wrangle_act.ipynb

{
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   "source": [
    "# Project: Wrangling and Analyze Data"
   ]
  },
  {
   "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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import os\n",
    "import re\n",
    "import tweepy\n",
    "from tweepy import OAuthHandler\n",
    "import json\n",
    "from timeit import default_timer as timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   },
   "outputs": [],
   "source": [
    "twitter_archive= pd.read_csv(\"twitter-archive-enhanced.csv\")\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Use the Requests library to download the tweet image prediction (image_predictions.tsv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make directory if it doesn't already exist\n",
    "folder_name = 'new_folder'\n",
    "if not os.path.exists(folder_name):\n",
    "    os.makedirs(folder_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating the request\n",
    "url =  \"https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv\"\n",
    "response = requests.get(url)\n",
    "#check status\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write the tsv file into our new folder\n",
    "with open(os.path.join(folder_name, url.split('/')[-1]), mode = 'wb') as file:\n",
    "    file.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use read_csv toread as a pandas Dataframe(set seperator to [\\t] since its a TSV file)\n",
    "image_predictions=pd.read_csv(\"new_folder/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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing our keys and tokens in variables\n",
    "consumer_key = 'yE5DGKB8RI90PJS4SvUq2ueeW'\n",
    "consumer_secret = 'F2fuP4GxOgUqbnv4H5swzRWodxhx7epJXVuan7TxjVFtBXeNrG'\n",
    "access_token = '4904439099-N5XF6sfXBt7ltZhWs6SVCJcO95z4MvGbKTifGws'\n",
    "access_secret = 'wSLX2dxuwlODlvKd18P0fVrpJLfdUaD7VrtMlR7gXwJM0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting up the API object\n",
    "auth = OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_secret)\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2356"
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#storing our tweets IDs from the enganced_archive  in the Tweet_IDs variable\n",
    "tweet_ids = twitter_archive.tweet_id.values\n",
    "#check the length\n",
    "len(tweet_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remember to convert  next cell to code before submission(was converted to \n",
    "# to markdown because it overwrites the txt file everytime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "# 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",
    "     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": 11,
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2326, 32)"
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "#read into a pandas dataframe line by line using lines= true\n",
    "tweet_ids = pd.read_json('tweet_json.txt', lines=True) \n",
    "tweet_ids.shape"
   ]
  },
  {
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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": [
    "### Twitter archive table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>891815181378084864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891815181...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Archie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>891689557279858688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891689557...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Darla</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>891327558926688256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/891327558...</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "2  891815181378084864                    NaN                  NaN   \n",
       "3  891689557279858688                    NaN                  NaN   \n",
       "4  891327558926688256                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "2  2017-07-31 00:18:03 +0000   \n",
       "3  2017-07-30 15:58:51 +0000   \n",
       "4  2017-07-29 16:00:24 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "2  This is Archie. He is a rare Norwegian Pouncin...                  NaN   \n",
       "3  This is Darla. She commenced a snooze mid meal...                  NaN   \n",
       "4  This is Franklin. He would like you to stop ca...                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "2                       NaN                        NaN   \n",
       "3                       NaN                        NaN   \n",
       "4                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "2  https://twitter.com/dog_rates/status/891815181...                12   \n",
       "3  https://twitter.com/dog_rates/status/891689557...                13   \n",
       "4  https://twitter.com/dog_rates/status/891327558...                12   \n",
       "\n",
       "   rating_denominator      name doggo floofer pupper puppo  \n",
       "0                  10   Phineas  None    None   None  None  \n",
       "1                  10     Tilly  None    None   None  None  \n",
       "2                  10    Archie  None    None   None  None  \n",
       "3                  10     Darla  None    None   None  None  \n",
       "4                  10  Franklin  None    None   None  None  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the Twitter Archive table table\n",
    "twitter_archive.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null tweet ids\n",
    "sum(twitter_archive.tweet_id.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for non-null retweet status id(to indicate its a retweet)\n",
    "sum(twitter_archive.retweeted_status_id.notnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for non-null in_reply_to_status_id id(to indicate its a reply)\n",
    "sum(twitter_archive.in_reply_to_status_id.notnull())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>679877062409191424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-24 04:11:37 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Meet Penelope. She's a bacon frise. Total babe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/679877062...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>Penelope</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>808838249661788160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-14 00:57:20 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Ito. He'll be your uber driver tonight...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/808838249...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Ito</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>762699858130116608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-08-08 17:19:51 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Leela. She's a Fetty Woof. Lost eye wh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/762699858...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>Leela</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>684914660081053696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-07 01:49:14 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>\"Hello yes I'll just get one of each color tha...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/684914660...</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>639</th>\n",
       "      <td>793256262322548741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-11-01 01:00:05 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>Oh h*ck look at this spookling right here. Fri...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/793256262...</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "1727  679877062409191424                    NaN                  NaN   \n",
       "526   808838249661788160                    NaN                  NaN   \n",
       "862   762699858130116608                    NaN                  NaN   \n",
       "1622  684914660081053696                    NaN                  NaN   \n",
       "639   793256262322548741                    NaN                  NaN   \n",
       "\n",
       "                      timestamp  \\\n",
       "1727  2015-12-24 04:11:37 +0000   \n",
       "526   2016-12-14 00:57:20 +0000   \n",
       "862   2016-08-08 17:19:51 +0000   \n",
       "1622  2016-01-07 01:49:14 +0000   \n",
       "639   2016-11-01 01:00:05 +0000   \n",
       "\n",
       "                                                 source  \\\n",
       "1727  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "526   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "862   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1622  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "639   <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                   text  retweeted_status_id  \\\n",
       "1727  Meet Penelope. She's a bacon frise. Total babe...                  NaN   \n",
       "526   This is Ito. He'll be your uber driver tonight...                  NaN   \n",
       "862   This is Leela. She's a Fetty Woof. Lost eye wh...                  NaN   \n",
       "1622  \"Hello yes I'll just get one of each color tha...                  NaN   \n",
       "639   Oh h*ck look at this spookling right here. Fri...                  NaN   \n",
       "\n",
       "      retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "1727                       NaN                        NaN   \n",
       "526                        NaN                        NaN   \n",
       "862                        NaN                        NaN   \n",
       "1622                       NaN                        NaN   \n",
       "639                        NaN                        NaN   \n",
       "\n",
       "                                          expanded_urls  rating_numerator  \\\n",
       "1727  https://twitter.com/dog_rates/status/679877062...                 5   \n",
       "526   https://twitter.com/dog_rates/status/808838249...                13   \n",
       "862   https://twitter.com/dog_rates/status/762699858...                11   \n",
       "1622  https://twitter.com/dog_rates/status/684914660...                12   \n",
       "639   https://twitter.com/dog_rates/status/793256262...                12   \n",
       "\n",
       "      rating_denominator      name doggo floofer pupper puppo  \n",
       "1727                  10  Penelope  None    None   None  None  \n",
       "526                   10       Ito  None    None   None  None  \n",
       "862                   10     Leela  None    None   None  None  \n",
       "1622                  10      None  None    None   None  None  \n",
       "639                   10      None  None    None   None  None  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#draw 50 samples for assessment\n",
    "twitter_archive.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicate tweet entries using the tweets ID column\n",
    "twitter_archive[twitter_archive.tweet_id.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "#check the info of the dataframe\n",
    "twitter_archive.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check description of the dataframe\n",
    "twitter_archive.describe()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892420643555336193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892420643...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Phineas</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>892177421306343426</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27 +0000</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://twitter.com/dog_rates/status/892177421...</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>Tilly</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id  in_reply_to_status_id  in_reply_to_user_id  \\\n",
       "0  892420643555336193                    NaN                  NaN   \n",
       "1  892177421306343426                    NaN                  NaN   \n",
       "\n",
       "                   timestamp  \\\n",
       "0  2017-08-01 16:23:56 +0000   \n",
       "1  2017-08-01 00:17:27 +0000   \n",
       "\n",
