",
       "14    2017-07-24 17:02:04 +0000   \n",
       "15    2017-07-24 00:19:32 +0000   \n",
       "16    2017-07-23 00:22:39 +0000   \n",
       "17    2017-07-22 16:56:37 +0000   \n",
       "18    2017-07-22 00:23:06 +0000   \n",
       "19    2017-07-21 01:02:36 +0000   \n",
       "20    2017-07-20 16:49:33 +0000   \n",
       "21    2017-07-19 16:06:48 +0000   \n",
       "22    2017-07-19 03:39:09 +0000   \n",
       "23    2017-07-19 00:47:34 +0000   \n",
       "24    2017-07-18 16:08:03 +0000   \n",
       "25    2017-07-18 00:07:08 +0000   \n",
       "26    2017-07-17 16:17:36 +0000   \n",
       "27    2017-07-16 23:58:41 +0000   \n",
       "28    2017-07-16 20:14:00 +0000   \n",
       "29    2017-07-15 23:25:31 +0000   \n",
       "...                         ...   \n",
       "2326  2015-11-17 00:24:19 +0000   \n",
       "2327  2015-11-17 00:06:54 +0000   \n",
       "2328  2015-11-16 23:23:41 +0000   \n",
       "2329  2015-11-16 21:54:18 +0000   \n",
       "2330  2015-11-16 21:10:36 +0000   \n",
       "2331  2015-11-16 20:32:58 +0000   \n",
       "2332  2015-11-16 20:01:42 +0000   \n",
       "2333  2015-11-16 19:31:45 +0000   \n",
       "2334  2015-11-16 16:37:02 +0000   \n",
       "2335  2015-11-16 16:11:11 +0000   \n",
       "2336  2015-11-16 15:14:19 +0000   \n",
       "2337  2015-11-16 14:57:41 +0000   \n",
       "2338  2015-11-16 04:02:55 +0000   \n",
       "2339  2015-11-16 03:55:04 +0000   \n",
       "2340  2015-11-16 03:44:34 +0000   \n",
       "2341  2015-11-16 03:22:39 +0000   \n",
       "2342  2015-11-16 02:38:37 +0000   \n",
       "2343  2015-11-16 01:59:36 +0000   \n",
       "2344  2015-11-16 01:52:02 +0000   \n",
       "2345  2015-11-16 01:22:45 +0000   \n",
       "2346  2015-11-16 01:01:59 +0000   \n",
       "2347  2015-11-16 00:55:59 +0000   \n",
       "2348  2015-11-16 00:49:46 +0000   \n",
       "2349  2015-11-16 00:35:11 +0000   \n",
       "2350  2015-11-16 00:30:50 +0000   \n",
       "2351  2015-11-16 00:24:50 +0000   \n",
       "2352  2015-11-16 00:04:52 +0000   \n",
       "2353  2015-11-15 23:21:54 +0000   \n",
       "2354  2015-11-15 23:05:30 +0000   \n",
       "2355  2015-11-15 22:32:08 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "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",
       "5     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "6     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "7     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "8     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "9     <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "10    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "11    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "12    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "13    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "14    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "15    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "16    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "17    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "18    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "19    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "20    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "21    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "22    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "23    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "24    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "25    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "26    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "27    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "28    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "29    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "...                                                 ...   \n",
       "2326  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2327  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2328  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2329  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2330  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2331  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2332  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2333  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2334  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2335  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2336  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2337  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2338  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2339  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2340  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2341  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2342  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2343  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2344  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2345  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2346  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2347  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2348  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2349  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2350  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2351  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2352  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2353  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2354  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2355  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "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",
       "5     Here we have a majestic great white breaching ...                  NaN   \n",
       "6     Meet Jax. He enjoys ice cream so much he gets ...                  NaN   \n",
       "7     When you watch your owner call another dog a g...                  NaN   \n",
       "8     This is Zoey. She doesn't want to be one of th...                  NaN   \n",
       "9     This is Cassie. She is a college pup. Studying...                  NaN   \n",
       "10    This is Koda. He is a South Australian decksha...                  NaN   \n",
       "11    This is Bruno. He is a service shark. Only get...                  NaN   \n",
       "12    Here's a puppo that seems to be on the fence a...                  NaN   \n",
       "13    This is Ted. He does his best. Sometimes that'...                  NaN   \n",
       "14    This is Stuart. He's sporting his favorite fan...                  NaN   \n",
       "15    This is Oliver. You're witnessing one of his m...                  NaN   \n",
       "16    This is Jim. He found a fren. Taught him how t...                  NaN   \n",
       "17    This is Zeke. He has a new stick. Very proud o...                  NaN   \n",
       "18    This is Ralphus. He's powering up. Attempting ...                  NaN   \n",
       "19    RT @dog_rates: This is Canela. She attempted s...         8.874740e+17   \n",
       "20    This is Gerald. He was just told he didn't get...                  NaN   \n",
       "21    This is Jeffrey. He has a monopoly on the pool...                  NaN   \n",
       "22    I've yet to rate a Venezuelan Hover Wiener. Th...                  NaN   \n",
       "23    This is Canela. She attempted some fancy porch...                  NaN   \n",
       "24    You may not have known you needed to see this ...                  NaN   \n",
       "25    This... is a Jubilant Antarctic House Bear. We...                  NaN   \n",
       "26    This is Maya. She's very shy. Rarely leaves he...                  NaN   \n",
       "27    This is Mingus. He's a wonderful father to his...                  NaN   \n",
       "28    This is Derek. He's late for a dog meeting. 13...                  NaN   \n",
       "29    This is Roscoe. Another pupper fallen victim t...                  NaN   \n",
       "...                                                 ...                  ...   \n",
       "2326  This is quite the dog. Gets really excited whe...                  NaN   \n",
       "2327  This is a southern Vesuvius bumblegruff. Can d...                  NaN   \n",
       "2328  Oh goodness. A super rare northeast Qdoba kang...                  NaN   \n",
       "2329  Those are sunglasses and a jean jacket. 11/10 ...                  NaN   \n",
       "2330  Unique dog here. Very small. Lives in containe...                  NaN   \n",
       "2331  Here we have a mixed Asiago from the Galápagos...                  NaN   \n",
       "2332  Look at this jokester thinking seat belt laws ...                  NaN   \n",
       "2333  This is an extremely rare horned Parthenon. No...                  NaN   \n",
       "2334  This is a funny dog. Weird toes. Won't come do...                  NaN   \n",
       "2335  This is an Albanian 3 1/2 legged  Episcopalian...                  NaN   \n",
       "2336     Can take selfies 11/10 https://t.co/ws2AMaNwPW                  NaN   \n",
       "2337  Very concerned about fellow dog trapped in com...                  NaN   \n",
       "2338  Not familiar with this breed. No tail (weird)....                  NaN   \n",
       "2339  Oh my. Here you are seeing an Adobe Setter giv...                  NaN   \n",
       "2340  Can stand on stump for what seems like a while...                  NaN   \n",
       "2341  This appears to be a Mongolian Presbyterian mi...                  NaN   \n",
       "2342  Here we have a well-established sunblockerspan...                  NaN   \n",
       "2343  Let's hope this flight isn't Malaysian (lol). ...                  NaN   \n",
       "2344  Here we have a northern speckled Rhododendron....                  NaN   \n",
       "2345  This is the happiest dog you will ever see. Ve...                  NaN   \n",
       "2346  Here is the Rand Paul of retrievers folks! He'...                  NaN   \n",
       "2347  My oh my. This is a rare blond Canadian terrie...                  NaN   \n",
       "2348  Here is a Siberian heavily armored polar bear ...                  NaN   \n",
       "2349  This is an odd dog. Hard on the outside but lo...                  NaN   \n",
       "2350  This is a truly beautiful English Wilson Staff...                  NaN   \n",
       "2351  Here we have a 1949 1st generation vulpix. Enj...                  NaN   \n",
       "2352  This is a purebred Piers Morgan. Loves to Netf...                  NaN   \n",
       "2353  Here is a very happy pup. Big fan of well-main...                  NaN   \n",
       "2354  This is a western brown Mitsubishi terrier. Up...                  NaN   \n",
       "2355  Here we have a Japanese Irish Setter. Lost eye...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                          NaN                        NaN   \n",
       "1                          NaN                        NaN   \n",
       "2                          NaN                        NaN   \n",
       "3                          NaN                        NaN   \n",
       "4                          NaN                        NaN   \n",
       "5                          NaN                        NaN   \n",
       "6                          NaN                        NaN   \n",
       "7                          NaN                        NaN   \n",
       "8                          NaN                        NaN   \n",
       "9                          NaN                        NaN   \n",
       "10                         NaN                        NaN   \n",
       "11                         NaN                        NaN   \n",
       "12                         NaN                        NaN   \n",
       "13                         NaN                        NaN   \n",
       "14                         NaN                        NaN   \n",
       "15                         NaN                        NaN   \n",
       "16                         NaN                        NaN   \n",
       "17                         NaN                        NaN   \n",
       "18                         NaN                        NaN   \n",
       "19                4.196984e+09  2017-07-19 00:47:34 +0000   \n",
       "20                         NaN                        NaN   \n",
       "21                         NaN                        NaN   \n",
       "22                         NaN                        NaN   \n",
       "23                         NaN                        NaN   \n",
       "24                         NaN                        NaN   \n",
       "25                         NaN                        NaN   \n",
       "26                         NaN                        NaN   \n",
       "27                         NaN                        NaN   \n",
       "28                         NaN                        NaN   \n",
       "29                         NaN                        NaN   \n",
       "...                        ...                        ...   \n",
       "2326                       NaN                        NaN   \n",
       "2327                       NaN                        NaN   \n",
       "2328                       NaN                        NaN   \n",
       "2329                       NaN                        NaN   \n",
       "2330                       NaN                        NaN   \n",
       "2331                       NaN                        NaN   \n",
       "2332                       NaN                        NaN   \n",
       "2333                       NaN                        NaN   \n",
       "2334                       NaN                        NaN   \n",
       "2335                       NaN                        NaN   \n",
       "2336                       NaN                        NaN   \n",
       "2337                       NaN                        NaN   \n",
       "2338                       NaN                        NaN   \n",
       "2339                       NaN                        NaN   \n",
       "2340                       NaN                        NaN   \n",
       "2341                       NaN                        NaN   \n",
       "2342                       NaN                        NaN   \n",
       "2343                       NaN                        NaN   \n",
       "2344                       NaN                        NaN   \n",
       "2345                       NaN                        NaN   \n",
       "2346                       NaN                        NaN   \n",
       "2347                       NaN                        NaN   \n",
       "2348                       NaN                        NaN   \n",
       "2349                       NaN                        NaN   \n",
       "2350                       NaN                        NaN   \n",
       "2351                       NaN                        NaN   \n",
       "2352                       NaN                        NaN   \n",
       "2353                       NaN                        NaN   \n",
       "2354                       NaN                        NaN   \n",
       "2355                       NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "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",
       "5     https://twitter.com/dog_rates/status/891087950...                13   \n",
       "6     https://gofundme.com/ydvmve-surgery-for-jax,ht...                13   \n",
       "7     https://twitter.com/dog_rates/status/890729181...                13   \n",
       "8     https://twitter.com/dog_rates/status/890609185...                13   \n",
       "9     https://twitter.com/dog_rates/status/890240255...                14   \n",
       "10    https://twitter.com/dog_rates/status/890006608...                13   \n",
       "11    https://twitter.com/dog_rates/status/889880896...                13   \n",
       "12    https://twitter.com/dog_rates/status/889665388...                13   \n",
       "13    https://twitter.com/dog_rates/status/889638837...                12   \n",
       "14    https://twitter.com/dog_rates/status/889531135...                13   \n",
       "15    https://twitter.com/dog_rates/status/889278841...                13   \n",
       "16    https://twitter.com/dog_rates/status/888917238...                12   \n",
       "17    https://twitter.com/dog_rates/status/888804989...                13   \n",
       "18    https://twitter.com/dog_rates/status/888554962...                13   \n",
       "19    https://twitter.com/dog_rates/status/887473957...                13   \n",
       "20    https://twitter.com/dog_rates/status/888078434...                12   \n",
       "21    https://twitter.com/dog_rates/status/887705289...                13   \n",
       "22    https://twitter.com/dog_rates/status/887517139...                14   \n",
       "23    https://twitter.com/dog_rates/status/887473957...                13   \n",
       "24    https://twitter.com/dog_rates/status/887343217...                13   \n",
       "25    https://twitter.com/dog_rates/status/887101392...                12   \n",
       "26    https://twitter.com/dog_rates/status/886983233...                13   \n",
       "27    https://www.gofundme.com/mingusneedsus,https:/...                13   \n",
       "28    https://twitter.com/dog_rates/status/886680336...                13   \n",
       "29    https://twitter.com/dog_rates/status/886366144...                12   \n",
       "...                                                 ...               ...   \n",
       "2326  https://twitter.com/dog_rates/status/666411507...                 2   \n",
       "2327  https://twitter.com/dog_rates/status/666407126...                 7   \n",
       "2328  https://twitter.com/dog_rates/status/666396247...                 9   \n",
       "2329  https://twitter.com/dog_rates/status/666373753...                11   \n",
       "2330  https://twitter.com/dog_rates/status/666362758...                 6   \n",
       "2331  https://twitter.com/dog_rates/status/666353288...                 8   \n",
       "2332  https://twitter.com/dog_rates/status/666345417...                10   \n",
       "2333  https://twitter.com/dog_rates/status/666337882...                 9   \n",
       "2334  https://twitter.com/dog_rates/status/666293911...                 3   \n",
       "2335  https://twitter.com/dog_rates/status/666287406...                 1   \n",
       "2336  https://twitter.com/dog_rates/status/666273097...                11   \n",
       "2337  https://twitter.com/dog_rates/status/666268910...                10   \n",
       "2338  https://twitter.com/dog_rates/status/666104133...                 1   \n",
       "2339  https://twitter.com/dog_rates/status/666102155...                11   \n",
       "2340  https://twitter.com/dog_rates/status/666099513...                 8   \n",
       "2341  https://twitter.com/dog_rates/status/666094000...                 9   \n",
       "2342  https://twitter.com/dog_rates/status/666082916...                 6   \n",
       "2343  https://twitter.com/dog_rates/status/666073100...                10   \n",
       "2344  https://twitter.com/dog_rates/status/666071193...                 9   \n",
       "2345  https://twitter.com/dog_rates/status/666063827...                10   \n",
       "2346  https://twitter.com/dog_rates/status/666058600...                 8   \n",
       "2347  https://twitter.com/dog_rates/status/666057090...                 9   \n",
       "2348  https://twitter.com/dog_rates/status/666055525...                10   \n",
       "2349  https://twitter.com/dog_rates/status/666051853...                 2   \n",
       "2350  https://twitter.com/dog_rates/status/666050758...                10   \n",
       "2351  https://twitter.com/dog_rates/status/666049248...                 5   \n",
       "2352  https://twitter.com/dog_rates/status/666044226...                 6   \n",
       "2353  https://twitter.com/dog_rates/status/666033412...                 9   \n",
       "2354  https://twitter.com/dog_rates/status/666029285...                 7   \n",
       "2355  https://twitter.com/dog_rates/status/666020888...                 8   \n",
       "\n",
       "      rating_denominator      name  doggo floofer  pupper  puppo  \n",
       "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",
       "5                     10      None   None    None    None   None  \n",
       "6                     10       Jax   None    None    None   None  \n",
       "7                     10      None   None    None    None   None  \n",
       "8                     10      Zoey   None    None    None   None  \n",
       "9                     10    Cassie  doggo    None    None   None  \n",
       "10                    10      Koda   None    None    None   None  \n",
       "11                    10     Bruno   None    None    None   None  \n",
       "12                    10      None   None    None    None  puppo  \n",
       "13                    10       Ted   None    None    None   None  \n",
       "14                    10    Stuart   None    None    None  puppo  \n",
       "15                    10    Oliver   None    None    None   None  \n",
       "16                    10       Jim   None    None    None   None  \n",
       "17                    10      Zeke   None    None    None   None  \n",
       "18                    10   Ralphus   None    None    None   None  \n",
       "19                    10    Canela   None    None    None   None  \n",
       "20                    10    Gerald   None    None    None   None  \n",
       "21                    10   Jeffrey   None    None    None   None  \n",
       "22                    10      such   None    None    None   None  \n",
       "23                    10    Canela   None    None    None   None  \n",
       "24                    10      None   None    None    None   None  \n",
       "25                    10      None   None    None    None   None  \n",
       "26                    10      Maya   None    None    None   None  \n",
       "27                    10    Mingus   None    None    None   None  \n",
       "28                    10     Derek   None    None    None   None  \n",
       "29                    10    Roscoe   None    None  pupper   None  \n",
       "...                  ...       ...    ...     ...     ...    ...  \n",
       "2326                  10     quite   None    None    None   None  \n",
       "2327                  10         a   None    None    None   None  \n",
       "2328                  10      None   None    None    None   None  \n",
       "2329                  10      None   None    None    None   None  \n",
       "2330                  10      None   None    None    None   None  \n",
       "2331                  10      None   None    None    None   None  \n",
       "2332                  10      None   None    None    None   None  \n",
       "2333                  10        an   None    None    None   None  \n",
       "2334                  10         a   None    None    None   None  \n",
       "2335                   2        an   None    None    None   None  \n",
       "2336                  10      None   None    None    None   None  \n",
       "2337                  10      None   None    None    None   None  \n",
       "2338                  10      None   None    None    None   None  \n",
       "2339                  10      None   None    None    None   None  \n",
       "2340                  10      None   None    None    None   None  \n",
       "2341                  10      None   None    None    None   None  \n",
       "2342                  10      None   None    None    None   None  \n",
       "2343                  10      None   None    None    None   None  \n",
       "2344                  10      None   None    None    None   None  \n",
       "2345                  10       the   None    None    None   None  \n",
       "2346                  10       the   None    None    None   None  \n",
       "2347                  10         a   None    None    None   None  \n",
       "2348                  10         a   None    None    None   None  \n",
       "2349                  10        an   None    None    None   None  \n",
       "2350                  10         a   None    None    None   None  \n",
       "2351                  10      None   None    None    None   None  \n",
       "2352                  10         a   None    None    None   None  \n",
       "2353                  10         a   None    None    None   None  \n",
       "2354                  10         a   None    None    None   None  \n",
       "2355                  10      None   None    None    None   None  \n",
       "\n",
       "[2356 rows x 17 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually, we can see that the doggo, floofer, pupper, and puppo columns should not be four separate columns. They should be one column as they are all different categories of dog, and the 'none' values should be adjusted to nulls. \n",
    "\n",
    "Also, the name column does not always contain actual names. I see a lot of \"None\" values instead of names, and I also came across a few lowercases \"names\" like \"a\", \"an\", and \"quite\". I am fairly certain these are not real dog names. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      2356 non-null int64\n",
      "in_reply_to_status_id         78 non-null float64\n",
      "in_reply_to_user_id           78 non-null float64\n",
      "timestamp                     2356 non-null object\n",
      "source                        2356 non-null object\n",
      "text                          2356 non-null object\n",
      "retweeted_status_id           181 non-null float64\n",
      "retweeted_status_user_id      181 non-null float64\n",
      "retweeted_status_timestamp    181 non-null object\n",
      "expanded_urls                 2297 non-null object\n",
      "rating_numerator              2356 non-null int64\n",
      "rating_denominator            2356 non-null int64\n",
      "name                          2356 non-null object\n",
      "doggo                         2356 non-null object\n",
      "floofer                       2356 non-null object\n",
      "pupper                        2356 non-null object\n",
      "puppo                         2356 non-null object\n",
      "dtypes: float64(4), int64(3), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_archive.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The timestamp column is a string. This should be converted to datetime.\n",
    "\n",
    "Also, the tweet_id is an integer. We are not doing calculations on this identifier, therefore it should be converted to a string.\n",
    "\n",
    "We also see 78 replies and 181 retweets that need to be removed. We will use the in_reply_to_status_id and retweeted_status_id to help with that.\n",
    "\n",
    "Some tweets are missing images (expanded_urls). We will also want to remove those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_archive.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_archive.duplicated('tweet_id'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df_archive does not have any duplicated records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.356000e+03</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>7.800000e+01</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>1.810000e+02</td>\n",
       "      <td>2356.000000</td>\n",
       "      <td>2356.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.427716e+17</td>\n",
       "      <td>7.455079e+17</td>\n",
       "      <td>2.014171e+16</td>\n",
       "      <td>7.720400e+17</td>\n",
       "      <td>1.241698e+16</td>\n",
       "      <td>13.126486</td>\n",
       "      <td>10.455433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.856705e+16</td>\n",
       "      <td>7.582492e+16</td>\n",
       "      <td>1.252797e+17</td>\n",
       "      <td>6.236928e+16</td>\n",
       "      <td>9.599254e+16</td>\n",
       "      <td>45.876648</td>\n",
       "      <td>6.745237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>6.658147e+17</td>\n",
       "      <td>1.185634e+07</td>\n",
       "      <td>6.661041e+17</td>\n",
       "      <td>7.832140e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783989e+17</td>\n",
       "      <td>6.757419e+17</td>\n",
       "      <td>3.086374e+08</td>\n",
       "      <td>7.186315e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.196279e+17</td>\n",
       "      <td>7.038708e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>7.804657e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.993373e+17</td>\n",
       "      <td>8.257804e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>8.203146e+17</td>\n",
       "      <td>4.196984e+09</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>8.862664e+17</td>\n",
       "      <td>8.405479e+17</td>\n",
       "      <td>8.874740e+17</td>\n",
       "      <td>7.874618e+17</td>\n",
       "      <td>1776.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "count  2.356000e+03           7.800000e+01         7.800000e+01   \n",
       "mean   7.427716e+17           7.455079e+17         2.014171e+16   \n",
       "std    6.856705e+16           7.582492e+16         1.252797e+17   \n",
       "min    6.660209e+17           6.658147e+17         1.185634e+07   \n",
       "25%    6.783989e+17           6.757419e+17         3.086374e+08   \n",
       "50%    7.196279e+17           7.038708e+17         4.196984e+09   \n",
       "75%    7.993373e+17           8.257804e+17         4.196984e+09   \n",
       "max    8.924206e+17           8.862664e+17         8.405479e+17   \n",
       "\n",
       "       retweeted_status_id  retweeted_status_user_id  rating_numerator  \\\n",
       "count         1.810000e+02              1.810000e+02       2356.000000   \n",
       "mean          7.720400e+17              1.241698e+16         13.126486   \n",
       "std           6.236928e+16              9.599254e+16         45.876648   \n",
       "min           6.661041e+17              7.832140e+05          0.000000   \n",
       "25%           7.186315e+17              4.196984e+09         10.000000   \n",
       "50%           7.804657e+17              4.196984e+09         11.000000   \n",
       "75%           8.203146e+17              4.196984e+09         12.000000   \n",
       "max           8.874740e+17              7.874618e+17       1776.000000   \n",
       "\n",
       "       rating_denominator  \n",
       "count         2356.000000  \n",
       "mean            10.455433  \n",
       "std              6.745237  \n",
       "min              0.000000  \n",
       "25%             10.000000  \n",
       "50%             10.000000  \n",
       "75%             10.000000  \n",
       "max            170.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wacky numerator values are part of We Rate Dogs' charm, so I am not concerned with most of the odd values in rating_numerator. I do, however, want to check into the 0 minimum values for both the numerator and denominator. Those could cause problems later. I also would like to separately check rating_denominator values that are not equal to 10. We may be able to normalize those or simply drop them if there are not many."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Onwards to the **df_predictions** table!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Bernese_mountain_dog</td>\n",
       "      <td>0.651137</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.263788</td>\n",
       "      <td>True</td>\n",
       "      <td>Greater_Swiss_Mountain_dog</td>\n",
       "      <td>0.016199</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>box_turtle</td>\n",
       "      <td>0.933012</td>\n",
       "      <td>False</td>\n",
       "      <td>mud_turtle</td>\n",
       "      <td>0.045885</td>\n",
       "      <td>False</td>\n",
       "      <td>terrapin</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.692517</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.058279</td>\n",
       "      <td>True</td>\n",
       "      <td>fur_coat</td>\n",
       "      <td>0.054449</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>shopping_cart</td>\n",
       "      <td>0.962465</td>\n",
       "      <td>False</td>\n",
       "      <td>shopping_basket</td>\n",
       "      <td>0.014594</td>\n",
       "      <td>False</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.007959</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>666058600524156928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_poodle</td>\n",
       "      <td>0.201493</td>\n",
       "      <td>True</td>\n",
       "      <td>komondor</td>\n",
       "      <td>0.192305</td>\n",
       "      <td>True</td>\n",
       "      <td>soft-coated_wheaten_terrier</td>\n",
       "      <td>0.082086</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>666063827256086533</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.775930</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.093718</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.072427</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>666071193221509120</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Gordon_setter</td>\n",
       "      <td>0.503672</td>\n",
       "      <td>True</td>\n",
       "      <td>Yorkshire_terrier</td>\n",
       "      <td>0.174201</td>\n",
       "      <td>True</td>\n",
       "      <td>Pekinese</td>\n",
       "      <td>0.109454</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>666073100786774016</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Walker_hound</td>\n",
       "      <td>0.260857</td>\n",
       "      <td>True</td>\n",
       "      <td>English_foxhound</td>\n",
       "      <td>0.175382</td>\n",
       "      <td>True</td>\n",
       "      <td>Ibizan_hound</td>\n",
       "      <td>0.097471</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.489814</td>\n",
       "      <td>True</td>\n",
       "      <td>bull_mastiff</td>\n",
       "      <td>0.404722</td>\n",
       "      <td>True</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.048960</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>666094000022159362</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.195217</td>\n",
       "      <td>True</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.078260</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.075628</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>666099513787052032</td>\n",
       "      <td>https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Lhasa</td>\n",
       "      <td>0.582330</td>\n",
       "      <td>True</td>\n",
       "      <td>Shih-Tzu</td>\n",
       "      <td>0.166192</td>\n",
       "      <td>True</td>\n",
       "      <td>Dandie_Dinmont</td>\n",
       "      <td>0.089688</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>666102155909144576</td>\n",
       "      <td>https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>English_setter</td>\n",
       "      <td>0.298617</td>\n",
       "      <td>True</td>\n",
       "      <td>Newfoundland</td>\n",
       "      <td>0.149842</td>\n",
       "      <td>True</td>\n",
       "      <td>borzoi</td>\n",
       "      <td>0.133649</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>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>19</th>\n",
       "      <td>666273097616637952</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Italian_greyhound</td>\n",
       "      <td>0.176053</td>\n",
       "      <td>True</td>\n",
       "      <td>toy_terrier</td>\n",
       "      <td>0.111884</td>\n",
       "      <td>True</td>\n",
       "      <td>basenji</td>\n",
       "      <td>0.111152</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8g3BpUEAAuFjg.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Maltese_dog</td>\n",
       "      <td>0.857531</td>\n",
       "      <td>True</td>\n",
       "      <td>toy_poodle</td>\n",
       "      <td>0.063064</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_poodle</td>\n",
       "      <td>0.025581</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>666337882303524864</td>\n",
       "      <td>https://pbs.twimg.com/media/CT9OwFIWEAMuRje.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>ox</td>\n",
       "      <td>0.416669</td>\n",
       "      <td>False</td>\n",
       "      <td>Newfoundland</td>\n",
       "      <td>0.278407</td>\n",
       "      <td>True</td>\n",
       "      <td>groenendael</td>\n",
       "      <td>0.102643</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>666345417576210432</td>\n",
       "      <td>https://pbs.twimg.com/media/CT9Vn7PWoAA_ZCM.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.858744</td>\n",
       "      <td>True</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.054787</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.014241</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>666353288456101888</td>\n",
       "      <td>https://pbs.twimg.com/media/CT9cx0tUEAAhNN_.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.336874</td>\n",
       "      <td>True</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.147655</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.093412</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>666362758909284353</td>\n",
       "      <td>https://pbs.twimg.com/media/CT9lXGsUcAAyUFt.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>guinea_pig</td>\n",
       "      <td>0.996496</td>\n",
       "      <td>False</td>\n",
       "      <td>skunk</td>\n",
       "      <td>0.002402</td>\n",
       "      <td>False</td>\n",
       "      <td>hamster</td>\n",
       "      <td>0.000461</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>666373753744588802</td>\n",
       "      <td>https://pbs.twimg.com/media/CT9vZEYWUAAlZ05.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>soft-coated_wheaten_terrier</td>\n",
       "      <td>0.326467</td>\n",
       "      <td>True</td>\n",
       "      <td>Afghan_hound</td>\n",
       "      <td>0.259551</td>\n",
       "      <td>True</td>\n",
       "      <td>briard</td>\n",
       "      <td>0.206803</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>666396247373291520</td>\n",
       "      <td>https://pbs.twimg.com/media/CT-D2ZHWIAA3gK1.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.978108</td>\n",
       "      <td>True</td>\n",
       "      <td>toy_terrier</td>\n",
       "      <td>0.009397</td>\n",
       "      <td>True</td>\n",
       "      <td>papillon</td>\n",
       "      <td>0.004577</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>666407126856765440</td>\n",
       "      <td>https://pbs.twimg.com/media/CT-NvwmW4AAugGZ.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>black-and-tan_coonhound</td>\n",
       "      <td>0.529139</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.244220</td>\n",
       "      <td>True</td>\n",
       "      <td>flat-coated_retriever</td>\n",
       "      <td>0.173810</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>666411507551481857</td>\n",