       "                                              source  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "\n",
       "                                                text  retweeted_status_id  \\\n",
       "0  This is Phineas. He's a mystical boy. Only eve...                  NaN   \n",
       "1  This is Tilly. She's just checking pup on you....                  NaN   \n",
       "\n",
       "   retweeted_status_user_id retweeted_status_timestamp  \\\n",
       "0                       NaN                        NaN   \n",
       "1                       NaN                        NaN   \n",
       "\n",
       "                                       expanded_urls  rating_numerator  \\\n",
       "0  https://twitter.com/dog_rates/status/892420643...                13   \n",
       "1  https://twitter.com/dog_rates/status/892177421...                13   \n",
       "\n",
       "   rating_denominator     name doggo floofer pupper puppo  \n",
       "0                  10  Phineas  None    None   None  None  \n",
       "1                  10    Tilly  None    None   None  None  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#query for tweets beyond Aug. 1st 2017\n",
    "twitter_archive.query(\"timestamp > '2017-08-01'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12      558\n",
       "11      464\n",
       "10      461\n",
       "13      351\n",
       "9       158\n",
       "8       102\n",
       "7        55\n",
       "14       54\n",
       "5        37\n",
       "6        32\n",
       "3        19\n",
       "4        17\n",
       "1         9\n",
       "2         9\n",
       "420       2\n",
       "0         2\n",
       "15        2\n",
       "75        2\n",
       "80        1\n",
       "20        1\n",
       "24        1\n",
       "26        1\n",
       "44        1\n",
       "50        1\n",
       "60        1\n",
       "165       1\n",
       "84        1\n",
       "88        1\n",
       "144       1\n",
       "182       1\n",
       "143       1\n",
       "666       1\n",
       "960       1\n",
       "1776      1\n",
       "17        1\n",
       "27        1\n",
       "45        1\n",
       "99        1\n",
       "121       1\n",
       "204       1\n",
       "Name: rating_numerator, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#value counts of rating numerator\n",
    "twitter_archive.rating_numerator.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     2333\n",
       "11        3\n",
       "50        3\n",
       "80        2\n",
       "20        2\n",
       "2         1\n",
       "16        1\n",
       "40        1\n",
       "70        1\n",
       "15        1\n",
       "90        1\n",
       "110       1\n",
       "120       1\n",
       "130       1\n",
       "150       1\n",
       "170       1\n",
       "7         1\n",
       "0         1\n",
       "Name: rating_denominator, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#value counts of ratings denominator\n",
    "twitter_archive.rating_denominator.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image prediction table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>666020888022790149</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Welsh_springer_spaniel</td>\n",
       "      <td>0.465074</td>\n",
       "      <td>True</td>\n",
       "      <td>collie</td>\n",
       "      <td>0.156665</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.061428</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666029285002620928</td>\n",
       "      <td>https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.506826</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.074192</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.072010</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666033412701032449</td>\n",
       "      <td>https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>German_shepherd</td>\n",
       "      <td>0.596461</td>\n",
       "      <td>True</td>\n",
       "      <td>malinois</td>\n",
       "      <td>0.138584</td>\n",
       "      <td>True</td>\n",
       "      <td>bloodhound</td>\n",
       "      <td>0.116197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>666044226329800704</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rhodesian_ridgeback</td>\n",
       "      <td>0.408143</td>\n",
       "      <td>True</td>\n",
       "      <td>redbone</td>\n",
       "      <td>0.360687</td>\n",
       "      <td>True</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>666049248165822465</td>\n",
       "      <td>https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>miniature_pinscher</td>\n",
       "      <td>0.560311</td>\n",
       "      <td>True</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.243682</td>\n",
       "      <td>True</td>\n",
       "      <td>Doberman</td>\n",
       "      <td>0.154629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                          jpg_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num                      p1   p1_conf  p1_dog                  p2  \\\n",
       "0        1  Welsh_springer_spaniel  0.465074    True              collie   \n",
       "1        1                 redbone  0.506826    True  miniature_pinscher   \n",
       "2        1         German_shepherd  0.596461    True            malinois   \n",
       "3        1     Rhodesian_ridgeback  0.408143    True             redbone   \n",
       "4        1      miniature_pinscher  0.560311    True          Rottweiler   \n",
       "\n",
       "    p2_conf  p2_dog                   p3   p3_conf  p3_dog  \n",
       "0  0.156665    True    Shetland_sheepdog  0.061428    True  \n",
       "1  0.074192    True  Rhodesian_ridgeback  0.072010    True  \n",
       "2  0.138584    True           bloodhound  0.116197    True  \n",
       "3  0.360687    True   miniature_pinscher  0.222752    True  \n",
       "4  0.243682    True             Doberman  0.154629    True  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the image predictions table\n",
    "image_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1421</th>\n",
       "      <td>772102971039580160</td>\n",
       "      <td>https://pbs.twimg.com/media/CrcPjh0WcAA_SPT.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>0.541780</td>\n",
       "      <td>True</td>\n",
       "      <td>Cardigan</td>\n",
       "      <td>0.260504</td>\n",
       "      <td>True</td>\n",
       "      <td>Shetland_sheepdog</td>\n",
       "      <td>0.063703</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>674372068062928900</td>\n",
       "      <td>https://pbs.twimg.com/media/CVvZ0KTWwAAdXKV.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>seashore</td>\n",
       "      <td>0.346126</td>\n",
       "      <td>False</td>\n",
       "      <td>American_alligator</td>\n",
       "      <td>0.106404</td>\n",
       "      <td>False</td>\n",
       "      <td>sandbar</td>\n",
       "      <td>0.049349</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>863062471531167744</td>\n",
       "      <td>https://pbs.twimg.com/media/C_o2vKCUwAAgtOp.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>French_bulldog</td>\n",
       "      <td>0.935804</td>\n",
       "      <td>True</td>\n",
       "      <td>pug</td>\n",
       "      <td>0.059576</td>\n",
       "      <td>True</td>\n",
       "      <td>boxer</td>\n",
       "      <td>0.001412</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>676811746707918848</td>\n",
       "      <td>https://pbs.twimg.com/media/CWSEsO9WwAAX-fZ.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>0.440916</td>\n",
       "      <td>True</td>\n",
       "      <td>Pomeranian</td>\n",
       "      <td>0.345806</td>\n",
       "      <td>True</td>\n",
       "      <td>cocker_spaniel</td>\n",
       "      <td>0.060331</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1758</th>\n",
       "      <td>825535076884762624</td>\n",
       "      <td>https://pbs.twimg.com/media/C3TjvitXAAAI-QH.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>0.681495</td>\n",
       "      <td>True</td>\n",
       "      <td>Tibetan_mastiff</td>\n",
       "      <td>0.147940</td>\n",
       "      <td>True</td>\n",
       "      <td>black-and-tan_coonhound</td>\n",
       "      <td>0.024525</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>751538714308972544</td>\n",
       "      <td>https://pbs.twimg.com/media/Cm4AeG8XEAAulD2.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "      <td>0.516257</td>\n",
       "      <td>True</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>0.210839</td>\n",
       "      <td>True</td>\n",
       "      <td>dingo</td>\n",
       "      <td>0.162022</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>672970152493887488</td>\n",
       "      <td>https://pbs.twimg.com/media/CVbeyGUU8AEq300.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>leaf_beetle</td>\n",
       "      <td>0.340154</td>\n",
       "      <td>False</td>\n",
       "      <td>rhinoceros_beetle</td>\n",
       "      <td>0.139698</td>\n",
       "      <td>False</td>\n",
       "      <td>crayfish</td>\n",
       "      <td>0.058034</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>859074603037188101</td>\n",
       "      <td>https://pbs.twimg.com/media/C-wLyufW0AA546I.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>revolver</td>\n",
       "      <td>0.190292</td>\n",
       "      <td>False</td>\n",
       "      <td>projectile</td>\n",
       "      <td>0.149064</td>\n",
       "      <td>False</td>\n",
       "      <td>fountain</td>\n",
       "      <td>0.066047</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>671159727754231808</td>\n",
       "      <td>https://pbs.twimg.com/media/CVBwNjVWwAAlUFQ.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pitcher</td>\n",
       "      <td>0.117446</td>\n",
       "      <td>False</td>\n",
       "      <td>sunglasses</td>\n",
       "      <td>0.062487</td>\n",
       "      <td>False</td>\n",
       "      <td>mask</td>\n",
       "      <td>0.059517</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>667861340749471744</td>\n",
       "      <td>https://pbs.twimg.com/media/CUS4WJ-UsAEJj10.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>malamute</td>\n",
       "      <td>0.967275</td>\n",
       "      <td>True</td>\n",
       "      <td>Siberian_husky</td>\n",
       "      <td>0.016168</td>\n",
       "      <td>True</td>\n",
       "      <td>Eskimo_dog</td>\n",
       "      <td>0.011277</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                          jpg_url  \\\n",
       "1421  772102971039580160  https://pbs.twimg.com/media/CrcPjh0WcAA_SPT.jpg   \n",
       "434   674372068062928900  https://pbs.twimg.com/media/CVvZ0KTWwAAdXKV.jpg   \n",
       "1949  863062471531167744  https://pbs.twimg.com/media/C_o2vKCUwAAgtOp.jpg   \n",
       "529   676811746707918848  https://pbs.twimg.com/media/CWSEsO9WwAAX-fZ.jpg   \n",
       "1758  825535076884762624  https://pbs.twimg.com/media/C3TjvitXAAAI-QH.jpg   \n",
       "1291  751538714308972544  https://pbs.twimg.com/media/Cm4AeG8XEAAulD2.jpg   \n",
       "369   672970152493887488  https://pbs.twimg.com/media/CVbeyGUU8AEq300.jpg   \n",
       "1931  859074603037188101  https://pbs.twimg.com/media/C-wLyufW0AA546I.jpg   \n",
       "288   671159727754231808  https://pbs.twimg.com/media/CVBwNjVWwAAlUFQ.jpg   \n",
       "105   667861340749471744  https://pbs.twimg.com/media/CUS4WJ-UsAEJj10.jpg   \n",
       "\n",
       "      img_num                  p1   p1_conf  p1_dog                  p2  \\\n",
       "1421        1            Pembroke  0.541780    True            Cardigan   \n",
       "434         1            seashore  0.346126   False  American_alligator   \n",
       "1949        2      French_bulldog  0.935804    True                 pug   \n",
       "529         1           Chihuahua  0.440916    True          Pomeranian   \n",
       "1758        1          Rottweiler  0.681495    True     Tibetan_mastiff   \n",
       "1291        2  Labrador_retriever  0.516257    True    golden_retriever   \n",
       "369         1         leaf_beetle  0.340154   False   rhinoceros_beetle   \n",