       "      <td>https://pbs.twimg.com/media/CT-RugiWIAELEaq.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>coho</td>\n",
       "      <td>0.404640</td>\n",
       "      <td>False</td>\n",
       "      <td>barracouta</td>\n",
       "      <td>0.271485</td>\n",
       "      <td>False</td>\n",
       "      <td>gar</td>\n",
       "      <td>0.189945</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2045</th>\n",
       "      <td>886366144734445568</td>\n",
       "      <td>https://pbs.twimg.com/media/DE0BTnQUwAApKEH.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.999201</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.000361</td>\n",
       "      <td>True</td>\n",
       "      <td>Boston_bull</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046</th>\n",
       "      <td>886680336477933568</td>\n",
       "      <td>https://pbs.twimg.com/media/DE4fEDzWAAAyHMM.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>convertible</td>\n",
       "      <td>0.738995</td>\n",
       "      <td>False</td>\n",
       "      <td>sports_car</td>\n",
       "      <td>0.139952</td>\n",
       "      <td>False</td>\n",
       "      <td>car_wheel</td>\n",
       "      <td>0.044173</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2047</th>\n",
       "      <td>886736880519319552</td>\n",
       "      <td>https://pbs.twimg.com/media/DE5Se8FXcAAJFx4.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.309706</td>\n",
       "      <td>True</td>\n",
       "      <td>Great_Pyrenees</td>\n",
       "      <td>0.186136</td>\n",
       "      <td>True</td>\n",
       "      <td>Dandie_Dinmont</td>\n",
       "      <td>0.086346</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2048</th>\n",
       "      <td>886983233522544640</td>\n",
       "      <td>https://pbs.twimg.com/media/DE8yicJW0AAAvBJ.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.793469</td>\n",
       "      <td>True</td>\n",
       "      <td>toy_terrier</td>\n",
       "      <td>0.143528</td>\n",
       "      <td>True</td>\n",
       "      <td>can_opener</td>\n",
       "      <td>0.032253</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2049</th>\n",
       "      <td>887101392804085760</td>\n",
       "      <td>https://pbs.twimg.com/media/DE-eAq6UwAA-jaE.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.733942</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.035029</td>\n",
       "      <td>True</td>\n",
       "      <td>Staffordshire_bullterrier</td>\n",
       "      <td>0.029705</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>887343217045368832</td>\n",
       "      <td>https://pbs.twimg.com/ext_tw_video_thumb/88734...</td>\n",
       "      <td>1</td>\n",
       "      <td>Mexican_hairless</td>\n",
       "      <td>0.330741</td>\n",
       "      <td>True</td>\n",
       "      <td>sea_lion</td>\n",
       "      <td>0.275645</td>\n",
       "      <td>False</td>\n",
       "      <td>Weimaraner</td>\n",
       "      <td>0.134203</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2051</th>\n",
       "      <td>887473957103951883</td>\n",
       "      <td>https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.809197</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.054950</td>\n",
       "      <td>True</td>\n",
       "      <td>beagle</td>\n",
       "      <td>0.038915</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2052</th>\n",
       "      <td>887517139158093824</td>\n",
       "      <td>https://pbs.twimg.com/ext_tw_video_thumb/88751...</td>\n",
       "      <td>1</td>\n",
       "      <td>limousine</td>\n",
       "      <td>0.130432</td>\n",
       "      <td>False</td>\n",
       "      <td>tow_truck</td>\n",
       "      <td>0.029175</td>\n",
       "      <td>False</td>\n",
       "      <td>shopping_cart</td>\n",
       "      <td>0.026321</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2053</th>\n",
       "      <td>887705289381826560</td>\n",
       "      <td>https://pbs.twimg.com/media/DFHDQBbXgAEqY7t.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.821664</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.087582</td>\n",
       "      <td>True</td>\n",
       "      <td>Weimaraner</td>\n",
       "      <td>0.026236</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2054</th>\n",
       "      <td>888078434458587136</td>\n",
       "      <td>https://pbs.twimg.com/media/DFMWn56WsAAkA7B.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.995026</td>\n",
       "      <td>True</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.000932</td>\n",
       "      <td>True</td>\n",
       "      <td>bull_mastiff</td>\n",
       "      <td>0.000903</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2055</th>\n",
       "      <td>888202515573088257</td>\n",
       "      <td>https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.809197</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.054950</td>\n",
       "      <td>True</td>\n",
       "      <td>beagle</td>\n",
       "      <td>0.038915</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2056</th>\n",
       "      <td>888554962724278272</td>\n",
       "      <td>https://pbs.twimg.com/media/DFTH_O-UQAACu20.jpg</td>\n",
       "      <td>3</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.700377</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.166511</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.111411</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2057</th>\n",
       "      <td>888804989199671297</td>\n",
       "      <td>https://pbs.twimg.com/media/DFWra-3VYAA2piG.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.469760</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.184172</td>\n",
       "      <td>True</td>\n",
       "      <td>English_setter</td>\n",
       "      <td>0.073482</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>888917238123831296</td>\n",
       "      <td>https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.714719</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.120184</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.105506</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>889278841981685760</td>\n",
       "      <td>https://pbs.twimg.com/ext_tw_video_thumb/88927...</td>\n",
       "      <td>1</td>\n",
       "      <td>whippet</td>\n",
       "      <td>0.626152</td>\n",
       "      <td>True</td>\n",
       "      <td>borzoi</td>\n",
       "      <td>0.194742</td>\n",
       "      <td>True</td>\n",
       "      <td>Saluki</td>\n",
       "      <td>0.027351</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2060</th>\n",
       "      <td>889531135344209921</td>\n",
       "      <td>https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.953442</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.013834</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.007958</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2061</th>\n",
       "      <td>889638837579907072</td>\n",
       "      <td>https://pbs.twimg.com/media/DFihzFfXsAYGDPR.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.991650</td>\n",
       "      <td>True</td>\n",
       "      <td>boxer</td>\n",
       "      <td>0.002129</td>\n",
       "      <td>True</td>\n",
       "      <td>Staffordshire_bullterrier</td>\n",
       "      <td>0.001498</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2062</th>\n",
       "      <td>889665388333682689</td>\n",
       "      <td>https://pbs.twimg.com/media/DFi579UWsAAatzw.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.966327</td>\n",
       "      <td>True</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.027356</td>\n",
       "      <td>True</td>\n",
       "      <td>basenji</td>\n",
       "      <td>0.004633</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2063</th>\n",
       "      <td>889880896479866881</td>\n",
       "      <td>https://pbs.twimg.com/media/DFl99B1WsAITKsg.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.377417</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.151317</td>\n",
       "      <td>True</td>\n",
       "      <td>muzzle</td>\n",
       "      <td>0.082981</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2064</th>\n",
       "      <td>890006608113172480</td>\n",
       "      <td>https://pbs.twimg.com/media/DFnwSY4WAAAMliS.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.957979</td>\n",
       "      <td>True</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.013884</td>\n",
       "      <td>True</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.008167</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2065</th>\n",
       "      <td>890240255349198849</td>\n",
       "      <td>https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.511319</td>\n",
       "      <td>True</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.451038</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.029248</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>890609185150312448</td>\n",
       "      <td>https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.487574</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish_setter</td>\n",
       "      <td>0.193054</td>\n",
       "      <td>True</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.118184</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067</th>\n",
       "      <td>890729181411237888</td>\n",
       "      <td>https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.566142</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.178406</td>\n",
       "      <td>True</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.076507</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2068</th>\n",
       "      <td>890971913173991426</td>\n",
       "      <td>https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Appenzeller</td>\n",
       "      <td>0.341703</td>\n",
       "      <td>True</td>\n",
       "      <td>Border_collie</td>\n",
       "      <td>0.199287</td>\n",
       "      <td>True</td>\n",
       "      <td>ice_lolly</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2069</th>\n",
       "      <td>891087950875897856</td>\n",
       "      <td>https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chesapeake_Bay_retriever</td>\n",
       "      <td>0.425595</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.116317</td>\n",
       "      <td>True</td>\n",
       "      <td>Indian_elephant</td>\n",
       "      <td>0.076902</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2070</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.555712</td>\n",
       "      <td>True</td>\n",
       "      <td>English_springer</td>\n",
       "      <td>0.225770</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.175219</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>0.170278</td>\n",
       "      <td>False</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.168086</td>\n",
       "      <td>True</td>\n",
       "      <td>spatula</td>\n",
       "      <td>0.040836</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2072</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "      <td>True</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.078253</td>\n",
       "      <td>True</td>\n",
       "      <td>kelpie</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2073</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</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",
       "    <tr>\n",
       "      <th>2074</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.097049</td>\n",
       "      <td>False</td>\n",
       "      <td>bagel</td>\n",
       "      <td>0.085851</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>0.076110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2075 rows × 12 columns</p>\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",
       "5     666050758794694657    https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg   \n",
       "6     666051853826850816    https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg   \n",
       "7     666055525042405380    https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg   \n",
       "8     666057090499244032    https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg   \n",
       "9     666058600524156928    https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg   \n",
       "10    666063827256086533    https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg   \n",
       "11    666071193221509120    https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg   \n",
       "12    666073100786774016    https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg   \n",
       "13    666082916733198337    https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg   \n",
       "14    666094000022159362    https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpg   \n",
       "15    666099513787052032    https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg   \n",
       "16    666102155909144576    https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpg   \n",
       "17    666104133288665088    https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg   \n",
       "18    666268910803644416    https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg   \n",
       "19    666273097616637952    https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpg   \n",
       "20    666287406224695296    https://pbs.twimg.com/media/CT8g3BpUEAAuFjg.jpg   \n",
       "21    666293911632134144    https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg   \n",
       "22    666337882303524864    https://pbs.twimg.com/media/CT9OwFIWEAMuRje.jpg   \n",
       "23    666345417576210432    https://pbs.twimg.com/media/CT9Vn7PWoAA_ZCM.jpg   \n",
       "24    666353288456101888    https://pbs.twimg.com/media/CT9cx0tUEAAhNN_.jpg   \n",
       "25    666362758909284353    https://pbs.twimg.com/media/CT9lXGsUcAAyUFt.jpg   \n",
       "26    666373753744588802    https://pbs.twimg.com/media/CT9vZEYWUAAlZ05.jpg   \n",
       "27    666396247373291520    https://pbs.twimg.com/media/CT-D2ZHWIAA3gK1.jpg   \n",
       "28    666407126856765440    https://pbs.twimg.com/media/CT-NvwmW4AAugGZ.jpg   \n",
       "29    666411507551481857    https://pbs.twimg.com/media/CT-RugiWIAELEaq.jpg   \n",
       "...                  ...                                                ...   \n",
       "2045  886366144734445568    https://pbs.twimg.com/media/DE0BTnQUwAApKEH.jpg   \n",
       "2046  886680336477933568    https://pbs.twimg.com/media/DE4fEDzWAAAyHMM.jpg   \n",
       "2047  886736880519319552    https://pbs.twimg.com/media/DE5Se8FXcAAJFx4.jpg   \n",
       "2048  886983233522544640    https://pbs.twimg.com/media/DE8yicJW0AAAvBJ.jpg   \n",
       "2049  887101392804085760    https://pbs.twimg.com/media/DE-eAq6UwAA-jaE.jpg   \n",
       "2050  887343217045368832  https://pbs.twimg.com/ext_tw_video_thumb/88734...   \n",
       "2051  887473957103951883    https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg   \n",
       "2052  887517139158093824  https://pbs.twimg.com/ext_tw_video_thumb/88751...   \n",
       "2053  887705289381826560    https://pbs.twimg.com/media/DFHDQBbXgAEqY7t.jpg   \n",
       "2054  888078434458587136    https://pbs.twimg.com/media/DFMWn56WsAAkA7B.jpg   \n",
       "2055  888202515573088257    https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg   \n",
       "2056  888554962724278272    https://pbs.twimg.com/media/DFTH_O-UQAACu20.jpg   \n",
       "2057  888804989199671297    https://pbs.twimg.com/media/DFWra-3VYAA2piG.jpg   \n",
       "2058  888917238123831296    https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpg   \n",
       "2059  889278841981685760  https://pbs.twimg.com/ext_tw_video_thumb/88927...   \n",
       "2060  889531135344209921    https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg   \n",
       "2061  889638837579907072    https://pbs.twimg.com/media/DFihzFfXsAYGDPR.jpg   \n",
       "2062  889665388333682689    https://pbs.twimg.com/media/DFi579UWsAAatzw.jpg   \n",
       "2063  889880896479866881    https://pbs.twimg.com/media/DFl99B1WsAITKsg.jpg   \n",
       "2064  890006608113172480    https://pbs.twimg.com/media/DFnwSY4WAAAMliS.jpg   \n",
       "2065  890240255349198849    https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg   \n",
       "2066  890609185150312448    https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg   \n",
       "2067  890729181411237888    https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg   \n",
       "2068  890971913173991426    https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg   \n",
       "2069  891087950875897856    https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg   \n",
       "2070  891327558926688256    https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg   \n",
       "2071  891689557279858688    https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg   \n",
       "2072  891815181378084864    https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg   \n",
       "2073  892177421306343426    https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg   \n",
       "2074  892420643555336193    https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num                           p1   p1_conf  p1_dog  \\\n",
       "0           1       Welsh_springer_spaniel  0.465074    True   \n",
       "1           1                      redbone  0.506826    True   \n",
       "2           1              German_shepherd  0.596461    True   \n",
       "3           1          Rhodesian_ridgeback  0.408143    True   \n",
       "4           1           miniature_pinscher  0.560311    True   \n",
       "5           1         Bernese_mountain_dog  0.651137    True   \n",
       "6           1                   box_turtle  0.933012   False   \n",
       "7           1                         chow  0.692517    True   \n",
       "8           1                shopping_cart  0.962465   False   \n",
       "9           1             miniature_poodle  0.201493    True   \n",
       "10          1             golden_retriever  0.775930    True   \n",
       "11          1                Gordon_setter  0.503672    True   \n",
       "12          1                 Walker_hound  0.260857    True   \n",
       "13          1                          pug  0.489814    True   \n",
       "14          1                   bloodhound  0.195217    True   \n",
       "15          1                        Lhasa  0.582330    True   \n",
       "16          1               English_setter  0.298617    True   \n",
       "17          1                          hen  0.965932   False   \n",
       "18          1             desktop_computer  0.086502   False   \n",
       "19          1            Italian_greyhound  0.176053    True   \n",
       "20          1                  Maltese_dog  0.857531    True   \n",
       "21          1             three-toed_sloth  0.914671   False   \n",
       "22          1                           ox  0.416669   False   \n",
       "23          1             golden_retriever  0.858744    True   \n",
       "24          1                     malamute  0.336874    True   \n",
       "25          1                   guinea_pig  0.996496   False   \n",
       "26          1  soft-coated_wheaten_terrier  0.326467    True   \n",
       "27          1                    Chihuahua  0.978108    True   \n",
       "28          1      black-and-tan_coonhound  0.529139    True   \n",