       "1931        1            revolver  0.190292   False          projectile   \n",
       "288         1             pitcher  0.117446   False          sunglasses   \n",
       "105         1            malamute  0.967275    True      Siberian_husky   \n",
       "\n",
       "       p2_conf  p2_dog                       p3   p3_conf  p3_dog  \n",
       "1421  0.260504    True        Shetland_sheepdog  0.063703    True  \n",
       "434   0.106404   False                  sandbar  0.049349   False  \n",
       "1949  0.059576    True                    boxer  0.001412    True  \n",
       "529   0.345806    True           cocker_spaniel  0.060331    True  \n",
       "1758  0.147940    True  black-and-tan_coonhound  0.024525    True  \n",
       "1291  0.210839    True                    dingo  0.162022   False  \n",
       "369   0.139698   False                 crayfish  0.058034   False  \n",
       "1931  0.149064   False                 fountain  0.066047   False  \n",
       "288   0.062487   False                     mask  0.059517   False  \n",
       "105   0.016168    True               Eskimo_dog  0.011277    True  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#draw 10 samples for assessment\n",
    "image_predictions.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null tweet ids\n",
    "sum(image_predictions.tweet_id.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, jpg_url, img_num, p1, p1_conf, p1_dog, p2, p2_conf, p2_dog, p3, p3_conf, p3_dog]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicate predictions\n",
    "image_predictions[image_predictions.tweet_id.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "#check info\n",
    "image_predictions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get description\n",
    "image_predictions.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "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>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>4.588540e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>terrapin</td>\n",
       "      <td>1.788530e-02</td>\n",
       "      <td>False</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>3.391940e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>partridge</td>\n",
       "      <td>5.206580e-05</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>8.554740e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>bookcase</td>\n",
       "      <td>7.947970e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>666293911632134144</td>\n",
       "      <td>https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>three-toed_sloth</td>\n",
       "      <td>0.914671</td>\n",
       "      <td>False</td>\n",
       "      <td>otter</td>\n",
       "      <td>1.525000e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>great_grey_owl</td>\n",
       "      <td>1.320720e-02</td>\n",
       "      <td>False</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>2.402450e-03</td>\n",
       "      <td>False</td>\n",
       "      <td>hamster</td>\n",
       "      <td>4.608630e-04</td>\n",
       "      <td>False</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>2.714850e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>gar</td>\n",
       "      <td>1.899450e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>666786068205871104</td>\n",
       "      <td>https://pbs.twimg.com/media/CUDmZIkWcAAIPPe.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>snail</td>\n",
       "      <td>0.999888</td>\n",
       "      <td>False</td>\n",
       "      <td>slug</td>\n",
       "      <td>5.514170e-05</td>\n",
       "      <td>False</td>\n",
       "      <td>acorn</td>\n",
       "      <td>2.625800e-05</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>666837028449972224</td>\n",
       "      <td>https://pbs.twimg.com/media/CUEUva1WsAA2jPb.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>triceratops</td>\n",
       "      <td>0.442113</td>\n",
       "      <td>False</td>\n",
       "      <td>armadillo</td>\n",
       "      <td>1.140710e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>common_iguana</td>\n",
       "      <td>4.325530e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>666983947667116034</td>\n",
       "      <td>https://pbs.twimg.com/media/CUGaXDhW4AY9JUH.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>swab</td>\n",
       "      <td>0.589446</td>\n",
       "      <td>False</td>\n",
       "      <td>chain_saw</td>\n",
       "      <td>1.901420e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>wig</td>\n",
       "      <td>3.450970e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>667012601033924608</td>\n",
       "      <td>https://pbs.twimg.com/media/CUG0bC0U8AAw2su.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>hyena</td>\n",
       "      <td>0.987230</td>\n",
       "      <td>False</td>\n",
       "      <td>African_hunting_dog</td>\n",
       "      <td>1.260080e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>coyote</td>\n",
       "      <td>5.735010e-05</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>667065535570550784</td>\n",
       "      <td>https://pbs.twimg.com/media/CUHkkJpXIAA2w3n.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>jigsaw_puzzle</td>\n",
       "      <td>0.560001</td>\n",
       "      <td>False</td>\n",
       "      <td>doormat</td>\n",
       "      <td>1.032590e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>space_heater</td>\n",
       "      <td>4.256800e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>667188689915760640</td>\n",
       "      <td>https://pbs.twimg.com/media/CUJUk2iWUAAVtOv.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>vacuum</td>\n",
       "      <td>0.335830</td>\n",
       "      <td>False</td>\n",
       "      <td>swab</td>\n",
       "      <td>2.652780e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>toilet_tissue</td>\n",
       "      <td>1.407030e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>667369227918143488</td>\n",
       "      <td>https://pbs.twimg.com/media/CUL4xR9UkAEdlJ6.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>teddy</td>\n",
       "      <td>0.709545</td>\n",
       "      <td>False</td>\n",
       "      <td>bath_towel</td>\n",
       "      <td>1.272850e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>Christmas_stocking</td>\n",
       "      <td>2.856750e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>667437278097252352</td>\n",
       "      <td>https://pbs.twimg.com/media/CUM2qWaWoAUZ06L.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>porcupine</td>\n",
       "      <td>0.989154</td>\n",
       "      <td>False</td>\n",
       "      <td>bath_towel</td>\n",
       "      <td>6.300490e-03</td>\n",
       "      <td>False</td>\n",
       "      <td>badger</td>\n",
       "      <td>9.663400e-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>667443425659232256</td>\n",
       "      <td>https://pbs.twimg.com/media/CUM8QZwW4AAVsBl.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>goose</td>\n",
       "      <td>0.980815</td>\n",
       "      <td>False</td>\n",
       "      <td>drake</td>\n",
       "      <td>6.917770e-03</td>\n",
       "      <td>False</td>\n",
       "      <td>hen</td>\n",
       "      <td>5.255170e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>667549055577362432</td>\n",
       "      <td>https://pbs.twimg.com/media/CUOcVCwWsAERUKY.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>electric_fan</td>\n",
       "      <td>0.984377</td>\n",
       "      <td>False</td>\n",
       "      <td>spotlight</td>\n",
       "      <td>7.736710e-03</td>\n",
       "      <td>False</td>\n",
       "      <td>lampshade</td>\n",
       "      <td>1.901230e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>667550882905632768</td>\n",
       "      <td>https://pbs.twimg.com/media/CUObvUJVEAAnYPF.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>web_site</td>\n",
       "      <td>0.998258</td>\n",
       "      <td>False</td>\n",
       "      <td>dishwasher</td>\n",
       "      <td>2.010840e-04</td>\n",
       "      <td>False</td>\n",
       "      <td>oscilloscope</td>\n",
       "      <td>1.417360e-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>667724302356258817</td>\n",
       "      <td>https://pbs.twimg.com/media/CUQ7tv3W4AA3KlI.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>ibex</td>\n",
       "      <td>0.619098</td>\n",
       "      <td>False</td>\n",
       "      <td>bighorn</td>\n",
       "      <td>1.251190e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>ram</td>\n",
       "      <td>7.467320e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>667766675769573376</td>\n",
       "      <td>https://pbs.twimg.com/media/CURiQMnUAAAPT2M.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>fire_engine</td>\n",
       "      <td>0.883493</td>\n",
       "      <td>False</td>\n",
       "      <td>tow_truck</td>\n",
       "      <td>7.473390e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>jeep</td>\n",
       "      <td>1.277260e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>667782464991965184</td>\n",
       "      <td>https://pbs.twimg.com/media/CURwm3cUkAARcO6.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>lorikeet</td>\n",
       "      <td>0.466149</td>\n",
       "      <td>False</td>\n",
       "      <td>hummingbird</td>\n",
       "      <td>8.301100e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>African_grey</td>\n",
       "      <td>5.424740e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>667866724293877760</td>\n",
       "      <td>https://pbs.twimg.com/media/CUS9PlUWwAANeAD.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>jigsaw_puzzle</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>prayer_rug</td>\n",
       "      <td>1.011300e-08</td>\n",
       "      <td>False</td>\n",
       "      <td>doormat</td>\n",
       "      <td>1.740170e-10</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>667873844930215936</td>\n",
       "      <td>https://pbs.twimg.com/media/CUTDtyGXIAARxus.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>common_iguana</td>\n",
       "      <td>0.999647</td>\n",
       "      <td>False</td>\n",
       "      <td>frilled_lizard</td>\n",
       "      <td>1.811500e-04</td>\n",
       "      <td>False</td>\n",
       "      <td>African_chameleon</td>\n",
       "      <td>1.283570e-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>667911425562669056</td>\n",
       "      <td>https://pbs.twimg.com/media/CUTl5m1WUAAabZG.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>frilled_lizard</td>\n",
       "      <td>0.257695</td>\n",
       "      <td>False</td>\n",
       "      <td>ox</td>\n",
       "      <td>2.351600e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>triceratops</td>\n",
       "      <td>8.531690e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>667937095915278337</td>\n",
       "      <td>https://pbs.twimg.com/media/CUT9PuQWwAABQv7.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>hamster</td>\n",
       "      <td>0.172078</td>\n",
       "      <td>False</td>\n",
       "      <td>guinea_pig</td>\n",
       "      <td>9.492420e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>Band_Aid</td>\n",
       "      <td>5.999520e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>668142349051129856</td>\n",
       "      <td>https://pbs.twimg.com/media/CUW37BzWsAAlJlN.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Angora</td>\n",
       "      <td>0.918834</td>\n",
       "      <td>False</td>\n",
       "      <td>hen</td>\n",
       "      <td>3.779340e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>wood_rabbit</td>\n",
       "      <td>1.101490e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>668154635664932864</td>\n",
       "      <td>https://pbs.twimg.com/media/CUXDGR2WcAAUQKz.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>Arctic_fox</td>\n",
       "      <td>0.473584</td>\n",
       "      <td>False</td>\n",
       "      <td>wallaby</td>\n",
       "      <td>2.614110e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>white_wolf</td>\n",