       "29          1                         coho  0.404640   False   \n",
       "...       ...                          ...       ...     ...   \n",
       "2045        1               French_bulldog  0.999201    True   \n",
       "2046        1                  convertible  0.738995   False   \n",
       "2047        1                       kuvasz  0.309706    True   \n",
       "2048        2                    Chihuahua  0.793469    True   \n",
       "2049        1                      Samoyed  0.733942    True   \n",
       "2050        1             Mexican_hairless  0.330741    True   \n",
       "2051        2                     Pembroke  0.809197    True   \n",
       "2052        1                    limousine  0.130432   False   \n",
       "2053        1                       basset  0.821664    True   \n",
       "2054        1               French_bulldog  0.995026    True   \n",
       "2055        2                     Pembroke  0.809197    True   \n",
       "2056        3               Siberian_husky  0.700377    True   \n",
       "2057        1             golden_retriever  0.469760    True   \n",
       "2058        1             golden_retriever  0.714719    True   \n",
       "2059        1                      whippet  0.626152    True   \n",
       "2060        1             golden_retriever  0.953442    True   \n",
       "2061        1               French_bulldog  0.991650    True   \n",
       "2062        1                     Pembroke  0.966327    True   \n",
       "2063        1               French_bulldog  0.377417    True   \n",
       "2064        1                      Samoyed  0.957979    True   \n",
       "2065        1                     Pembroke  0.511319    True   \n",
       "2066        1                Irish_terrier  0.487574    True   \n",
       "2067        2                   Pomeranian  0.566142    True   \n",
       "2068        1                  Appenzeller  0.341703    True   \n",
       "2069        1     Chesapeake_Bay_retriever  0.425595    True   \n",
       "2070        2                       basset  0.555712    True   \n",
       "2071        1                  paper_towel  0.170278   False   \n",
       "2072        1                    Chihuahua  0.716012    True   \n",
       "2073        1                    Chihuahua  0.323581    True   \n",
       "2074        1                       orange  0.097049   False   \n",
       "\n",
       "                            p2   p2_conf  p2_dog                           p3  \\\n",
       "0                       collie  0.156665    True            Shetland_sheepdog   \n",
       "1           miniature_pinscher  0.074192    True          Rhodesian_ridgeback   \n",
       "2                     malinois  0.138584    True                   bloodhound   \n",
       "3                      redbone  0.360687    True           miniature_pinscher   \n",
       "4                   Rottweiler  0.243682    True                     Doberman   \n",
       "5             English_springer  0.263788    True   Greater_Swiss_Mountain_dog   \n",
       "6                   mud_turtle  0.045885   False                     terrapin   \n",
       "7              Tibetan_mastiff  0.058279    True                     fur_coat   \n",
       "8              shopping_basket  0.014594   False             golden_retriever   \n",
       "9                     komondor  0.192305    True  soft-coated_wheaten_terrier   \n",
       "10             Tibetan_mastiff  0.093718    True           Labrador_retriever   \n",
       "11           Yorkshire_terrier  0.174201    True                     Pekinese   \n",
       "12            English_foxhound  0.175382    True                 Ibizan_hound   \n",
       "13                bull_mastiff  0.404722    True               French_bulldog   \n",
       "14             German_shepherd  0.078260    True                     malinois   \n",
       "15                    Shih-Tzu  0.166192    True               Dandie_Dinmont   \n",
       "16                Newfoundland  0.149842    True                       borzoi   \n",
       "17                        cock  0.033919   False                    partridge   \n",
       "18                        desk  0.085547   False                     bookcase   \n",
       "19                 toy_terrier  0.111884    True                      basenji   \n",
       "20                  toy_poodle  0.063064    True             miniature_poodle   \n",
       "21                       otter  0.015250   False               great_grey_owl   \n",
       "22                Newfoundland  0.278407    True                  groenendael   \n",
       "23    Chesapeake_Bay_retriever  0.054787    True           Labrador_retriever   \n",
       "24              Siberian_husky  0.147655    True                   Eskimo_dog   \n",
       "25                       skunk  0.002402   False                      hamster   \n",
       "26                Afghan_hound  0.259551    True                       briard   \n",
       "27                 toy_terrier  0.009397    True                     papillon   \n",
       "28                  bloodhound  0.244220    True        flat-coated_retriever   \n",
       "29                  barracouta  0.271485   False                          gar   \n",
       "...                        ...       ...     ...                          ...   \n",
       "2045                 Chihuahua  0.000361    True                  Boston_bull   \n",
       "2046                sports_car  0.139952   False                    car_wheel   \n",
       "2047            Great_Pyrenees  0.186136    True               Dandie_Dinmont   \n",
       "2048               toy_terrier  0.143528    True                   can_opener   \n",
       "2049                Eskimo_dog  0.035029    True    Staffordshire_bullterrier   \n",
       "2050                  sea_lion  0.275645   False                   Weimaraner   \n",
       "2051       Rhodesian_ridgeback  0.054950    True                       beagle   \n",
       "2052                 tow_truck  0.029175   False                shopping_cart   \n",
       "2053                   redbone  0.087582    True                   Weimaraner   \n",
       "2054                       pug  0.000932    True                 bull_mastiff   \n",
       "2055       Rhodesian_ridgeback  0.054950    True                       beagle   \n",
       "2056                Eskimo_dog  0.166511    True                     malamute   \n",
       "2057        Labrador_retriever  0.184172    True               English_setter   \n",
       "2058           Tibetan_mastiff  0.120184    True           Labrador_retriever   \n",
       "2059                    borzoi  0.194742    True                       Saluki   \n",
       "2060        Labrador_retriever  0.013834    True                      redbone   \n",
       "2061                     boxer  0.002129    True    Staffordshire_bullterrier   \n",
       "2062                  Cardigan  0.027356    True                      basenji   \n",
       "2063        Labrador_retriever  0.151317    True                       muzzle   \n",
       "2064                Pomeranian  0.013884    True                         chow   \n",
       "2065                  Cardigan  0.451038    True                    Chihuahua   \n",
       "2066              Irish_setter  0.193054    True     Chesapeake_Bay_retriever   \n",
       "2067                Eskimo_dog  0.178406    True                     Pembroke   \n",
       "2068             Border_collie  0.199287    True                    ice_lolly   \n",
       "2069             Irish_terrier  0.116317    True              Indian_elephant   \n",
       "2070          English_springer  0.225770    True  German_short-haired_pointer   \n",
       "2071        Labrador_retriever  0.168086    True                      spatula   \n",
       "2072                  malamute  0.078253    True                       kelpie   \n",
       "2073                  Pekinese  0.090647    True                     papillon   \n",
       "2074                     bagel  0.085851   False                       banana   \n",
       "\n",
       "       p3_conf  p3_dog  \n",
       "0     0.061428    True  \n",
       "1     0.072010    True  \n",
       "2     0.116197    True  \n",
       "3     0.222752    True  \n",
       "4     0.154629    True  \n",
       "5     0.016199    True  \n",
       "6     0.017885   False  \n",
       "7     0.054449   False  \n",
       "8     0.007959    True  \n",
       "9     0.082086    True  \n",
       "10    0.072427    True  \n",
       "11    0.109454    True  \n",
       "12    0.097471    True  \n",
       "13    0.048960    True  \n",
       "14    0.075628    True  \n",
       "15    0.089688    True  \n",
       "16    0.133649    True  \n",
       "17    0.000052   False  \n",
       "18    0.079480   False  \n",
       "19    0.111152    True  \n",
       "20    0.025581    True  \n",
       "21    0.013207   False  \n",
       "22    0.102643    True  \n",
       "23    0.014241    True  \n",
       "24    0.093412    True  \n",
       "25    0.000461   False  \n",
       "26    0.206803    True  \n",
       "27    0.004577    True  \n",
       "28    0.173810    True  \n",
       "29    0.189945   False  \n",
       "...        ...     ...  \n",
       "2045  0.000076    True  \n",
       "2046  0.044173   False  \n",
       "2047  0.086346    True  \n",
       "2048  0.032253   False  \n",
       "2049  0.029705    True  \n",
       "2050  0.134203    True  \n",
       "2051  0.038915    True  \n",
       "2052  0.026321   False  \n",
       "2053  0.026236    True  \n",
       "2054  0.000903    True  \n",
       "2055  0.038915    True  \n",
       "2056  0.111411    True  \n",
       "2057  0.073482    True  \n",
       "2058  0.105506    True  \n",
       "2059  0.027351    True  \n",
       "2060  0.007958    True  \n",
       "2061  0.001498    True  \n",
       "2062  0.004633    True  \n",
       "2063  0.082981   False  \n",
       "2064  0.008167    True  \n",
       "2065  0.029248    True  \n",
       "2066  0.118184    True  \n",
       "2067  0.076507    True  \n",
       "2068  0.193548   False  \n",
       "2069  0.076902   False  \n",
       "2070  0.175219    True  \n",
       "2071  0.040836   False  \n",
       "2072  0.031379    True  \n",
       "2073  0.068957    True  \n",
       "2074  0.076110   False  \n",
       "\n",
       "[2075 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visually, I see three issues with the predictions. First, some predictions are not actually dogs. We should simply change those to \"None\" or null because we do not care whether the prediction identifies an orange, a bagel, a limousine, or anything else. We only want dogs. \n",
    "\n",
    "Second, the breed names are not standard. Some are capitalized, some are not, and there looks to be an underscore instead of a space between words. \n",
    "\n",
    "Third, we do not really need three sets of predictions with three columns each per record. I want to combine the dog breeds into one column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      "tweet_id    2075 non-null int64\n",
      "jpg_url     2075 non-null object\n",
      "img_num     2075 non-null int64\n",
      "p1          2075 non-null object\n",
      "p1_conf     2075 non-null float64\n",
      "p1_dog      2075 non-null bool\n",
      "p2          2075 non-null object\n",
      "p2_conf     2075 non-null float64\n",
      "p2_dog      2075 non-null bool\n",
      "p3          2075 non-null object\n",
      "p3_conf     2075 non-null float64\n",
      "p3_dog      2075 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(2), object(4)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_predictions.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the tweet_id needs to be converted to a string. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_predictions.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_predictions.duplicated('tweet_id'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicates here in df_predictions. Excellent!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>img_num</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p3_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2075.000000</td>\n",
       "      <td>2.075000e+03</td>\n",
       "      <td>2.075000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.384514e+17</td>\n",
       "      <td>1.203855</td>\n",
       "      <td>0.594548</td>\n",
       "      <td>1.345886e-01</td>\n",
       "      <td>6.032417e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.785203e+16</td>\n",
       "      <td>0.561875</td>\n",
       "      <td>0.271174</td>\n",
       "      <td>1.006657e-01</td>\n",
       "      <td>5.090593e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044333</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>1.740170e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.764835e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.364412</td>\n",
       "      <td>5.388625e-02</td>\n",
       "      <td>1.622240e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.119988e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588230</td>\n",
       "      <td>1.181810e-01</td>\n",
       "      <td>4.944380e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.932034e+17</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843855</td>\n",
       "      <td>1.955655e-01</td>\n",
       "      <td>9.180755e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.880140e-01</td>\n",
       "      <td>2.734190e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id      img_num      p1_conf       p2_conf       p3_conf\n",
       "count  2.075000e+03  2075.000000  2075.000000  2.075000e+03  2.075000e+03\n",
       "mean   7.384514e+17     1.203855     0.594548  1.345886e-01  6.032417e-02\n",
       "std    6.785203e+16     0.561875     0.271174  1.006657e-01  5.090593e-02\n",
       "min    6.660209e+17     1.000000     0.044333  1.011300e-08  1.740170e-10\n",
       "25%    6.764835e+17     1.000000     0.364412  5.388625e-02  1.622240e-02\n",
       "50%    7.119988e+17     1.000000     0.588230  1.181810e-01  4.944380e-02\n",
       "75%    7.932034e+17     1.000000     0.843855  1.955655e-01  9.180755e-02\n",
       "max    8.924206e+17     4.000000     1.000000  4.880140e-01  2.734190e-01"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predictions.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I don't see anything else concerning here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's look at **df_tweets**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>8853</td>\n",
       "      <td>39467</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>6514</td>\n",
       "      <td>33819</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>4328</td>\n",
       "      <td>25461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>8964</td>\n",
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       "      <th>4</th>\n",
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       "      <td>41048</td>\n",
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       "      <th>5</th>\n",
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       "      <td>3261</td>\n",
       "      <td>20562</td>\n",
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       "      <th>6</th>\n",
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       "      <td>2158</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>890240255349198849</td>\n",
       "      <td>7711</td>\n",
       "      <td>32467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>890006608113172480</td>\n",
       "      <td>7624</td>\n",
       "      <td>31166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>889880896479866881</td>\n",
       "      <td>5156</td>\n",
       "      <td>28268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>889665388333682689</td>\n",
       "      <td>8538</td>\n",
       "      <td>38818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>889638837579907072</td>\n",
       "      <td>4735</td>\n",
       "      <td>27672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>889531135344209921</td>\n",
       "      <td>2321</td>\n",
       "      <td>15359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>889278841981685760</td>\n",
       "      <td>5637</td>\n",
       "      <td>25652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>888917238123831296</td>\n",
       "      <td>4709</td>\n",
       "      <td>29611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>888804989199671297</td>\n",
       "      <td>4559</td>\n",
       "      <td>26080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>888554962724278272</td>\n",
       "      <td>3732</td>\n",
       "      <td>20290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>888078434458587136</td>\n",
       "      <td>3653</td>\n",
       "      <td>22201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>887705289381826560</td>\n",
       "      <td>5609</td>\n",
       "      <td>30779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>887517139158093824</td>\n",
       "      <td>12082</td>\n",
       "      <td>46959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>887473957103951883</td>\n",
       "      <td>18781</td>\n",
       "      <td>69871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>887343217045368832</td>\n",
       "      <td>10737</td>\n",
       "      <td>34222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>887101392804085760</td>\n",
       "      <td>6167</td>\n",
       "      <td>31061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>886983233522544640</td>\n",
       "      <td>8084</td>\n",
       "      <td>35859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>886736880519319552</td>\n",
       "      <td>3443</td>\n",
       "      <td>12306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>886680336477933568</td>\n",
       "      <td>4610</td>\n",
       "      <td>22798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>886366144734445568</td>\n",
       "      <td>3316</td>\n",
       "      <td>21524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>886267009285017600</td>\n",
       "      <td>4</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2324</th>\n",
       "      <td>666411507551481857</td>\n",
       "      <td>339</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>666407126856765440</td>\n",
       "      <td>44</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>666396247373291520</td>\n",
       "      <td>92</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>666373753744588802</td>\n",
       "      <td>100</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>666362758909284353</td>\n",
       "      <td>595</td>\n",
       "      <td>804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2329</th>\n",
       "      <td>666353288456101888</td>\n",
       "      <td>77</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>666345417576210432</td>\n",
       "      <td>146</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2331</th>\n",
       "      <td>666337882303524864</td>\n",
       "      <td>96</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2332</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>368</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>666287406224695296</td>\n",
       "      <td>71</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>666273097616637952</td>\n",