       "      <td>8.094780e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>668226093875376128</td>\n",
       "      <td>https://pbs.twimg.com/media/CUYEFlQXAAUkPGm.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>trombone</td>\n",
       "      <td>0.390339</td>\n",
       "      <td>False</td>\n",
       "      <td>cornet</td>\n",
       "      <td>3.141490e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>French_horn</td>\n",
       "      <td>2.551820e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>668291999406125056</td>\n",
       "      <td>https://pbs.twimg.com/media/CUZABzGW4AE5F0k.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>web_site</td>\n",
       "      <td>0.995535</td>\n",
       "      <td>False</td>\n",
       "      <td>skunk</td>\n",
       "      <td>1.363490e-03</td>\n",
       "      <td>False</td>\n",
       "      <td>badger</td>\n",
       "      <td>6.856500e-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>668466899341221888</td>\n",
       "      <td>https://pbs.twimg.com/media/CUbfGbbWoAApZth.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>shopping_basket</td>\n",
       "      <td>0.398361</td>\n",
       "      <td>False</td>\n",
       "      <td>hamper</td>\n",
       "      <td>3.632220e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>bassinet</td>\n",
       "      <td>8.417350e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>668544745690562560</td>\n",
       "      <td>https://pbs.twimg.com/media/CUcl5jeWsAA6ufS.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>bearskin</td>\n",
       "      <td>0.427870</td>\n",
       "      <td>False</td>\n",
       "      <td>bow</td>\n",
       "      <td>2.588580e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>panpipe</td>\n",
       "      <td>2.156260e-02</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>1839</th>\n",
       "      <td>837482249356513284</td>\n",
       "      <td>https://pbs.twimg.com/media/C59VqMUXEAAzldG.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>birdhouse</td>\n",
       "      <td>0.541196</td>\n",
       "      <td>False</td>\n",
       "      <td>can_opener</td>\n",
       "      <td>1.210940e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>carton</td>\n",
       "      <td>5.613670e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1844</th>\n",
       "      <td>838916489579200512</td>\n",
       "      <td>https://pbs.twimg.com/media/C6RkiQZUsAAM4R4.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>web_site</td>\n",
       "      <td>0.993651</td>\n",
       "      <td>False</td>\n",
       "      <td>monitor</td>\n",
       "      <td>1.405900e-03</td>\n",
       "      <td>False</td>\n",
       "      <td>envelope</td>\n",
       "      <td>1.093090e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1847</th>\n",
       "      <td>839290600511926273</td>\n",
       "      <td>https://pbs.twimg.com/media/C6XBt9XXEAEEW9U.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>web_site</td>\n",
       "      <td>0.670892</td>\n",
       "      <td>False</td>\n",
       "      <td>monitor</td>\n",
       "      <td>1.015650e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>screen</td>\n",
       "      <td>7.530610e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1851</th>\n",
       "      <td>840370681858686976</td>\n",
       "      <td>https://pbs.twimg.com/media/C6mYrK0UwAANhep.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>teapot</td>\n",
       "      <td>0.981819</td>\n",
       "      <td>False</td>\n",
       "      <td>cup</td>\n",
       "      <td>1.402580e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>coffeepot</td>\n",
       "      <td>2.420540e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1853</th>\n",
       "      <td>840696689258311684</td>\n",
       "      <td>https://pbs.twimg.com/media/C6rBLenU0AAr8MN.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>web_site</td>\n",
       "      <td>0.841768</td>\n",
       "      <td>False</td>\n",
       "      <td>rule</td>\n",
       "      <td>7.087310e-03</td>\n",
       "      <td>False</td>\n",
       "      <td>envelope</td>\n",
       "      <td>6.820300e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>844580511645339650</td>\n",
       "      <td>https://pbs.twimg.com/media/C7iNfq1W0AAcbsR.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>washer</td>\n",
       "      <td>0.903064</td>\n",
       "      <td>False</td>\n",
       "      <td>dishwasher</td>\n",
       "      <td>3.248900e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>printer</td>\n",
       "      <td>1.645620e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>847962785489326080</td>\n",
       "      <td>https://pbs.twimg.com/media/C8SRpHNUIAARB3j.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>sea_lion</td>\n",
       "      <td>0.882654</td>\n",
       "      <td>False</td>\n",
       "      <td>mink</td>\n",
       "      <td>6.688020e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>otter</td>\n",
       "      <td>2.567870e-02</td>\n",
       "      <td>False</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>coffee_mug</td>\n",
       "      <td>0.633652</td>\n",
       "      <td>False</td>\n",
       "      <td>cup</td>\n",
       "      <td>2.733920e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>toilet_tissue</td>\n",
       "      <td>6.665580e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>849051919805034497</td>\n",
       "      <td>https://pbs.twimg.com/media/C8hwNxbXYAAwyVG.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>fountain</td>\n",
       "      <td>0.997509</td>\n",
       "      <td>False</td>\n",
       "      <td>American_black_bear</td>\n",
       "      <td>1.413120e-03</td>\n",
       "      <td>False</td>\n",
       "      <td>sundial</td>\n",
       "      <td>6.811150e-04</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>849336543269576704</td>\n",
       "      <td>https://pbs.twimg.com/media/C8lzFC4XcAAQxB4.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>patio</td>\n",
       "      <td>0.521788</td>\n",
       "      <td>False</td>\n",
       "      <td>prison</td>\n",
       "      <td>1.495440e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>restaurant</td>\n",
       "      <td>2.715260e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>851464819735769094</td>\n",
       "      <td>https://pbs.twimg.com/media/C9ECujZXsAAPCSM.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>web_site</td>\n",
       "      <td>0.919649</td>\n",
       "      <td>False</td>\n",
       "      <td>menu</td>\n",
       "      <td>2.630610e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>crossword_puzzle</td>\n",
       "      <td>3.481510e-03</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1902</th>\n",
       "      <td>851861385021730816</td>\n",
       "      <td>https://pbs.twimg.com/media/C8W6sY_W0AEmttW.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>pencil_box</td>\n",
       "      <td>0.662183</td>\n",
       "      <td>False</td>\n",
       "      <td>purse</td>\n",
       "      <td>6.650550e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>pillow</td>\n",
       "      <td>4.472530e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1905</th>\n",
       "      <td>852226086759018497</td>\n",
       "      <td>https://pbs.twimg.com/ext_tw_video_thumb/85222...</td>\n",
       "      <td>1</td>\n",
       "      <td>prison</td>\n",
       "      <td>0.352793</td>\n",
       "      <td>False</td>\n",
       "      <td>dishwasher</td>\n",
       "      <td>1.107230e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>file</td>\n",
       "      <td>9.411200e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906</th>\n",
       "      <td>852311364735569921</td>\n",
       "      <td>https://pbs.twimg.com/media/C9QEqZ7XYAIR7fS.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>barbell</td>\n",
       "      <td>0.971581</td>\n",
       "      <td>False</td>\n",
       "      <td>dumbbell</td>\n",
       "      <td>2.841790e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>go-kart</td>\n",
       "      <td>5.595040e-07</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>853299958564483072</td>\n",
       "      <td>https://pbs.twimg.com/media/C9eHyF7XgAAOxPM.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>grille</td>\n",
       "      <td>0.652280</td>\n",
       "      <td>False</td>\n",
       "      <td>beach_wagon</td>\n",
       "      <td>1.128460e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>convertible</td>\n",
       "      <td>8.625230e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>859074603037188101</td>\n",
       "      <td>https://pbs.twimg.com/media/C-wLyufW0AA546I.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>revolver</td>\n",
       "      <td>0.190292</td>\n",
       "      <td>False</td>\n",
       "      <td>projectile</td>\n",
       "      <td>1.490640e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>fountain</td>\n",
       "      <td>6.604660e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936</th>\n",
       "      <td>860184849394610176</td>\n",
       "      <td>https://pbs.twimg.com/media/C-_9jWWUwAAnwkd.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>chimpanzee</td>\n",
       "      <td>0.267612</td>\n",
       "      <td>False</td>\n",
       "      <td>gorilla</td>\n",
       "      <td>1.042930e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>orangutan</td>\n",
       "      <td>5.990750e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>860276583193509888</td>\n",
       "      <td>https://pbs.twimg.com/media/C_BQ_NlVwAAgYGD.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>lakeside</td>\n",
       "      <td>0.312299</td>\n",
       "      <td>False</td>\n",
       "      <td>dock</td>\n",
       "      <td>1.598420e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>canoe</td>\n",
       "      <td>7.079450e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>860924035999428608</td>\n",
       "      <td>https://pbs.twimg.com/media/C_KVJjDXsAEUCWn.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>envelope</td>\n",
       "      <td>0.933016</td>\n",
       "      <td>False</td>\n",
       "      <td>oscilloscope</td>\n",
       "      <td>1.259140e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>paper_towel</td>\n",
       "      <td>1.117850e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>862457590147678208</td>\n",
       "      <td>https://pbs.twimg.com/media/C_gQmaTUMAAPYSS.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>home_theater</td>\n",
       "      <td>0.496348</td>\n",
       "      <td>False</td>\n",
       "      <td>studio_couch</td>\n",
       "      <td>1.672560e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>barber_chair</td>\n",
       "      <td>5.262500e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>863907417377173506</td>\n",
       "      <td>https://pbs.twimg.com/media/C_03NPeUQAAgrMl.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>marmot</td>\n",
       "      <td>0.358828</td>\n",
       "      <td>False</td>\n",
       "      <td>meerkat</td>\n",
       "      <td>1.747030e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>weasel</td>\n",
       "      <td>1.234850e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>864873206498414592</td>\n",
       "      <td>https://pbs.twimg.com/media/DAClmHkXcAA1kSv.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>pole</td>\n",
       "      <td>0.478616</td>\n",
       "      <td>False</td>\n",
       "      <td>lakeside</td>\n",
       "      <td>1.141820e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>wreck</td>\n",
       "      <td>5.592650e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>870063196459192321</td>\n",
       "      <td>https://pbs.twimg.com/media/DBMV3NnXUAAm0Pp.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>comic_book</td>\n",
       "      <td>0.534409</td>\n",
       "      <td>False</td>\n",
       "      <td>envelope</td>\n",