       "      <td>82</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>666268910803644416</td>\n",
       "      <td>37</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2336</th>\n",
       "      <td>666104133288665088</td>\n",
       "      <td>6871</td>\n",
       "      <td>14765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>666102155909144576</td>\n",
       "      <td>16</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2338</th>\n",
       "      <td>666099513787052032</td>\n",
       "      <td>73</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339</th>\n",
       "      <td>666094000022159362</td>\n",
       "      <td>79</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2340</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>47</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2341</th>\n",
       "      <td>666073100786774016</td>\n",
       "      <td>174</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2342</th>\n",
       "      <td>666071193221509120</td>\n",
       "      <td>67</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2343</th>\n",
       "      <td>666063827256086533</td>\n",
       "      <td>232</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2344</th>\n",
       "      <td>666058600524156928</td>\n",
       "      <td>61</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2345</th>\n",
       "      <td>666057090499244032</td>\n",
       "      <td>146</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2346</th>\n",
       "      <td>666055525042405380</td>\n",
       "      <td>261</td>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>666051853826850816</td>\n",
       "      <td>879</td>\n",
       "      <td>1253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>666050758794694657</td>\n",
       "      <td>60</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>41</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>147</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>47</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>48</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>532</td>\n",
       "      <td>2535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2354 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  retweet_count  favorite_count\n",
       "0     892420643555336193           8853           39467\n",
       "1     892177421306343426           6514           33819\n",
       "2     891815181378084864           4328           25461\n",
       "3     891689557279858688           8964           42908\n",
       "4     891327558926688256           9774           41048\n",
       "5     891087950875897856           3261           20562\n",
       "6     890971913173991426           2158           12041\n",
       "7     890729181411237888          16716           56848\n",
       "8     890609185150312448           4429           28226\n",
       "9     890240255349198849           7711           32467\n",
       "10    890006608113172480           7624           31166\n",
       "11    889880896479866881           5156           28268\n",
       "12    889665388333682689           8538           38818\n",
       "13    889638837579907072           4735           27672\n",
       "14    889531135344209921           2321           15359\n",
       "15    889278841981685760           5637           25652\n",
       "16    888917238123831296           4709           29611\n",
       "17    888804989199671297           4559           26080\n",
       "18    888554962724278272           3732           20290\n",
       "19    888078434458587136           3653           22201\n",
       "20    887705289381826560           5609           30779\n",
       "21    887517139158093824          12082           46959\n",
       "22    887473957103951883          18781           69871\n",
       "23    887343217045368832          10737           34222\n",
       "24    887101392804085760           6167           31061\n",
       "25    886983233522544640           8084           35859\n",
       "26    886736880519319552           3443           12306\n",
       "27    886680336477933568           4610           22798\n",
       "28    886366144734445568           3316           21524\n",
       "29    886267009285017600              4             117\n",
       "...                  ...            ...             ...\n",
       "2324  666411507551481857            339             459\n",
       "2325  666407126856765440             44             113\n",
       "2326  666396247373291520             92             172\n",
       "2327  666373753744588802            100             194\n",
       "2328  666362758909284353            595             804\n",
       "2329  666353288456101888             77             229\n",
       "2330  666345417576210432            146             307\n",
       "2331  666337882303524864             96             204\n",
       "2332  666293911632134144            368             522\n",
       "2333  666287406224695296             71             152\n",
       "2334  666273097616637952             82             184\n",
       "2335  666268910803644416             37             108\n",
       "2336  666104133288665088           6871           14765\n",
       "2337  666102155909144576             16              81\n",
       "2338  666099513787052032             73             164\n",
       "2339  666094000022159362             79             169\n",
       "2340  666082916733198337             47             121\n",
       "2341  666073100786774016            174             335\n",
       "2342  666071193221509120             67             154\n",
       "2343  666063827256086533            232             496\n",
       "2344  666058600524156928             61             115\n",
       "2345  666057090499244032            146             304\n",
       "2346  666055525042405380            261             448\n",
       "2347  666051853826850816            879            1253\n",
       "2348  666050758794694657             60             136\n",
       "2349  666049248165822465             41             111\n",
       "2350  666044226329800704            147             311\n",
       "2351  666033412701032449             47             128\n",
       "2352  666029285002620928             48             132\n",
       "2353  666020888022790149            532            2535\n",
       "\n",
       "[2354 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      "tweet_id          2354 non-null int64\n",
      "retweet_count     2354 non-null int64\n",
      "favorite_count    2354 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 55.2 KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_tweets.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_tweets.duplicated('tweet_id'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.354000e+03</td>\n",
       "      <td>2354.000000</td>\n",
       "      <td>2354.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.426978e+17</td>\n",
       "      <td>3164.797366</td>\n",
       "      <td>8080.968564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.852812e+16</td>\n",
       "      <td>5284.770364</td>\n",
       "      <td>11814.771334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.660209e+17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.783975e+17</td>\n",
       "      <td>624.500000</td>\n",
       "      <td>1415.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.194596e+17</td>\n",
       "      <td>1473.500000</td>\n",
       "      <td>3603.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.993058e+17</td>\n",
       "      <td>3652.000000</td>\n",
       "      <td>10122.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.924206e+17</td>\n",
       "      <td>79515.000000</td>\n",
       "      <td>132810.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tweet_id  retweet_count  favorite_count\n",
       "count  2.354000e+03    2354.000000     2354.000000\n",
       "mean   7.426978e+17    3164.797366     8080.968564\n",
       "std    6.852812e+16    5284.770364    11814.771334\n",
       "min    6.660209e+17       0.000000        0.000000\n",
       "25%    6.783975e+17     624.500000     1415.000000\n",
       "50%    7.194596e+17    1473.500000     3603.500000\n",
       "75%    7.993058e+17    3652.000000    10122.250000\n",
       "max    8.924206e+17   79515.000000   132810.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweets.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only thing I see in df_tweets itself is that tweet_id again. It's an integer, but really needs to be a string. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality issues\n",
    "1. Remove retweets, replies, and tweets without images (per project instructions).\n",
    "\n",
    "2. The timestamp column in df_archive is a string and should be a datetime.\n",
    "\n",
    "3. The name column in df_archive contains many lowercase \"names\" that are not actually names. We also should change the \"None\" names to nulls.\n",
    "\n",
    "4. 0's in df_archive rating_numerator and rating_denominator columns may cause issues in our analysis.\n",
    "\n",
    "5. df_predictions includes predictions that are not actual dogs, such as orange, bagel, etc. \n",
    "\n",
    "6. Non-standardized breed names in df_predictions. Fix capitalization and underscore/space issue.\n",
    "\n",
    "7. Redundant prediction columns in df_predictions. We can combine the dog breeds into one column.\n",
    "\n",
    "8. Convert tweet_id from an integer to a string. \n",
    "\n",
    "9. Large rating_denominator values in df_archive may be able to be normalized to 10 or simply dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 7,
        "hidden": false,
        "row": 40,
        "width": 12
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "### Tidiness issues\n",
    "1. In the df_archive table, doggo, floofer, pupper, and puppo are categories of dog. These should be in one column, not four different columns.\n",
    "\n",
    "2. Data should be combined into one dataframe because there is only one observational unit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 32,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## Cleaning Data\n",
    "In this section, 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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of original pieces of data\n",
    "df_archive_clean = df_archive.copy()\n",
    "df_predictions_clean = df_predictions.copy()\n",
    "df_tweets_clean = df_tweets.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note:* I will not be cleaning the data in the order above! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #1: The tweet_id is an integer in all tables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Convert tweet_id to a string as we will not be doing calculations with this identifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_clean.tweet_id = df_archive_clean.tweet_id.astype(str)\n",
    "df_predictions_clean.tweet_id = df_predictions_clean.tweet_id.astype(str)\n",
    "df_tweets_clean.tweet_id = df_tweets_clean.tweet_id.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      2356 non-null object\n",
      "in_reply_to_status_id         78 non-null float64\n",
      "in_reply_to_user_id           78 non-null float64\n",
      "timestamp                     2356 non-null object\n",
      "source                        2356 non-null object\n",
      "text                          2356 non-null object\n",
      "retweeted_status_id           181 non-null float64\n",
      "retweeted_status_user_id      181 non-null float64\n",
      "retweeted_status_timestamp    181 non-null object\n",
      "expanded_urls                 2297 non-null object\n",
      "rating_numerator              2356 non-null int64\n",
      "rating_denominator            2356 non-null int64\n",
      "name                          2356 non-null object\n",
      "doggo                         2356 non-null object\n",
      "floofer                       2356 non-null object\n",
      "pupper                        2356 non-null object\n",
      "puppo                         2356 non-null object\n",
      "dtypes: float64(4), int64(2), object(11)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_archive_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 12 columns):\n",
      "tweet_id    2075 non-null object\n",
      "jpg_url     2075 non-null object\n",
      "img_num     2075 non-null int64\n",
      "p1          2075 non-null object\n",
      "p1_conf     2075 non-null float64\n",
      "p1_dog      2075 non-null bool\n",
      "p2          2075 non-null object\n",
      "p2_conf     2075 non-null float64\n",
      "p2_dog      2075 non-null bool\n",
      "p3          2075 non-null object\n",
      "p3_conf     2075 non-null float64\n",
      "p3_dog      2075 non-null bool\n",
      "dtypes: bool(3), float64(3), int64(1), object(5)\n",
      "memory usage: 152.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_predictions_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2354 entries, 0 to 2353\n",
      "Data columns (total 3 columns):\n",
      "tweet_id          2354 non-null object\n",
      "retweet_count     2354 non-null int64\n",
      "favorite_count    2354 non-null int64\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 55.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweets_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #2: The timestamp column in df_archive is a string."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "#### Define: Convert timestamp column to datetime. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_clean.timestamp = df_archive_clean.timestamp.astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      2356 non-null object\n",
      "in_reply_to_status_id         78 non-null float64\n",
      "in_reply_to_user_id           78 non-null float64\n",
      "timestamp                     2356 non-null datetime64[ns]\n",
      "source                        2356 non-null object\n",
      "text                          2356 non-null object\n",
      "retweeted_status_id           181 non-null float64\n",
      "retweeted_status_user_id      181 non-null float64\n",
      "retweeted_status_timestamp    181 non-null object\n",
      "expanded_urls                 2297 non-null object\n",
      "rating_numerator              2356 non-null int64\n",
      "rating_denominator            2356 non-null int64\n",
      "name                          2356 non-null object\n",
      "doggo                         2356 non-null object\n",
      "floofer                       2356 non-null object\n",
      "pupper                        2356 non-null object\n",
      "puppo                         2356 non-null object\n",
      "dtypes: datetime64[ns](1), float64(4), int64(2), object(10)\n",
      "memory usage: 313.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #3: In the df_archive table, doggo, floofer, pupper, and puppo are four categories of dog in four columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Combine all dog stage columns into one column. If a record has two or more dog stages, we will use the first stage encountered in the following order: doggo, floofer, pupper, puppo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_clean.loc[df_archive_clean['doggo'] == 'doggo', 'dog_stage'] = 'doggo'\n",
    "df_archive_clean.loc[df_archive_clean['floofer'] == 'floofer', 'dog_stage'] = 'floofer'\n",
    "df_archive_clean.loc[df_archive_clean['pupper'] == 'pupper', 'dog_stage'] = 'pupper'\n",
    "df_archive_clean.loc[df_archive_clean['puppo'] == 'puppo', 'dog_stage'] = 'puppo'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if this worked on a sample before we drop the original columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pupper     257\n",
       "doggo       83\n",
       "puppo       30\n",
       "floofer     10\n",
       "Name: dog_stage, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.dog_stage.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 18 columns):\n",
      "tweet_id                      2356 non-null object\n",
      "in_reply_to_status_id         78 non-null float64\n",
      "in_reply_to_user_id           78 non-null float64\n",
      "timestamp                     2356 non-null datetime64[ns]\n",
      "source                        2356 non-null object\n",
      "text                          2356 non-null object\n",
      "retweeted_status_id           181 non-null float64\n",
      "retweeted_status_user_id      181 non-null float64\n",
      "retweeted_status_timestamp    181 non-null object\n",
      "expanded_urls                 2297 non-null object\n",
      "rating_numerator              2356 non-null int64\n",
      "rating_denominator            2356 non-null int64\n",
      "name                          2356 non-null object\n",
      "doggo                         2356 non-null object\n",
      "floofer                       2356 non-null object\n",
      "pupper                        2356 non-null object\n",
      "puppo                         2356 non-null object\n",
      "dog_stage                     380 non-null object\n",
      "dtypes: datetime64[ns](1), float64(4), int64(2), object(11)\n",
      "memory usage: 331.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good. Let's drop those old columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_clean = df_archive_clean.drop(['doggo', 'floofer', 'pupper', 'puppo'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2356 entries, 0 to 2355\n",
      "Data columns (total 14 columns):\n",
      "tweet_id                      2356 non-null object\n",
      "in_reply_to_status_id         78 non-null float64\n",
      "in_reply_to_user_id           78 non-null float64\n",
      "timestamp                     2356 non-null datetime64[ns]\n",
      "source                        2356 non-null object\n",
      "text                          2356 non-null object\n",
      "retweeted_status_id           181 non-null float64\n",
      "retweeted_status_user_id      181 non-null float64\n",
      "retweeted_status_timestamp    181 non-null object\n",
      "expanded_urls                 2297 non-null object\n",
      "rating_numerator              2356 non-null int64\n",
      "rating_denominator            2356 non-null int64\n",
      "name                          2356 non-null object\n",
      "dog_stage                     380 non-null object\n",
      "dtypes: datetime64[ns](1), float64(4), int64(2), object(7)\n",
      "memory usage: 257.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_archive_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" 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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" 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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" 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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" 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>NaN</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                                             source  \\\n",
       "0 2017-08-01 16:23:56  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1 2017-08-01 00:17:27  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2 2017-07-31 00:18:03  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3 2017-07-30 15:58:51  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4 2017-07-29 16:00:24  <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 dog_stage  \n",
       "0                  10   Phineas       NaN  \n",
       "1                  10     Tilly       NaN  \n",
       "2                  10    Archie       NaN  \n",
       "3                  10     Darla       NaN  \n",
       "4                  10  Franklin       NaN  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #4: Remove retweets, replies, and tweets without images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Per the project instructions, we are only concerned with original tweets. We do not want to see retweets, replies, or tweets without images. How can you rate a dog if you don't have a picture?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop tweets where expanded_urls is null\n",
    "df_archive_clean = df_archive_clean.dropna(subset=['expanded_urls'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop replies (in_reply_to_status_id is not null)\n",
    "df_archive_clean = df_archive_clean[df_archive_clean['in_reply_to_status_id'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop retweets (retweeted_status_id is not null)\n",
    "df_archive_clean = df_archive_clean[df_archive_clean['retweeted_status_id'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2094 entries, 0 to 2355\n",
      "Data columns (total 14 columns):\n",
      "tweet_id                      2094 non-null object\n",
      "in_reply_to_status_id         0 non-null float64\n",
      "in_reply_to_user_id           0 non-null float64\n",
      "timestamp                     2094 non-null datetime64[ns]\n",
      "source                        2094 non-null object\n",
      "text                          2094 non-null object\n",
      "retweeted_status_id           0 non-null float64\n",
      "retweeted_status_user_id      0 non-null float64\n",
      "retweeted_status_timestamp    0 non-null object\n",
      "expanded_urls                 2094 non-null object\n",
      "rating_numerator              2094 non-null int64\n",
      "rating_denominator            2094 non-null int64\n",
      "name                          2094 non-null object\n",
      "dog_stage                     335 non-null object\n",
      "dtypes: datetime64[ns](1), float64(4), int64(2), object(7)\n",
      "memory usage: 245.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#Check it out before we continue\n",
    "df_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last step to clean up this issue is to drop the columns we now do not need. Namely, in_reply_to_status_id, in_reply_to_user_id, retweeted_status_id, retweeted_status_user_id, and retweeted_status_timestamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_clean = df_archive_clean.drop(['in_reply_to_status_id', 'in_reply_to_user_id', 'retweeted_status_id', 'retweeted_status_user_id', 'retweeted_status_timestamp'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2094 entries, 0 to 2355\n",
      "Data columns (total 9 columns):\n",
      "tweet_id              2094 non-null object\n",
      "timestamp             2094 non-null datetime64[ns]\n",
      "source                2094 non-null object\n",
      "text                  2094 non-null object\n",
      "expanded_urls         2094 non-null object\n",
      "rating_numerator      2094 non-null int64\n",
      "rating_denominator    2094 non-null int64\n",
      "name                  2094 non-null object\n",
      "dog_stage             335 non-null object\n",
      "dtypes: datetime64[ns](1), int64(2), object(6)\n",
      "memory usage: 163.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #5: Lowercase \"names\" and 'None' name values in df_archive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: The name column contains some lowercase \"names\" that are not really names. We need to change them to None. Finally, I would prefer to change the None values to nulls."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None            600\n",
       "a                55\n",
       "Charlie          11\n",
       "Lucy             11\n",
       "Oliver           10\n",
       "Cooper           10\n",
       "Tucker            9\n",
       "Penny             9\n",
       "Winston           8\n",
       "the               8\n",
       "Sadie             8\n",
       "Lola              8\n",
       "Daisy             7\n",
       "Toby              7\n",
       "Koda              6\n",
       "Bella             6\n",
       "an                6\n",
       "Bo                6\n",
       "Stanley           6\n",
       "Bailey            6\n",
       "Jax               6\n",
       "Oscar             6\n",
       "Leo               5\n",
       "Milo              5\n",
       "Scout             5\n",
       "Chester           5\n",
       "Buddy             5\n",
       "Rusty             5\n",
       "Bentley           5\n",
       "Dave              5\n",
       "               ... \n",
       "Snoopy            1\n",
       "Bobbay            1\n",
       "Ronduh            1\n",
       "Betty             1\n",
       "Kuyu              1\n",
       "Rodney            1\n",
       "Livvie            1\n",
       "Mutt              1\n",
       "Stuart            1\n",
       "Ralph             1\n",
       "Tiger             1\n",
       "Severus           1\n",
       "Bobby             1\n",
       "Lilli             1\n",
       "Brooks            1\n",
       "Birf              1\n",
       "Aqua              1\n",
       "Noah              1\n",
       "Shooter           1\n",
       "Lili              1\n",
       "Caryl             1\n",
       "Jazzy             1\n",
       "Alexanderson      1\n",
       "Bradley           1\n",
       "Barry             1\n",
       "Luther            1\n",
       "Wishes            1\n",
       "Taco              1\n",
       "Kollin            1\n",
       "Willow            1\n",
       "Name: name, Length: 955, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check out the name column so we do not have to scroll all the way back to the top\n",
    "df_archive_clean.name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change all lowercase \"names\" to 'None'.\n",
    "df_archive_clean.name = df_archive_clean.name.str.replace('^[a-z]+', 'None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change 'None' to null\n",
    "df_archive_clean['name'].replace('None', np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Charlie         11\n",
       "Lucy            11\n",
       "Cooper          10\n",
       "Oliver          10\n",
       "Penny            9\n",
       "Tucker           9\n",
       "Sadie            8\n",
       "Winston          8\n",
       "Lola             8\n",
       "Toby             7\n",
       "Daisy            7\n",
       "Bo               6\n",
       "Oscar            6\n",
       "Stanley          6\n",
       "Bella            6\n",
       "Jax              6\n",
       "Bailey           6\n",
       "Koda             6\n",
       "Scout            5\n",
       "Chester          5\n",
       "Bentley          5\n",
       "Milo             5\n",
       "Leo              5\n",
       "Buddy            5\n",
       "Rusty            5\n",
       "Dave             5\n",
       "Louis            5\n",
       "Maggie           4\n",
       "Oakley           4\n",
       "Chip             4\n",
       "                ..\n",
       "Monkey           1\n",
       "Hamrick          1\n",
       "Millie           1\n",
       "Damon            1\n",
       "Kollin           1\n",
       "Wishes           1\n",
       "Moofasa          1\n",
       "Luther           1\n",
       "Kenny            1\n",
       "Brutus           1\n",
       "Rupert           1\n",
       "Sweets           1\n",
       "Tupawc           1\n",
       "Stuart           1\n",
       "Ralph            1\n",
       "Tiger            1\n",
       "Severus          1\n",
       "Bobby            1\n",
       "Lilli            1\n",
       "Brooks           1\n",
       "Birf             1\n",
       "Aqua             1\n",
       "Noah             1\n",
       "Shooter          1\n",
       "Lili             1\n",
       "Caryl            1\n",
       "Jazzy            1\n",
       "Alexanderson     1\n",
       "Bradley          1\n",
       "Lacy             1\n",
       "Name: name, Length: 929, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2094 entries, 0 to 2355\n",
      "Data columns (total 9 columns):\n",
      "tweet_id              2094 non-null object\n",
      "timestamp             2094 non-null datetime64[ns]\n",
      "source                2094 non-null object\n",
      "text                  2094 non-null object\n",
      "expanded_urls         2094 non-null object\n",
      "rating_numerator      2094 non-null int64\n",
      "rating_denominator    2094 non-null int64\n",
      "name                  1390 non-null object\n",
      "dog_stage             335 non-null object\n",
      "dtypes: datetime64[ns](1), int64(2), object(6)\n",
      "memory usage: 163.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #6: 0's in df_archive rating_numerator and rating_denominator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: There are 0's in the rating_numerator and rating_denominator columns. Having a 0 denominator will cause problems with any calculations we may want to perform. The 0 in the numerator might be fine, but I find it hard to believe that any dog could ever be rated a 0. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, timestamp, source, text, expanded_urls, rating_numerator, rating_denominator, name, dog_stage]\n",
       "Index: []"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find records with 0 in the denominator\n",
    "df_archive_clean.loc[df_archive_clean['rating_denominator'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like we solved this while working on a previous issue! Great!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>835152434251116546</td>\n",
       "      <td>2017-02-24 15:40:31</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>When you're so blinded by your systematic plag...</td>\n",
       "      <td>https://twitter.com/dog_rates/status/835152434...</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id           timestamp  \\\n",
       "315  835152434251116546 2017-02-24 15:40:31   \n",
       "\n",
       "                                                source  \\\n",
       "315  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                  text  \\\n",
       "315  When you're so blinded by your systematic plag...   \n",
       "\n",
       "                                         expanded_urls  rating_numerator  \\\n",
       "315  https://twitter.com/dog_rates/status/835152434...                 0   \n",
       "\n",
       "     rating_denominator name dog_stage  \n",
       "315                  10  NaN       NaN  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find records with 0 in the denominator\n",
    "df_archive_clean.loc[df_archive_clean['rating_numerator'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adjust column width so I can read the text better. \n",
    "pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>835152434251116546</td>\n",
       "      <td>2017-02-24 15:40:31</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag</td>\n",
       "      <td>https://twitter.com/dog_rates/status/835152434251116546/photo/1,https://twitter.com/dog_rates/status/835152434251116546/photo/1,https://twitter.com/dog_rates/status/835152434251116546/photo/1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tweet_id           timestamp  \\\n",
       "315  835152434251116546 2017-02-24 15:40:31   \n",
       "\n",
       "                                                                                 source  \\\n",
       "315  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                  text  \\\n",
       "315  When you're so blinded by your systematic plagiarism that you forget what day it is. 0/10 https://t.co/YbEJPkg4Ag   \n",
       "\n",
       "                                                                                                                                                                                       expanded_urls  \\\n",
       "315  https://twitter.com/dog_rates/status/835152434251116546/photo/1,https://twitter.com/dog_rates/status/835152434251116546/photo/1,https://twitter.com/dog_rates/status/835152434251116546/photo/1   \n",
       "\n",
       "     rating_numerator  rating_denominator name dog_stage  \n",
       "315  0                 10                  NaN  NaN       "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.loc[df_archive_clean['rating_numerator'] == 0]"
   ]
  },
  {
   "attachments": {
    "Screenshot%202023-08-22%20164828.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah, the We Rate Dogs owner is calling out somebody who stole their photo. \n",
    "![Screenshot%202023-08-22%20164828.png](attachment:Screenshot%202023-08-22%20164828.png)\n",
    "We can just drop this row. It is not relevant, and now we know why they rated something 0/10. The dog definitely isn't a 0!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_clean = df_archive_clean.drop([315], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, timestamp, source, text, expanded_urls, rating_numerator, rating_denominator, name, dog_stage]\n",
       "Index: []"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.loc[df_archive_clean['rating_numerator'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #7: Large rating_denominator values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Values greater than 10 in the rating_denominator column in df_archive might be errors. We may be able to normalize them to 10 or simply drop them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2076\n",
       "50     3   \n",
       "11     2   \n",
       "80     2   \n",
       "7      1   \n",
       "170    1   \n",
       "150    1   \n",
       "120    1   \n",
       "110    1   \n",
       "90     1   \n",
       "70     1   \n",
       "40     1   \n",
       "20     1   \n",
       "2      1   \n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see exactly what values we still have in df_archive.\n",
    "df_archive_clean.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We only have 17 records with a denominator not equal to 10. We will drop them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_archive_clean = df_archive_clean[df_archive_clean.rating_denominator == 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    2076\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_archive_clean.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2076 entries, 0 to 2355\n",
      "Data columns (total 9 columns):\n",
      "tweet_id              2076 non-null object\n",
      "timestamp             2076 non-null datetime64[ns]\n",
      "source                2076 non-null object\n",
      "text                  2076 non-null object\n",
      "expanded_urls         2076 non-null object\n",
      "rating_numerator      2076 non-null int64\n",
      "rating_denominator    2076 non-null int64\n",
      "name                  1387 non-null object\n",
      "dog_stage             335 non-null object\n",
      "dtypes: datetime64[ns](1), int64(2), object(6)\n",
      "memory usage: 162.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #8 and #9: Redundant prediction columns in df_predictions & predictions that are not actual dogs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: I am combining issues 8 and 9 here because in the process of combining the redundant prediction columns, the predictions that are not actual dogs are going to be adjusted to \"Not a dog\". This is due to using the boolean to choose which dog breed to assign. A value of False will default to \"Not a dog\" and will solve issue #9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "dog_breed = []\n",
    "confidence = []\n",
    "\n",
    "#Create a function that will choose the dog breed based on confidence\n",
    "def prediction(row):\n",
    "    if row['p1_dog'] == True:\n",
    "        dog_breed.append(row['p1'])\n",
    "        confidence.append(row['p1_conf'])\n",
    "    elif row['p2_dog'] == True:\n",
    "        dog_breed.append(row['p2'])\n",
    "        confidence.append(row['p2_conf'])\n",
    "    elif row['p3_dog'] == True:\n",
    "        dog_breed.append(row['p3'])\n",
    "        confidence.append(row['p3_conf'])\n",
    "    else:\n",
    "        dog_breed.append('Not a Dog')\n",
    "        confidence.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use pandas apply by columns to call the function and then add columns to df_predictions\n",
    "df_predictions_clean.apply(prediction, axis=1)\n",
    "\n",
    "df_predictions_clean['dog_breed'] = dog_breed\n",
    "df_predictions_clean['confidence'] = confidence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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",
       "      <th>dog_breed</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>724046343203856385</td>\n",
       "      <td>https://pbs.twimg.com/media/CgxUTS_XEAAC0pv.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>boxer</td>\n",
       "      <td>0.826272</td>\n",
       "      <td>True</td>\n",
       "      <td>bull_mastiff</td>\n",
       "      <td>0.158595</td>\n",
       "      <td>True</td>\n",
       "      <td>Great_Dane</td>\n",
       "      <td>0.011859</td>\n",
       "      <td>True</td>\n",
       "      <td>boxer</td>\n",
       "      <td>0.826272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>682406705142087680</td>\n",
       "      <td>https://pbs.twimg.com/media/CXhlRmRUMAIYoFO.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>wombat</td>\n",
       "      <td>0.709344</td>\n",
       "      <td>False</td>\n",
       "      <td>koala</td>\n",
       "      <td>0.169758</td>\n",
       "      <td>False</td>\n",
       "      <td>beaver</td>\n",
       "      <td>0.079433</td>\n",
       "      <td>False</td>\n",
       "      <td>Not a Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>752173152931807232</td>\n",
       "      <td>https://pbs.twimg.com/media/CnBBfNuWcAAkOgO.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.527659</td>\n",
       "      <td>True</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.174765</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.045525</td>\n",
       "      <td>True</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.527659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>882627270321602560</td>\n",
       "      <td>https://pbs.twimg.com/media/DD-40X3WAAAJPU5.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.542982</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.251988</td>\n",
       "      <td>True</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.107699</td>\n",
       "      <td>True</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.542982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>705428427625635840</td>\n",
       "      <td>https://pbs.twimg.com/media/CcovaMUXIAApFDl.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.774792</td>\n",
       "      <td>True</td>\n",
       "      <td>quilt</td>\n",
       "      <td>0.073079</td>\n",
       "      <td>False</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.022365</td>\n",
       "      <td>True</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.774792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>674638615994089473</td>\n",
       "      <td>https://pbs.twimg.com/media/CVzMPh1UsAELQ_p.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.846986</td>\n",