       "      <td>2.807220e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>book_jacket</td>\n",
       "      <td>4.378550e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>870804317367881728</td>\n",
       "      <td>https://pbs.twimg.com/media/DBW35ZsVoAEWZUU.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>home_theater</td>\n",
       "      <td>0.168290</td>\n",
       "      <td>False</td>\n",
       "      <td>sandbar</td>\n",
       "      <td>9.804040e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>television</td>\n",
       "      <td>7.972940e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>879050749262655488</td>\n",
       "      <td>https://pbs.twimg.com/media/DDMD_phXoAQ1qf0.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>tabby</td>\n",
       "      <td>0.311861</td>\n",
       "      <td>False</td>\n",
       "      <td>window_screen</td>\n",
       "      <td>1.691230e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>Egyptian_cat</td>\n",
       "      <td>1.329320e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>880935762899988482</td>\n",
       "      <td>https://pbs.twimg.com/media/DDm2Z5aXUAEDS2u.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>street_sign</td>\n",
       "      <td>0.251801</td>\n",
       "      <td>False</td>\n",
       "      <td>umbrella</td>\n",
       "      <td>1.151230e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>traffic_light</td>\n",
       "      <td>6.953380e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>881268444196462592</td>\n",
       "      <td>https://pbs.twimg.com/media/DDrk-f9WAAI-WQv.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>tusker</td>\n",
       "      <td>0.473303</td>\n",
       "      <td>False</td>\n",
       "      <td>Indian_elephant</td>\n",
       "      <td>2.456460e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>ibex</td>\n",
       "      <td>5.566070e-02</td>\n",
       "      <td>False</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>1.399520e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>car_wheel</td>\n",
       "      <td>4.417270e-02</td>\n",
       "      <td>False</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>2.917540e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>shopping_cart</td>\n",
       "      <td>2.632080e-02</td>\n",
       "      <td>False</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>8.585110e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>banana</td>\n",
       "      <td>7.611000e-02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>324 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id                                            jpg_url  \\\n",
       "6     666051853826850816    https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg   \n",
       "17    666104133288665088    https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg   \n",
       "18    666268910803644416    https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg   \n",
       "21    666293911632134144    https://pbs.twimg.com/media/CT8mx7KW4AEQu8N.jpg   \n",
       "25    666362758909284353    https://pbs.twimg.com/media/CT9lXGsUcAAyUFt.jpg   \n",
       "29    666411507551481857    https://pbs.twimg.com/media/CT-RugiWIAELEaq.jpg   \n",
       "45    666786068205871104    https://pbs.twimg.com/media/CUDmZIkWcAAIPPe.jpg   \n",
       "50    666837028449972224    https://pbs.twimg.com/media/CUEUva1WsAA2jPb.jpg   \n",
       "51    666983947667116034    https://pbs.twimg.com/media/CUGaXDhW4AY9JUH.jpg   \n",
       "53    667012601033924608    https://pbs.twimg.com/media/CUG0bC0U8AAw2su.jpg   \n",
       "56    667065535570550784    https://pbs.twimg.com/media/CUHkkJpXIAA2w3n.jpg   \n",
       "69    667188689915760640    https://pbs.twimg.com/media/CUJUk2iWUAAVtOv.jpg   \n",
       "73    667369227918143488    https://pbs.twimg.com/media/CUL4xR9UkAEdlJ6.jpg   \n",
       "77    667437278097252352    https://pbs.twimg.com/media/CUM2qWaWoAUZ06L.jpg   \n",
       "78    667443425659232256    https://pbs.twimg.com/media/CUM8QZwW4AAVsBl.jpg   \n",
       "93    667549055577362432    https://pbs.twimg.com/media/CUOcVCwWsAERUKY.jpg   \n",
       "94    667550882905632768    https://pbs.twimg.com/media/CUObvUJVEAAnYPF.jpg   \n",
       "96    667724302356258817    https://pbs.twimg.com/media/CUQ7tv3W4AA3KlI.jpg   \n",
       "98    667766675769573376    https://pbs.twimg.com/media/CURiQMnUAAAPT2M.jpg   \n",
       "100   667782464991965184    https://pbs.twimg.com/media/CURwm3cUkAARcO6.jpg   \n",
       "106   667866724293877760    https://pbs.twimg.com/media/CUS9PlUWwAANeAD.jpg   \n",
       "107   667873844930215936    https://pbs.twimg.com/media/CUTDtyGXIAARxus.jpg   \n",
       "112   667911425562669056    https://pbs.twimg.com/media/CUTl5m1WUAAabZG.jpg   \n",
       "115   667937095915278337    https://pbs.twimg.com/media/CUT9PuQWwAABQv7.jpg   \n",
       "117   668142349051129856    https://pbs.twimg.com/media/CUW37BzWsAAlJlN.jpg   \n",
       "118   668154635664932864    https://pbs.twimg.com/media/CUXDGR2WcAAUQKz.jpg   \n",
       "123   668226093875376128    https://pbs.twimg.com/media/CUYEFlQXAAUkPGm.jpg   \n",
       "130   668291999406125056    https://pbs.twimg.com/media/CUZABzGW4AE5F0k.jpg   \n",
       "132   668466899341221888    https://pbs.twimg.com/media/CUbfGbbWoAApZth.jpg   \n",
       "140   668544745690562560    https://pbs.twimg.com/media/CUcl5jeWsAA6ufS.jpg   \n",
       "...                  ...                                                ...   \n",
       "1839  837482249356513284    https://pbs.twimg.com/media/C59VqMUXEAAzldG.jpg   \n",
       "1844  838916489579200512    https://pbs.twimg.com/media/C6RkiQZUsAAM4R4.jpg   \n",
       "1847  839290600511926273    https://pbs.twimg.com/media/C6XBt9XXEAEEW9U.jpg   \n",
       "1851  840370681858686976    https://pbs.twimg.com/media/C6mYrK0UwAANhep.jpg   \n",
       "1853  840696689258311684    https://pbs.twimg.com/media/C6rBLenU0AAr8MN.jpg   \n",
       "1869  844580511645339650    https://pbs.twimg.com/media/C7iNfq1W0AAcbsR.jpg   \n",
       "1886  847962785489326080    https://pbs.twimg.com/media/C8SRpHNUIAARB3j.jpg   \n",
       "1887  847971574464610304    https://pbs.twimg.com/media/C8SZH1EWAAAIRRF.jpg   \n",
       "1891  849051919805034497    https://pbs.twimg.com/media/C8hwNxbXYAAwyVG.jpg   \n",
       "1892  849336543269576704    https://pbs.twimg.com/media/C8lzFC4XcAAQxB4.jpg   \n",
       "1900  851464819735769094    https://pbs.twimg.com/media/C9ECujZXsAAPCSM.jpg   \n",
       "1902  851861385021730816    https://pbs.twimg.com/media/C8W6sY_W0AEmttW.jpg   \n",
       "1905  852226086759018497  https://pbs.twimg.com/ext_tw_video_thumb/85222...   \n",
       "1906  852311364735569921    https://pbs.twimg.com/media/C9QEqZ7XYAIR7fS.jpg   \n",
       "1910  853299958564483072    https://pbs.twimg.com/media/C9eHyF7XgAAOxPM.jpg   \n",
       "1931  859074603037188101    https://pbs.twimg.com/media/C-wLyufW0AA546I.jpg   \n",
       "1936  860184849394610176    https://pbs.twimg.com/media/C-_9jWWUwAAnwkd.jpg   \n",
       "1937  860276583193509888    https://pbs.twimg.com/media/C_BQ_NlVwAAgYGD.jpg   \n",
       "1940  860924035999428608    https://pbs.twimg.com/media/C_KVJjDXsAEUCWn.jpg   \n",
       "1946  862457590147678208    https://pbs.twimg.com/media/C_gQmaTUMAAPYSS.jpg   \n",
       "1953  863907417377173506    https://pbs.twimg.com/media/C_03NPeUQAAgrMl.jpg   \n",
       "1956  864873206498414592    https://pbs.twimg.com/media/DAClmHkXcAA1kSv.jpg   \n",
       "1975  870063196459192321    https://pbs.twimg.com/media/DBMV3NnXUAAm0Pp.jpg   \n",
       "1979  870804317367881728    https://pbs.twimg.com/media/DBW35ZsVoAEWZUU.jpg   \n",
       "2012  879050749262655488    https://pbs.twimg.com/media/DDMD_phXoAQ1qf0.jpg   \n",
       "2021  880935762899988482    https://pbs.twimg.com/media/DDm2Z5aXUAEDS2u.jpg   \n",
       "2022  881268444196462592    https://pbs.twimg.com/media/DDrk-f9WAAI-WQv.jpg   \n",
       "2046  886680336477933568    https://pbs.twimg.com/media/DE4fEDzWAAAyHMM.jpg   \n",
       "2052  887517139158093824  https://pbs.twimg.com/ext_tw_video_thumb/88751...   \n",
       "2074  892420643555336193    https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg   \n",
       "\n",
       "      img_num                p1   p1_conf  p1_dog                   p2  \\\n",
       "6           1        box_turtle  0.933012   False           mud_turtle   \n",
       "17          1               hen  0.965932   False                 cock   \n",
       "18          1  desktop_computer  0.086502   False                 desk   \n",
       "21          1  three-toed_sloth  0.914671   False                otter   \n",
       "25          1        guinea_pig  0.996496   False                skunk   \n",
       "29          1              coho  0.404640   False           barracouta   \n",
       "45          1             snail  0.999888   False                 slug   \n",
       "50          1       triceratops  0.442113   False            armadillo   \n",
       "51          1              swab  0.589446   False            chain_saw   \n",
       "53          1             hyena  0.987230   False  African_hunting_dog   \n",
       "56          1     jigsaw_puzzle  0.560001   False              doormat   \n",
       "69          1            vacuum  0.335830   False                 swab   \n",
       "73          1             teddy  0.709545   False           bath_towel   \n",
       "77          1         porcupine  0.989154   False           bath_towel   \n",
       "78          1             goose  0.980815   False                drake   \n",
       "93          1      electric_fan  0.984377   False            spotlight   \n",
       "94          1          web_site  0.998258   False           dishwasher   \n",
       "96          1              ibex  0.619098   False              bighorn   \n",
       "98          1       fire_engine  0.883493   False            tow_truck   \n",
       "100         1          lorikeet  0.466149   False          hummingbird   \n",
       "106         1     jigsaw_puzzle  1.000000   False           prayer_rug   \n",
       "107         1     common_iguana  0.999647   False       frilled_lizard   \n",
       "112         1    frilled_lizard  0.257695   False                   ox   \n",
       "115         1           hamster  0.172078   False           guinea_pig   \n",
       "117         1            Angora  0.918834   False                  hen   \n",
       "118         1        Arctic_fox  0.473584   False              wallaby   \n",
       "123         1          trombone  0.390339   False               cornet   \n",
       "130         1          web_site  0.995535   False                skunk   \n",
       "132         1   shopping_basket  0.398361   False               hamper   \n",
       "140         1          bearskin  0.427870   False                  bow   \n",
       "...       ...               ...       ...     ...                  ...   \n",
       "1839        2         birdhouse  0.541196   False           can_opener   \n",
       "1844        2          web_site  0.993651   False              monitor   \n",
       "1847        1          web_site  0.670892   False              monitor   \n",
       "1851        1            teapot  0.981819   False                  cup   \n",
       "1853        1          web_site  0.841768   False                 rule   \n",
       "1869        1            washer  0.903064   False           dishwasher   \n",
       "1886        1          sea_lion  0.882654   False                 mink   \n",
       "1887        1        coffee_mug  0.633652   False                  cup   \n",
       "1891        1          fountain  0.997509   False  American_black_bear   \n",
       "1892        1             patio  0.521788   False               prison   \n",
       "1900        2          web_site  0.919649   False                 menu   \n",
       "1902        1        pencil_box  0.662183   False                purse   \n",
       "1905        1            prison  0.352793   False           dishwasher   \n",
       "1906        1           barbell  0.971581   False             dumbbell   \n",