       "      <td>True</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.142014</td>\n",
       "      <td>True</td>\n",
       "      <td>keeshond</td>\n",
       "      <td>0.002605</td>\n",
       "      <td>True</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.846986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>671485057807351808</td>\n",
       "      <td>https://pbs.twimg.com/media/CVGYGNYXAAAQ9m-.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.627901</td>\n",
       "      <td>True</td>\n",
       "      <td>Great_Pyrenees</td>\n",
       "      <td>0.276421</td>\n",
       "      <td>True</td>\n",
       "      <td>kuvasz</td>\n",
       "      <td>0.057874</td>\n",
       "      <td>True</td>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.627901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>728387165835677696</td>\n",
       "      <td>https://pbs.twimg.com/media/ChvAQuMWMAAVaKD.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.266414</td>\n",
       "      <td>True</td>\n",
       "      <td>Great_Pyrenees</td>\n",
       "      <td>0.138546</td>\n",
       "      <td>True</td>\n",
       "      <td>keeshond</td>\n",
       "      <td>0.109014</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.266414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>711652651650457602</td>\n",
       "      <td>https://pbs.twimg.com/media/CeBMT6-WIAA7Qqf.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>llama</td>\n",
       "      <td>0.856789</td>\n",
       "      <td>False</td>\n",
       "      <td>Arabian_camel</td>\n",
       "      <td>0.098727</td>\n",
       "      <td>False</td>\n",
       "      <td>neck_brace</td>\n",
       "      <td>0.016377</td>\n",
       "      <td>False</td>\n",
       "      <td>Not a Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>692894228850999298</td>\n",
       "      <td>https://pbs.twimg.com/media/CZ2nn7BUsAI2Pj3.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.876977</td>\n",
       "      <td>True</td>\n",
       "      <td>bluetick</td>\n",
       "      <td>0.036615</td>\n",
       "      <td>True</td>\n",
       "      <td>basset</td>\n",
       "      <td>0.017848</td>\n",
       "      <td>True</td>\n",
       "      <td>German_short-haired_pointer</td>\n",
       "      <td>0.876977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>673697980713705472</td>\n",
       "      <td>https://pbs.twimg.com/media/CVl0vFeWoAAMTfg.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>porcupine</td>\n",
       "      <td>0.151876</td>\n",
       "      <td>False</td>\n",
       "      <td>hen</td>\n",
       "      <td>0.111380</td>\n",
       "      <td>False</td>\n",
       "      <td>doormat</td>\n",
       "      <td>0.058934</td>\n",
       "      <td>False</td>\n",
       "      <td>Not a Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1739</th>\n",
       "      <td>822462944365645825</td>\n",
       "      <td>https://pbs.twimg.com/media/C2n5rUUXEAIXAtv.jpg</td>\n",
       "      <td>3</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.960199</td>\n",
       "      <td>True</td>\n",
       "      <td>Samoyed</td>\n",
       "      <td>0.023056</td>\n",
       "      <td>True</td>\n",
       "      <td>Maltese_dog</td>\n",
       "      <td>0.008945</td>\n",
       "      <td>True</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.960199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>876484053909872640</td>\n",
       "      <td>https://pbs.twimg.com/media/DCnll_dUQAAkBdG.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.874566</td>\n",
       "      <td>True</td>\n",
       "      <td>Irish_terrier</td>\n",
       "      <td>0.037354</td>\n",
       "      <td>True</td>\n",
       "      <td>chow</td>\n",
       "      <td>0.016724</td>\n",
       "      <td>True</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.874566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>674767892831932416</td>\n",
       "      <td>https://pbs.twimg.com/media/CV1B0WkWwAEBKVx.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>shower_curtain</td>\n",
       "      <td>0.238855</td>\n",
       "      <td>False</td>\n",
       "      <td>sarong</td>\n",
       "      <td>0.092414</td>\n",
       "      <td>False</td>\n",
       "      <td>kimono</td>\n",
       "      <td>0.056412</td>\n",
       "      <td>False</td>\n",
       "      <td>Not a Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>682788441537560576</td>\n",
       "      <td>https://pbs.twimg.com/media/CXnAdosWAAEMGCM.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>toyshop</td>\n",
       "      <td>0.375610</td>\n",
       "      <td>False</td>\n",
       "      <td>orange</td>\n",
       "      <td>0.094538</td>\n",
       "      <td>False</td>\n",
       "      <td>teddy</td>\n",
       "      <td>0.039808</td>\n",
       "      <td>False</td>\n",
       "      <td>Not a Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "1111  724046343203856385  https://pbs.twimg.com/media/CgxUTS_XEAAC0pv.jpg   \n",
       "660   682406705142087680  https://pbs.twimg.com/media/CXhlRmRUMAIYoFO.jpg   \n",
       "1296  752173152931807232  https://pbs.twimg.com/media/CnBBfNuWcAAkOgO.jpg   \n",
       "2028  882627270321602560  https://pbs.twimg.com/media/DD-40X3WAAAJPU5.jpg   \n",
       "956   705428427625635840  https://pbs.twimg.com/media/CcovaMUXIAApFDl.jpg   \n",
       "443   674638615994089473  https://pbs.twimg.com/media/CVzMPh1UsAELQ_p.jpg   \n",
       "298   671485057807351808  https://pbs.twimg.com/media/CVGYGNYXAAAQ9m-.jpg   \n",
       "1132  728387165835677696  https://pbs.twimg.com/media/ChvAQuMWMAAVaKD.jpg   \n",
       "1032  711652651650457602  https://pbs.twimg.com/media/CeBMT6-WIAA7Qqf.jpg   \n",
       "817   692894228850999298  https://pbs.twimg.com/media/CZ2nn7BUsAI2Pj3.jpg   \n",
       "402   673697980713705472  https://pbs.twimg.com/media/CVl0vFeWoAAMTfg.jpg   \n",
       "1739  822462944365645825  https://pbs.twimg.com/media/C2n5rUUXEAIXAtv.jpg   \n",
       "2001  876484053909872640  https://pbs.twimg.com/media/DCnll_dUQAAkBdG.jpg   \n",
       "455   674767892831932416  https://pbs.twimg.com/media/CV1B0WkWwAEBKVx.jpg   \n",
       "666   682788441537560576  https://pbs.twimg.com/media/CXnAdosWAAEMGCM.jpg   \n",
       "\n",
       "      img_num                           p1   p1_conf  p1_dog               p2  \\\n",
       "1111  1        boxer                        0.826272  True    bull_mastiff      \n",
       "660   1        wombat                       0.709344  False   koala             \n",
       "1296  1        Labrador_retriever           0.527659  True    German_shepherd   \n",
       "2028  1        Pembroke                     0.542982  True    Chihuahua         \n",
       "956   1        Chihuahua                    0.774792  True    quilt             \n",
       "443   1        Pomeranian                   0.846986  True    chow              \n",
       "298   1        Samoyed                      0.627901  True    Great_Pyrenees    \n",
       "1132  1        collie                       0.266414  True    Great_Pyrenees    \n",
       "1032  1        llama                        0.856789  False   Arabian_camel     \n",
       "817   1        German_short-haired_pointer  0.876977  True    bluetick          \n",
       "402   1        porcupine                    0.151876  False   hen               \n",
       "1739  3        Pomeranian                   0.960199  True    Samoyed           \n",
       "2001  1        golden_retriever             0.874566  True    Irish_terrier     \n",
       "455   1        shower_curtain               0.238855  False   sarong            \n",
       "666   1        toyshop                      0.375610  False   orange            \n",
       "\n",
       "       p2_conf  p2_dog           p3   p3_conf  p3_dog  \\\n",
       "1111  0.158595  True    Great_Dane   0.011859  True     \n",
       "660   0.169758  False   beaver       0.079433  False    \n",
       "1296  0.174765  True    Chihuahua    0.045525  True     \n",
       "2028  0.251988  True    Cardigan     0.107699  True     \n",
       "956   0.073079  False   Pembroke     0.022365  True     \n",
       "443   0.142014  True    keeshond     0.002605  True     \n",
       "298   0.276421  True    kuvasz       0.057874  True     \n",
       "1132  0.138546  True    keeshond     0.109014  True     \n",
       "1032  0.098727  False   neck_brace   0.016377  False    \n",
       "817   0.036615  True    basset       0.017848  True     \n",
       "402   0.111380  False   doormat      0.058934  False    \n",
       "1739  0.023056  True    Maltese_dog  0.008945  True     \n",
       "2001  0.037354  True    chow         0.016724  True     \n",
       "455   0.092414  False   kimono       0.056412  False    \n",
       "666   0.094538  False   teddy        0.039808  False    \n",
       "\n",
       "                        dog_breed  confidence  \n",
       "1111  boxer                        0.826272    \n",
       "660   Not a Dog                    0.000000    \n",
       "1296  Labrador_retriever           0.527659    \n",
       "2028  Pembroke                     0.542982    \n",
       "956   Chihuahua                    0.774792    \n",
       "443   Pomeranian                   0.846986    \n",
       "298   Samoyed                      0.627901    \n",
       "1132  collie                       0.266414    \n",
       "1032  Not a Dog                    0.000000    \n",
       "817   German_short-haired_pointer  0.876977    \n",
       "402   Not a Dog                    0.000000    \n",
       "1739  Pomeranian                   0.960199    \n",
       "2001  golden_retriever             0.874566    \n",
       "455   Not a Dog                    0.000000    \n",
       "666   Not a Dog                    0.000000    "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check a random sample\n",
    "df_predictions_clean.sample(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, in the process of solving the issue with the redundant prediction columns, we have also solved the issue with predictions of orange, bagel, hamster, etc and have adjusted those rows to \"Not a dog\". \n",
    "\n",
    "Let's do a final cleanup with this issue and remove all of the redundant prediction columns, leaving our new dog_breed and confidence columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predictions_clean = df_predictions_clean.drop(['p1','p1_conf','p1_dog','p2','p2_conf','p2_dog','p3','p3_conf','p3_dog'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2075 entries, 0 to 2074\n",
      "Data columns (total 5 columns):\n",
      "tweet_id      2075 non-null object\n",
      "jpg_url       2075 non-null object\n",
      "img_num       2075 non-null int64\n",
      "dog_breed     2075 non-null object\n",
      "confidence    2075 non-null float64\n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 81.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_predictions_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #10: The breed names in our new dog_breed column are still pretty ugly with some breeds capitalized and words separated by underscores instead of spaces."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: First we will replace underscores with a space and then we will tackle the capitalization issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace underscore with a space\n",
    "df_predictions_clean['dog_breed'] = df_predictions_clean['dog_breed'].str.replace('_', ' ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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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       "</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>dog_breed</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>750117059602808832</td>\n",
       "      <td>https://pbs.twimg.com/media/Cmjzc-oWEAESFCm.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Shih-Tzu</td>\n",
       "      <td>0.814405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>678675843183484930</td>\n",
       "      <td>https://pbs.twimg.com/media/CWskEqnWUAAQZW_.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Not a Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>673352124999274496</td>\n",
       "      <td>https://pbs.twimg.com/media/CVg6L2hWIAAYuEb.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden retriever</td>\n",
       "      <td>0.672808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>810984652412424192</td>\n",
       "      <td>https://pbs.twimg.com/media/C0EyPZbXAAAceSc.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>golden retriever</td>\n",
       "      <td>0.871342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>699801817392291840</td>\n",
       "      <td>https://pbs.twimg.com/media/CbYyCMcWIAAHHjF.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>golden retriever</td>\n",
       "      <td>0.808978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>687818504314159109</td>\n",
       "      <td>https://pbs.twimg.com/media/CYufR8_WQAAWCqo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Lakeland terrier</td>\n",
       "      <td>0.873029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>669583744538451968</td>\n",
       "      <td>https://pbs.twimg.com/media/CUrW3DWXIAAiRqk.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Not a Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>750101899009982464</td>\n",
       "      <td>https://pbs.twimg.com/media/Cmjlsh1XgAEvhq_.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>golden retriever</td>\n",
       "      <td>0.316704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>672975131468300288</td>\n",
       "      <td>https://pbs.twimg.com/media/CVbjRSIWsAElw2s.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.836421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>670046952931721218</td>\n",
       "      <td>https://pbs.twimg.com/media/CUx8JSEXIAU6zPp.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Blenheim spaniel</td>\n",
       "      <td>0.998335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>702671118226825216</td>\n",
       "      <td>https://pbs.twimg.com/media/CcBjp2nWoAA8w-2.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.381227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>666082916733198337</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.489814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1557</th>\n",
       "      <td>793226087023144960</td>\n",
       "      <td>https://pbs.twimg.com/media/CwIa5CjW8AErZgL.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>wire-haired fox terrier</td>\n",
       "      <td>0.456047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>736010884653420544</td>\n",
       "      <td>https://pbs.twimg.com/media/CjbV-lEWgAAr6WY.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>golden retriever</td>\n",
       "      <td>0.553901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926</th>\n",
       "      <td>857746408056729600</td>\n",
       "      <td>https://pbs.twimg.com/media/C-dTzBzXUAQRjYz.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Labrador retriever</td>\n",
       "      <td>0.919832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "1279  750117059602808832  https://pbs.twimg.com/media/Cmjzc-oWEAESFCm.jpg   \n",
       "576   678675843183484930  https://pbs.twimg.com/media/CWskEqnWUAAQZW_.jpg   \n",
       "387   673352124999274496  https://pbs.twimg.com/media/CVg6L2hWIAAYuEb.jpg   \n",
       "1658  810984652412424192  https://pbs.twimg.com/media/C0EyPZbXAAAceSc.jpg   \n",
       "900   699801817392291840  https://pbs.twimg.com/media/CbYyCMcWIAAHHjF.jpg   \n",
       "749   687818504314159109  https://pbs.twimg.com/media/CYufR8_WQAAWCqo.jpg   \n",
       "195   669583744538451968  https://pbs.twimg.com/media/CUrW3DWXIAAiRqk.jpg   \n",
       "1278  750101899009982464  https://pbs.twimg.com/media/Cmjlsh1XgAEvhq_.jpg   \n",
       "370   672975131468300288  https://pbs.twimg.com/media/CVbjRSIWsAElw2s.jpg   \n",
       "214   670046952931721218  https://pbs.twimg.com/media/CUx8JSEXIAU6zPp.jpg   \n",
       "927   702671118226825216  https://pbs.twimg.com/media/CcBjp2nWoAA8w-2.jpg   \n",
       "13    666082916733198337  https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg   \n",
       "1557  793226087023144960  https://pbs.twimg.com/media/CwIa5CjW8AErZgL.jpg   \n",
       "1169  736010884653420544  https://pbs.twimg.com/media/CjbV-lEWgAAr6WY.jpg   \n",
       "1926  857746408056729600  https://pbs.twimg.com/media/C-dTzBzXUAQRjYz.jpg   \n",
       "\n",
       "      img_num                dog_breed  confidence  \n",
       "1279  2        Shih-Tzu                 0.814405    \n",
       "576   1        Not a Dog                0.000000    \n",
       "387   1        golden retriever         0.672808    \n",
       "1658  1        golden retriever         0.871342    \n",
       "900   2        golden retriever         0.808978    \n",
       "749   1        Lakeland terrier         0.873029    \n",
       "195   1        Not a Dog                0.000000    \n",
       "1278  2        golden retriever         0.316704    \n",
       "370   1        pug                      0.836421    \n",
       "214   1        Blenheim spaniel         0.998335    \n",
       "927   1        bloodhound               0.381227    \n",
       "13    1        pug                      0.489814    \n",
       "1557  1        wire-haired fox terrier  0.456047    \n",
       "1169  2        golden retriever         0.553901    \n",
       "1926  1        Labrador retriever       0.919832    "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predictions_clean.sample(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Capitalize first letter in each word\n",
    "df_predictions_clean['dog_breed'] = df_predictions_clean['dog_breed'].map(str.title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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",
       "        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>dog_breed</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1446</th>\n",
       "      <td>775898661951791106</td>\n",
       "      <td>https://pbs.twimg.com/media/CiyHLocU4AI2pJu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.945523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>718454725339934721</td>\n",
       "      <td>https://pbs.twimg.com/media/Cfh2w6HWIAIIYAF.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Not A Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>669015743032369152</td>\n",
       "      <td>https://pbs.twimg.com/media/CUjSRNCXAAQ6Y_8.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Not A Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <td>686947101016735744</td>\n",
       "      <td>https://pbs.twimg.com/media/CYiGvn-UwAEe4wL.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Not A Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>689275259254616065</td>\n",
       "      <td>https://pbs.twimg.com/media/CZDMMY0WEAAQYjQ.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>American Staffordshire Terrier</td>\n",
       "      <td>0.215161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>706901761596989440</td>\n",
       "      <td>https://pbs.twimg.com/media/Cc9rZlBWwAA56Ra.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Not A Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>768609597686943744</td>\n",
       "      <td>https://pbs.twimg.com/media/CqqmWa7WcAAIM-n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Basenji</td>\n",
       "      <td>0.183283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>847971574464610304</td>\n",
       "      <td>https://pbs.twimg.com/media/C8SZH1EWAAAIRRF.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Not A Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>757729163776290825</td>\n",
       "      <td>https://pbs.twimg.com/media/CWyD2HGUYAQ1Xa7.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Schipperke</td>\n",
       "      <td>0.045519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>690015576308211712</td>\n",
       "      <td>https://pbs.twimg.com/media/CZNtgWhWkAAbq3W.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Malamute</td>\n",
       "      <td>0.949609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>725786712245440512</td>\n",
       "      <td>https://pbs.twimg.com/media/ChKDKmIWIAIJP_e.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chow</td>\n",
       "      <td>0.335761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>756303284449767430</td>\n",
       "      <td>https://pbs.twimg.com/media/Cn7tyyZWYAAPlAY.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.981652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>691090071332753408</td>\n",
       "      <td>https://pbs.twimg.com/media/CZc-u7IXEAQHV1N.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Not A Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>740365076218183684</td>\n",
       "      <td>https://pbs.twimg.com/media/CkZOGhJWsAAHvPv.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Not A Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>675354435921575936</td>\n",
       "      <td>https://pbs.twimg.com/ext_tw_video_thumb/675354114423808004/pu/img/qL1R_nGLqa6lmkOx.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>0.181351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  \\\n",
       "1446  775898661951791106   \n",
       "1082  718454725339934721   \n",
       "174   669015743032369152   \n",
       "734   686947101016735744   \n",
       "767   689275259254616065   \n",
       "974   706901761596989440   \n",
       "1400  768609597686943744   \n",
       "1887  847971574464610304   \n",
       "1333  757729163776290825   \n",
       "783   690015576308211712   \n",
       "1117  725786712245440512   \n",
       "1321  756303284449767430   \n",
       "797   691090071332753408   \n",
       "1195  740365076218183684   \n",
       "480   675354435921575936   \n",
       "\n",
       "                                                                                      jpg_url  \\\n",
       "1446  https://pbs.twimg.com/media/CiyHLocU4AI2pJu.jpg                                           \n",
       "1082  https://pbs.twimg.com/media/Cfh2w6HWIAIIYAF.jpg                                           \n",
       "174   https://pbs.twimg.com/media/CUjSRNCXAAQ6Y_8.jpg                                           \n",
       "734   https://pbs.twimg.com/media/CYiGvn-UwAEe4wL.jpg                                           \n",
       "767   https://pbs.twimg.com/media/CZDMMY0WEAAQYjQ.jpg                                           \n",
       "974   https://pbs.twimg.com/media/Cc9rZlBWwAA56Ra.jpg                                           \n",
       "1400  https://pbs.twimg.com/media/CqqmWa7WcAAIM-n.jpg                                           \n",
       "1887  https://pbs.twimg.com/media/C8SZH1EWAAAIRRF.jpg                                           \n",
       "1333  https://pbs.twimg.com/media/CWyD2HGUYAQ1Xa7.jpg                                           \n",
       "783   https://pbs.twimg.com/media/CZNtgWhWkAAbq3W.jpg                                           \n",
       "1117  https://pbs.twimg.com/media/ChKDKmIWIAIJP_e.jpg                                           \n",
       "1321  https://pbs.twimg.com/media/Cn7tyyZWYAAPlAY.jpg                                           \n",
       "797   https://pbs.twimg.com/media/CZc-u7IXEAQHV1N.jpg                                           \n",
       "1195  https://pbs.twimg.com/media/CkZOGhJWsAAHvPv.jpg                                           \n",
       "480   https://pbs.twimg.com/ext_tw_video_thumb/675354114423808004/pu/img/qL1R_nGLqa6lmkOx.jpg   \n",
       "\n",
       "      img_num                       dog_breed  confidence  \n",
       "1446  1        Golden Retriever                0.945523    \n",
       "1082  1        Not A Dog                       0.000000    \n",
       "174   1        Not A Dog                       0.000000    \n",
       "734   1        Not A Dog                       0.000000    \n",
       "767   1        American Staffordshire Terrier  0.215161    \n",
       "974   1        Not A Dog                       0.000000    \n",
       "1400  1        Basenji                         0.183283    \n",
       "1887  1        Not A Dog                       0.000000    \n",
       "1333  2        Schipperke                      0.045519    \n",
       "783   2        Malamute                        0.949609    \n",
       "1117  1        Chow                            0.335761    \n",
       "1321  1        Golden Retriever                0.981652    \n",
       "797   1        Not A Dog                       0.000000    \n",
       "1195  1        Not A Dog                       0.000000    \n",
       "480   1        Golden Retriever                0.181351    "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predictions_clean.sample(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Issue #11: Combine tables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: The tables should be combined into one dataframe because they deal with one observational unit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweet_archive_clean = pd.merge(df_archive_clean, df_tweets_clean, \n",
    "                         on = 'tweet_id', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweet_archive_clean = pd.merge(df_tweet_archive_clean, df_predictions_clean, \n",
    "                         on = 'tweet_id', how = 'left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>dog_stage</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>dog_breed</th>\n",
       "      <th>confidence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>2017-08-01 16:23:56</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643555336193/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8853</td>\n",
       "      <td>39467</td>\n",
       "      <td>https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Not A Dog</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>2017-08-01 00:17:27</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421306343426/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6514</td>\n",
       "      <td>33819</td>\n",
       "      <td>https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.323581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>2017-07-31 00:18:03</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181378084864/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4328</td>\n",
       "      <td>25461</td>\n",
       "      <td>https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.716012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>2017-07-30 15:58:51</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557279858688/photo/1</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8964</td>\n",
       "      <td>42908</td>\n",
       "      <td>https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>0.168086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>2017-07-29 16:00:24</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\"&gt;Twitter for iPhone&lt;/a&gt;</td>\n",
       "      <td>This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9774</td>\n",
       "      <td>41048</td>\n",
       "      <td>https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Basset</td>\n",
       "      <td>0.555712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id           timestamp  \\\n",
       "0  892420643555336193 2017-08-01 16:23:56   \n",
       "1  892177421306343426 2017-08-01 00:17:27   \n",
       "2  891815181378084864 2017-07-31 00:18:03   \n",
       "3  891689557279858688 2017-07-30 15:58:51   \n",
       "4  891327558926688256 2017-07-29 16:00:24   \n",
       "\n",
       "                                                                               source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>   \n",
       "\n",
       "                                                                                                                                                                 text  \\\n",
       "0  This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU                                                        \n",
       "1  This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV   \n",
       "2  This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB                    \n",
       "3  This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ                                                              \n",
       "4  This is Franklin. He would like you to stop calling him \"cute.\" He is a very fierce shark and should be respected as such. 12/10 #BarkWeek https://t.co/AtUZn91f7f   \n",
       "\n",
       "                                                                                                                     expanded_urls  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643555336193/photo/1                                                                   \n",
       "1  https://twitter.com/dog_rates/status/892177421306343426/photo/1                                                                   \n",
       "2  https://twitter.com/dog_rates/status/891815181378084864/photo/1                                                                   \n",
       "3  https://twitter.com/dog_rates/status/891689557279858688/photo/1                                                                   \n",
       "4  https://twitter.com/dog_rates/status/891327558926688256/photo/1,https://twitter.com/dog_rates/status/891327558926688256/photo/1   \n",
       "\n",
       "   rating_numerator  rating_denominator      name dog_stage  retweet_count  \\\n",
       "0  13                10                  Phineas   NaN       8853            \n",
       "1  13                10                  Tilly     NaN       6514            \n",
       "2  12                10                  Archie    NaN       4328            \n",
       "3  13                10                  Darla     NaN       8964            \n",
       "4  12                10                  Franklin  NaN       9774            \n",
       "\n",
       "   favorite_count                                          jpg_url  img_num  \\\n",
       "0  39467           https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg  1.0       \n",
       "1  33819           https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg  1.0       \n",
       "2  25461           https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg  1.0       \n",
       "3  42908           https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg  1.0       \n",
       "4  41048           https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg  2.0       \n",
       "\n",
       "            dog_breed  confidence  \n",
       "0  Not A Dog           0.000000    \n",
       "1  Chihuahua           0.323581    \n",
       "2  Chihuahua           0.716012    \n",
       "3  Labrador Retriever  0.168086    \n",
       "4  Basset              0.555712    "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tweet_archive_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2076 entries, 0 to 2075\n",
      "Data columns (total 15 columns):\n",
      "tweet_id              2076 non-null object\n",
      "timestamp             2076 non-null datetime64[ns]\n",
      "source                2076 non-null object\n",
      "text                  2076 non-null object\n",
      "expanded_urls         2076 non-null object\n",
      "rating_numerator      2076 non-null int64\n",
      "rating_denominator    2076 non-null int64\n",
      "name                  1387 non-null object\n",
      "dog_stage             335 non-null object\n",
      "retweet_count         2076 non-null int64\n",
      "favorite_count        2076 non-null int64\n",
      "jpg_url               1953 non-null object\n",
      "img_num               1953 non-null float64\n",
      "dog_breed             1953 non-null object\n",
      "confidence            1953 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(2), int64(4), object(8)\n",
      "memory usage: 259.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweet_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Issue!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: After combining the dataframes into one, we have discovered more dogs that do not have pictures. We need to drop those from the cleaned master dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweet_archive_clean = df_tweet_archive_clean.dropna(subset=['jpg_url'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1953 entries, 0 to 2075\n",
      "Data columns (total 15 columns):\n",
      "tweet_id              1953 non-null object\n",
      "timestamp             1953 non-null datetime64[ns]\n",
      "source                1953 non-null object\n",
      "text                  1953 non-null object\n",
      "expanded_urls         1953 non-null object\n",
      "rating_numerator      1953 non-null int64\n",
      "rating_denominator    1953 non-null int64\n",
      "name                  1346 non-null object\n",
      "dog_stage             303 non-null object\n",
      "retweet_count         1953 non-null int64\n",
      "favorite_count        1953 non-null int64\n",
      "jpg_url               1953 non-null object\n",
      "img_num               1953 non-null float64\n",
      "dog_breed             1953 non-null object\n",
      "confidence            1953 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(2), int64(4), object(8)\n",
      "memory usage: 244.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweet_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional issue!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: img_num should be an integer. We cannot have 1.5 images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweet_archive_clean.img_num = df_tweet_archive_clean.img_num.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1953 entries, 0 to 2075\n",
      "Data columns (total 15 columns):\n",
      "tweet_id              1953 non-null object\n",
      "timestamp             1953 non-null datetime64[ns]\n",
      "source                1953 non-null object\n",
      "text                  1953 non-null object\n",
      "expanded_urls         1953 non-null object\n",
      "rating_numerator      1953 non-null int64\n",
      "rating_denominator    1953 non-null int64\n",
      "name                  1346 non-null object\n",
      "dog_stage             303 non-null object\n",
      "retweet_count         1953 non-null int64\n",
      "favorite_count        1953 non-null int64\n",
      "jpg_url               1953 non-null object\n",
      "img_num               1953 non-null int64\n",
      "dog_breed             1953 non-null object\n",
      "confidence            1953 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), int64(5), object(8)\n",
      "memory usage: 244.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tweet_archive_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing Data\n",
    "Save gathered, assessed, and cleaned master dataset to a CSV file named \"twitter_archive_master.csv\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tweet_archive_clean.to_csv('twitter_archive_master.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read twitter_archive_master.csv\n",
    "df_master = pd.read_csv('twitter_archive_master.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight #1:\n",
    "#### Which dog breeds appear the most often in the dataset?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, I am curious which dog breeds are mentioned most often in the dataset according to the machine learning prediction. The predictions are not 100% accurate, but it should be enough to provide a meaningful insight. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frequent_breeds = (df_master.dog_breed.value_counts().head(10).to_frame().reset_index().rename(columns={'index': 'dog_breed', 'dog_breed': 'total'}))\n",
    "\n",
    "plt.figure(dpi=100);\n",
    "ax = sb.barplot(x='total', y='dog_breed', data=frequent_breeds, palette='rainbow');\n",
    "ax.set_title('Ten Most Frequent Dog Breeds in WeRateDogs Tweets');\n",
    "ax.set_ylabel('Dog Breed');\n",
    "ax.set_xlabel('Total Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The machine learning algorithm predicted \"Not A Dog\" for most tweets. It is possible that the predictions are based off of something that was more prominent than the actual dog in the image, but cleaning the predictions like that is out of scope for this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aside from the \"Not A Dog\" result, the breed most frequently found in the dataset is Golden Retriever! I see nothing wrong with this. Everybody loves those majestic, fluffy goofballs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight #2:\n",
    "#### Which dog names are found most frequently?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I would like to see which names are found most frequently in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Charlie    11\n",
       "Lucy       10\n",
       "Oliver     10\n",
       "Cooper     10\n",
       "Tucker     9 \n",
       "Penny      9 \n",
       "Sadie      8 \n",
       "Winston    8 \n",
       "Daisy      7 \n",
       "Toby       7 \n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master.name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Charlie is the most common dog name. Lucy, Cooper, and Oliver are in a three-way tie for second."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight #3:\n",
    "#### Is there any correlation between favorite count and retweet count?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, I'd like to find out whether there is a positive or negative correlation between favorite count and retweet count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100);\n",
    "ax = sb.scatterplot(x = 'retweet_count', y = 'favorite_count', data = df_master);\n",
    "ax.set_title('Correlation Between Favorite Count and Retweet Count');\n",
    "ax.set_xlabel('Retweet Count');\n",
    "ax.set_ylabel('Favorite Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few large outliers, but we obviously have a strong positive correlation between favorite count and retweet count. The tweets that have been retweeted the most tend to have the highest favorite count. This makes sense because a higher retweet count means more visibility, which means more people are seeing it and could possibly mark it as a favorite. "
   ]
  }
 ],
 "metadata": {
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      "defaultCellHeight": 20,
      "maxColumns": 12,
      "name": "grid",
      "type": "grid"
     },
     "report_default": {
      "name": "report",
      "type": "report"
     }
    }
   }
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "pygments_lexer": "ipython3",
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 },
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}