       "1910        1            grille  0.652280   False          beach_wagon   \n",
       "1931        1          revolver  0.190292   False           projectile   \n",
       "1936        1        chimpanzee  0.267612   False              gorilla   \n",
       "1937        1          lakeside  0.312299   False                 dock   \n",
       "1940        2          envelope  0.933016   False         oscilloscope   \n",
       "1946        1      home_theater  0.496348   False         studio_couch   \n",
       "1953        1            marmot  0.358828   False              meerkat   \n",
       "1956        2              pole  0.478616   False             lakeside   \n",
       "1975        1        comic_book  0.534409   False             envelope   \n",
       "1979        1      home_theater  0.168290   False              sandbar   \n",
       "2012        1             tabby  0.311861   False        window_screen   \n",
       "2021        1       street_sign  0.251801   False             umbrella   \n",
       "2022        1            tusker  0.473303   False      Indian_elephant   \n",
       "2046        1       convertible  0.738995   False           sports_car   \n",
       "2052        1         limousine  0.130432   False            tow_truck   \n",
       "2074        1            orange  0.097049   False                bagel   \n",
       "\n",
       "           p2_conf  p2_dog                  p3       p3_conf  p3_dog  \n",
       "6     4.588540e-02   False            terrapin  1.788530e-02   False  \n",
       "17    3.391940e-02   False           partridge  5.206580e-05   False  \n",
       "18    8.554740e-02   False            bookcase  7.947970e-02   False  \n",
       "21    1.525000e-02   False      great_grey_owl  1.320720e-02   False  \n",
       "25    2.402450e-03   False             hamster  4.608630e-04   False  \n",
       "29    2.714850e-01   False                 gar  1.899450e-01   False  \n",
       "45    5.514170e-05   False               acorn  2.625800e-05   False  \n",
       "50    1.140710e-01   False       common_iguana  4.325530e-02   False  \n",
       "51    1.901420e-01   False                 wig  3.450970e-02   False  \n",
       "53    1.260080e-02   False              coyote  5.735010e-05   False  \n",
       "56    1.032590e-01   False        space_heater  4.256800e-02   False  \n",
       "69    2.652780e-01   False       toilet_tissue  1.407030e-01   False  \n",
       "73    1.272850e-01   False  Christmas_stocking  2.856750e-02   False  \n",
       "77    6.300490e-03   False              badger  9.663400e-04   False  \n",
       "78    6.917770e-03   False                 hen  5.255170e-03   False  \n",
       "93    7.736710e-03   False           lampshade  1.901230e-03   False  \n",
       "94    2.010840e-04   False        oscilloscope  1.417360e-04   False  \n",
       "96    1.251190e-01   False                 ram  7.467320e-02   False  \n",
       "98    7.473390e-02   False                jeep  1.277260e-02   False  \n",
       "100   8.301100e-02   False        African_grey  5.424740e-02   False  \n",
       "106   1.011300e-08   False             doormat  1.740170e-10   False  \n",
       "107   1.811500e-04   False   African_chameleon  1.283570e-04   False  \n",
       "112   2.351600e-01   False         triceratops  8.531690e-02   False  \n",
       "115   9.492420e-02   False            Band_Aid  5.999520e-02   False  \n",
       "117   3.779340e-02   False         wood_rabbit  1.101490e-02   False  \n",
       "118   2.614110e-01   False          white_wolf  8.094780e-02   False  \n",
       "123   3.141490e-01   False         French_horn  2.551820e-01   False  \n",
       "130   1.363490e-03   False              badger  6.856500e-04   False  \n",
       "132   3.632220e-01   False            bassinet  8.417350e-02   False  \n",
       "140   2.588580e-01   False             panpipe  2.156260e-02   False  \n",
       "...            ...     ...                 ...           ...     ...  \n",
       "1839  1.210940e-01   False              carton  5.613670e-02   False  \n",
       "1844  1.405900e-03   False            envelope  1.093090e-03   False  \n",
       "1847  1.015650e-01   False              screen  7.530610e-02   False  \n",
       "1851  1.402580e-02   False           coffeepot  2.420540e-03   False  \n",
       "1853  7.087310e-03   False            envelope  6.820300e-03   False  \n",
       "1869  3.248900e-02   False             printer  1.645620e-02   False  \n",
       "1886  6.688020e-02   False               otter  2.567870e-02   False  \n",
       "1887  2.733920e-01   False       toilet_tissue  6.665580e-02   False  \n",
       "1891  1.413120e-03   False             sundial  6.811150e-04   False  \n",
       "1892  1.495440e-01   False          restaurant  2.715260e-02   False  \n",
       "1900  2.630610e-02   False    crossword_puzzle  3.481510e-03   False  \n",
       "1902  6.650550e-02   False              pillow  4.472530e-02   False  \n",
       "1905  1.107230e-01   False                file  9.411200e-02   False  \n",
       "1906  2.841790e-02   False             go-kart  5.595040e-07   False  \n",
       "1910  1.128460e-01   False         convertible  8.625230e-02   False  \n",
       "1931  1.490640e-01   False            fountain  6.604660e-02   False  \n",
       "1936  1.042930e-01   False           orangutan  5.990750e-02   False  \n",
       "1937  1.598420e-01   False               canoe  7.079450e-02   False  \n",
       "1940  1.259140e-02   False         paper_towel  1.117850e-02   False  \n",
       "1946  1.672560e-01   False        barber_chair  5.262500e-02   False  \n",
       "1953  1.747030e-01   False              weasel  1.234850e-01   False  \n",
       "1956  1.141820e-01   False               wreck  5.592650e-02   False  \n",
       "1975  2.807220e-01   False         book_jacket  4.378550e-02   False  \n",
       "1979  9.804040e-02   False          television  7.972940e-02   False  \n",
       "2012  1.691230e-01   False        Egyptian_cat  1.329320e-01   False  \n",
       "2021  1.151230e-01   False       traffic_light  6.953380e-02   False  \n",
       "2022  2.456460e-01   False                ibex  5.566070e-02   False  \n",
       "2046  1.399520e-01   False           car_wheel  4.417270e-02   False  \n",
       "2052  2.917540e-02   False       shopping_cart  2.632080e-02   False  \n",
       "2074  8.585110e-02   False              banana  7.611000e-02   False  \n",
       "\n",
       "[324 rows x 12 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#query for non dog predictions\n",
    "image_predictions.query(\"p1_dog== False & p2_dog== False &p3_dog == False \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tweets ID Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>contributors</th>\n",
       "      <th>coordinates</th>\n",
       "      <th>created_at</th>\n",
       "      <th>display_text_range</th>\n",
       "      <th>entities</th>\n",
       "      <th>extended_entities</th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>favorited</th>\n",
       "      <th>full_text</th>\n",
       "      <th>geo</th>\n",
       "      <th>...</th>\n",
       "      <th>quoted_status</th>\n",
       "      <th>quoted_status_id</th>\n",
       "      <th>quoted_status_id_str</th>\n",
       "      <th>quoted_status_permalink</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>retweeted</th>\n",
       "      <th>retweeted_status</th>\n",
       "      <th>source</th>\n",
       "      <th>truncated</th>\n",
       "      <th>user</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 16:23:56</td>\n",
       "      <td>[0, 85]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 892420639486877696, 'id_str'...</td>\n",
       "      <td>33835</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Phineas. He's a mystical boy. Only eve...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7015</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-08-01 00:17:27</td>\n",
       "      <td>[0, 138]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 892177413194625024, 'id_str'...</td>\n",
       "      <td>29348</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Tilly. She's just checking pup on you....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5302</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-31 00:18:03</td>\n",
       "      <td>[0, 121]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 891815175371796480, 'id_str'...</td>\n",
       "      <td>22069</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Archie. He is a rare Norwegian Pouncin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3484</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-30 15:58:51</td>\n",
       "      <td>[0, 79]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 891689552724799489, 'id_str'...</td>\n",
       "      <td>36960</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Darla. She commenced a snooze mid meal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7229</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-07-29 16:00:24</td>\n",
       "      <td>[0, 138]</td>\n",
       "      <td>{'hashtags': [{'text': 'BarkWeek', 'indices': ...</td>\n",
       "      <td>{'media': [{'id': 891327551943041024, 'id_str'...</td>\n",
       "      <td>35338</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Franklin. He would like you to stop ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7768</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   contributors  coordinates          created_at display_text_range  \\\n",
       "0           NaN          NaN 2017-08-01 16:23:56            [0, 85]   \n",
       "1           NaN          NaN 2017-08-01 00:17:27           [0, 138]   \n",
       "2           NaN          NaN 2017-07-31 00:18:03           [0, 121]   \n",
       "3           NaN          NaN 2017-07-30 15:58:51            [0, 79]   \n",
       "4           NaN          NaN 2017-07-29 16:00:24           [0, 138]   \n",
       "\n",
       "                                            entities  \\\n",
       "0  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "3  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "4  {'hashtags': [{'text': 'BarkWeek', 'indices': ...   \n",
       "\n",
       "                                   extended_entities  favorite_count  \\\n",
       "0  {'media': [{'id': 892420639486877696, 'id_str'...           33835   \n",
       "1  {'media': [{'id': 892177413194625024, 'id_str'...           29348   \n",
       "2  {'media': [{'id': 891815175371796480, 'id_str'...           22069   \n",
       "3  {'media': [{'id': 891689552724799489, 'id_str'...           36960   \n",
       "4  {'media': [{'id': 891327551943041024, 'id_str'...           35338   \n",
       "\n",
       "   favorited                                          full_text  geo  \\\n",
       "0      False  This is Phineas. He's a mystical boy. Only eve...  NaN   \n",
       "1      False  This is Tilly. She's just checking pup on you....  NaN   \n",
       "2      False  This is Archie. He is a rare Norwegian Pouncin...  NaN   \n",
       "3      False  This is Darla. She commenced a snooze mid meal...  NaN   \n",
       "4      False  This is Franklin. He would like you to stop ca...  NaN   \n",
       "\n",
       "                         ...                          quoted_status  \\\n",
       "0                        ...                                    NaN   \n",
       "1                        ...                                    NaN   \n",
       "2                        ...                                    NaN   \n",
       "3                        ...                                    NaN   \n",
       "4                        ...                                    NaN   \n",
       "\n",
       "   quoted_status_id quoted_status_id_str  quoted_status_permalink  \\\n",
       "0               NaN                  NaN                      NaN   \n",
       "1               NaN                  NaN                      NaN   \n",
       "2               NaN                  NaN                      NaN   \n",
       "3               NaN                  NaN                      NaN   \n",
       "4               NaN                  NaN                      NaN   \n",
       "\n",
       "   retweet_count  retweeted  retweeted_status  \\\n",
       "0           7015      False               NaN   \n",
       "1           5302      False               NaN   \n",
       "2           3484      False               NaN   \n",
       "3           7229      False               NaN   \n",
       "4           7768      False               NaN   \n",
       "\n",
       "                                              source truncated  \\\n",
       "0  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "1  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "2  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "3  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "4  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "\n",
       "                                                user  \n",
       "0  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "1  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "2  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "3  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "4  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the tweet IDs table\n",
    "tweet_ids.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>718</th>\n",
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       "      <td>2016-09-27 16:06:28</td>\n",
       "      <td>[0, 105]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
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       "      <td>False</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-17 01:35:24</td>\n",
       "      <td>[0, 99]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
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       "      <td>1116</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Juckson. He's totally on his way to a ...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>355</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
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       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-08 03:09:46</td>\n",
       "      <td>[0, 135]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 674063277207457792, 'id_str'...</td>\n",
       "      <td>5743</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Earl. Earl is lost. Someone help Earl....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1911</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
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       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-07-06 15:54:42</td>\n",
       "      <td>[0, 114]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 750719617786609664, 'id_str'...</td>\n",
       "      <td>12282</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Meyer. He has to hold somebody's hand ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4596</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
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       "    <tr>\n",
       "      <th>1388</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-02-13 22:29:29</td>\n",
       "      <td>[0, 134]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 698635005506015234, 'id_str'...</td>\n",
       "      <td>1198</td>\n",
       "      <td>False</td>\n",
       "      <td>Here we are witnessing five Guatemalan Birch F...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-05-05 02:21:37</td>\n",
       "      <td>[0, 81]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 728046957697052672, 'id_str'...</td>\n",
       "      <td>3950</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Raymond. He controls fountains with hi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1086</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2224</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-20 19:12:01</td>\n",
       "      <td>[0, 134]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 667782457777754112, 'id_str'...</td>\n",
       "      <td>363</td>\n",
       "      <td>False</td>\n",
       "      <td>Super rare dog. Endangered (?). Thinks it's fu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-10-06 01:23:05</td>\n",
       "      <td>[0, 117]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 783839953138683904, 'id_str'...</td>\n",
       "      <td>28425</td>\n",
       "      <td>False</td>\n",
       "      <td>This is Riley. His owner put a donut pillow ar...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10062</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-27 19:05:49</td>\n",
       "      <td>[0, 103]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1369</td>\n",
       "      <td>False</td>\n",
       "      <td>PUPDATE: just noticed this dog has some extra ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>198</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-11-18 04:35:11</td>\n",
       "      <td>[0, 138]</td>\n",
       "      <td>{'hashtags': [], 'symbols': [], 'user_mentions...</td>\n",
       "      <td>{'media': [{'id': 666837024717058048, 'id_str'...</td>\n",
       "      <td>703</td>\n",
       "      <td>False</td>\n",
       "      <td>My goodness. Very rare dog here. Large. Tail d...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>474</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 4196983835, 'id_str': '4196983835', 'na...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      contributors  coordinates          created_at display_text_range  \\\n",
       "718            NaN          NaN 2016-09-27 16:06:28           [0, 105]   \n",
       "1767           NaN          NaN 2015-12-17 01:35:24            [0, 99]   \n",
       "1896           NaN          NaN 2015-12-08 03:09:46           [0, 135]   \n",
       "935            NaN          NaN 2016-07-06 15:54:42           [0, 114]   \n",
       "1388           NaN          NaN 2016-02-13 22:29:29           [0, 134]   \n",
       "1109           NaN          NaN 2016-05-05 02:21:37            [0, 81]   \n",
       "2224           NaN          NaN 2015-11-20 19:12:01           [0, 134]   \n",
       "695            NaN          NaN 2016-10-06 01:23:05           [0, 117]   \n",
       "1469           NaN          NaN 2016-01-27 19:05:49           [0, 103]   \n",
       "2275           NaN          NaN 2015-11-18 04:35:11           [0, 138]   \n",
       "\n",
       "                                               entities  \\\n",
       "718   {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1767  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1896  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "935   {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1388  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1109  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2224  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "695   {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "1469  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "2275  {'hashtags': [], 'symbols': [], 'user_mentions...   \n",
       "\n",
       "                                      extended_entities  favorite_count  \\\n",
       "718   {'media': [{'id': 780800773873668096, 'id_str'...            5201   \n",
       "1767  {'media': [{'id': 677301027595530240, 'id_str'...            1116   \n",
       "1896  {'media': [{'id': 674063277207457792, 'id_str'...            5743   \n",
       "935   {'media': [{'id': 750719617786609664, 'id_str'...           12282   \n",
       "1388  {'media': [{'id': 698635005506015234, 'id_str'...            1198   \n",
       "1109  {'media': [{'id': 728046957697052672, 'id_str'...            3950   \n",
       "2224  {'media': [{'id': 667782457777754112, 'id_str'...             363   \n",
       "695   {'media': [{'id': 783839953138683904, 'id_str'...           28425   \n",
       "1469                                                NaN            1369   \n",
       "2275  {'media': [{'id': 666837024717058048, 'id_str'...             703   \n",
       "\n",
       "      favorited                                          full_text  geo  \\\n",
       "718       False  This is Koda. He has a weird relationship with...  NaN   \n",
       "1767      False  This is Juckson. He's totally on his way to a ...  NaN   \n",
       "1896      False  This is Earl. Earl is lost. Someone help Earl....  NaN   \n",
       "935       False  This is Meyer. He has to hold somebody's hand ...  NaN   \n",
       "1388      False  Here we are witnessing five Guatemalan Birch F...  NaN   \n",
       "1109      False  This is Raymond. He controls fountains with hi...  NaN   \n",
       "2224      False  Super rare dog. Endangered (?). Thinks it's fu...  NaN   \n",
       "695       False  This is Riley. His owner put a donut pillow ar...  NaN   \n",
       "1469      False  PUPDATE: just noticed this dog has some extra ...  NaN   \n",
       "2275      False  My goodness. Very rare dog here. Large. Tail d...  NaN   \n",
       "\n",
       "                            ...                          quoted_status  \\\n",
       "718                         ...                                    NaN   \n",
       "1767                        ...                                    NaN   \n",
       "1896                        ...                                    NaN   \n",
       "935                         ...                                    NaN   \n",
       "1388                        ...                                    NaN   \n",
       "1109                        ...                                    NaN   \n",
       "2224                        ...                                    NaN   \n",
       "695                         ...                                    NaN   \n",
       "1469                        ...                                    NaN   \n",
       "2275                        ...                                    NaN   \n",
       "\n",
       "      quoted_status_id quoted_status_id_str  quoted_status_permalink  \\\n",
       "718                NaN                  NaN                      NaN   \n",
       "1767               NaN                  NaN                      NaN   \n",
       "1896               NaN                  NaN                      NaN   \n",
       "935                NaN                  NaN                      NaN   \n",
       "1388               NaN                  NaN                      NaN   \n",
       "1109               NaN                  NaN                      NaN   \n",
       "2224               NaN                  NaN                      NaN   \n",
       "695                NaN                  NaN                      NaN   \n",
       "1469               NaN                  NaN                      NaN   \n",
       "2275               NaN                  NaN                      NaN   \n",
       "\n",
       "      retweet_count  retweeted  retweeted_status  \\\n",
       "718            1202      False               NaN   \n",
       "1767            355      False               NaN   \n",
       "1896           1911      False               NaN   \n",
       "935            4596      False               NaN   \n",
       "1388            315      False               NaN   \n",
       "1109           1086      False               NaN   \n",
       "2224            210      False               NaN   \n",
       "695           10062      False               NaN   \n",
       "1469            198      False               NaN   \n",
       "2275            474      False               NaN   \n",
       "\n",
       "                                                 source truncated  \\\n",
       "718   <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "1767  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "1896  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "935   <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "1388  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "1109  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "2224  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "695   <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "1469  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "2275  <a href=\"http://twitter.com/download/iphone\" r...     False   \n",
       "\n",
       "                                                   user  \n",
       "718   {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "1767  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "1896  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "935   {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "1388  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "1109  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "2224  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "695   {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "1469  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "2275  {'id': 4196983835, 'id_str': '4196983835', 'na...  \n",
       "\n",
       "[10 rows x 32 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#draw 10 samples for assessment\n",
    "tweet_ids.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    892420643555336193\n",
       "1    892177421306343426\n",
       "2    891815181378084864\n",
       "3    891689557279858688\n",
       "4    891327558926688256\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the tweet ID column\n",
    "tweet_ids.id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2326 entries, 0 to 2325\n",
      "Data columns (total 32 columns):\n",
      "contributors                     0 non-null float64\n",
      "coordinates                      0 non-null float64\n",
      "created_at                       2326 non-null datetime64[ns]\n",
      "display_text_range               2326 non-null object\n",
      "entities                         2326 non-null object\n",
      "extended_entities                2056 non-null object\n",
      "favorite_count                   2326 non-null int64\n",
      "favorited                        2326 non-null bool\n",
      "full_text                        2326 non-null object\n",
      "geo                              0 non-null float64\n",
      "id                               2326 non-null int64\n",
      "id_str                           2326 non-null int64\n",
      "in_reply_to_screen_name          77 non-null object\n",
      "in_reply_to_status_id            77 non-null float64\n",
      "in_reply_to_status_id_str        77 non-null float64\n",
      "in_reply_to_user_id              77 non-null float64\n",
      "in_reply_to_user_id_str          77 non-null float64\n",
      "is_quote_status                  2326 non-null bool\n",
      "lang                             2326 non-null object\n",
      "place                            1 non-null object\n",
      "possibly_sensitive               2194 non-null float64\n",
      "possibly_sensitive_appealable    2194 non-null float64\n",
      "quoted_status                    24 non-null object\n",
      "quoted_status_id                 26 non-null float64\n",
      "quoted_status_id_str             26 non-null float64\n",
      "quoted_status_permalink          26 non-null object\n",
      "retweet_count                    2326 non-null int64\n",
      "retweeted                        2326 non-null bool\n",
      "retweeted_status                 160 non-null object\n",
      "source                           2326 non-null object\n",
      "truncated                        2326 non-null bool\n",
      "user                             2326 non-null object\n",
      "dtypes: bool(4), datetime64[ns](1), float64(11), int64(4), object(12)\n",
      "memory usage: 518.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#get info\n",
    "tweet_ids.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(image_predictions.tweet_id.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2326\n",
       "Name: favorited, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get value count for favorited column\n",
    "tweet_ids.favorited.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality issues\n",
    "\n",
    "1. 181 non-null values in the retweet_id column indicating retweets on the tweet_archive table.\n",
    "\n",
    "2. Timestamp is a string on the tweet_archive table.\n",
    "\n",
    "3. 78 non-null values in the in_reply_to_status_id column indicating a reply tweets on the tweet_archive table.\n",
    "\n",
    "4. Some ratings Denominators are invalid e.g 170 on the tweet_archive table.\n",
    "\n",
    "5. Some ratings numerators are invalid eg 1,776 on the tweet_archive table.\n",
    "\n",
    "6. non descript column name on the tweet IDs table.\n",
    "\n",
    "7. 324 predictions that aren't dog breeds on the image prediction table.\n",
    "\n",
    "8. Non descript column names on the image prediction table.\n",
    "\n",
    "9. some tweets were beyond Aug. 1st 2017 on the twitter archove Table.\n"
   ]
  },
  {
   "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. The dog stage being stored with the texts in the text column of the twitter enhanced table.\n",
    "\n",
    "2. Ratings is a single variable but it was spread into two columns on the twitter enhanced Table.\n",
    "\n",
    "3. 29 columns on the tweets IDs table are not needed because some are repeated columns or they donot help the current analysis in any way.\n",
    "\n",
    "4. 13 columns not neccesary on the archive table(including retweet counts because we wouldnt be making use of retweets for this analysis)\n",
    "\n",
    "5. three predictions with different confidence levels on the image prediction table.\n",
    "\n",
    "6.  Drop unneccesary columns on the image prediction table.\n",
    "\n",
    "6. only one table is neccesary."
   ]
  },
  {
   "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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make copies of original pieces of data\n",
    "twitter_archive2=twitter_archive.copy()\n",
    "image_predictions2=image_predictions.copy()\n",
    "tweets_ids2=tweet_ids.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1. 181 non-null values in the retweet_id column indicating retweets on the tweet_archive table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:  drop all rows without null values in the retweet status id column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive2=twitter_archive2[twitter_archive2.retweeted_status_id.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(twitter_archive2.retweeted_status_id.notnull())\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Timestamp is a string on the tweet_archive table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "source": [
    "#### Define: change timestamp to datetime format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive2['timestamp'] = pd.to_datetime(twitter_archive2['timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2175 entries, 0 to 2355\n",
      "Data columns (total 17 columns):\n",
      "tweet_id                      2175 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                     2175 non-null datetime64[ns]\n",
      "source                        2175 non-null object\n",
      "text                          2175 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                 2117 non-null object\n",
      "rating_numerator              2175 non-null int64\n",
      "rating_denominator            2175 non-null int64\n",
      "name                          2175 non-null object\n",
      "doggo                         2175 non-null object\n",
      "floofer                       2175 non-null object\n",
      "pupper                        2175 non-null object\n",
      "puppo                         2175 non-null object\n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(9)\n",
      "memory usage: 305.9+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_archive2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 78 non-null values in the in_reply_to_status_id column indicating a reply tweets on the tweet_archive table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:  drop all rows without null values in the reply status id column.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive2=twitter_archive2[twitter_archive2.in_reply_to_status_id.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " sum(twitter_archive2.in_reply_to_status_id.notnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Some ratings Denominators are invalid e.g 170 on the tweet_archive table.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:  Drop all rows with rating denominators with 3 digits (>99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive2=twitter_archive2.query('rating_denominator <=99')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive2.query('rating_denominator >99')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Some ratings Numerators are invalid e.g 170 on the tweet_archive table.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:  Drop all rows with rating Numerators with 3 digits (>99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive2=twitter_archive2.query('rating_numerator <=99')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive2.query('rating_numerator >99')\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. non descript column name on the tweet IDs table.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:   The favorite_count column should be renamed to like_count and ID should be changed to tweets ID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_ids2. rename(columns = {'favorite_count':'like_count', 'id':'tweet_id'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['contributors', 'coordinates', 'created_at', 'display_text_range',\n",
       "       'entities', 'extended_entities', 'like_count', 'favorited', 'full_text',\n",
       "       'geo', 'tweet_id', 'id_str', 'in_reply_to_screen_name',\n",
       "       'in_reply_to_status_id', 'in_reply_to_status_id_str',\n",
       "       'in_reply_to_user_id', 'in_reply_to_user_id_str', 'is_quote_status',\n",
       "       'lang', 'place', 'possibly_sensitive', 'possibly_sensitive_appealable',\n",
       "       'quoted_status', 'quoted_status_id', 'quoted_status_id_str',\n",
       "       'quoted_status_permalink', 'retweet_count', 'retweeted',\n",
       "       'retweeted_status', 'source', 'truncated', 'user'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_ids2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. 324 predictions that aren't dog breeds on the image prediction table.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: Remove all predictions without atleast one dog breed prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_predictions2=image_predictions2.query(\"p1_dog== True | p2_dog== True |p3_dog == True\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>jpg_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>p1</th>\n",
       "      <th>p1_conf</th>\n",
       "      <th>p1_dog</th>\n",
       "      <th>p2</th>\n",
       "      <th>p2_conf</th>\n",
       "      <th>p2_dog</th>\n",
       "      <th>p3</th>\n",
       "      <th>p3_conf</th>\n",
       "      <th>p3_dog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, jpg_url, img_num, p1, p1_conf, p1_dog, p2, p2_conf, p2_dog, p3, p3_conf, p3_dog]\n",
       "Index: []"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions2.query(\"p1_dog== False & p2_dog== False &p3_dog == False \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Non descript column names on the image prediction table.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:   replace all \"p\" with prediction to add description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'jpg_url', 'img_num', 'p1', 'p1_conf', 'p1_dog', 'p2',\n",
       "       'p2_conf', 'p2_dog', 'p3', 'p3_conf', 'p3_dog'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_predictions2 = image_predictions2.rename(columns=lambda x: re.sub('p','prediction',x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'jpredictiong_url', 'img_num', 'prediction1',\n",
       "       'prediction1_conf', 'prediction1_dog', 'prediction2',\n",
       "       'prediction2_conf', 'prediction2_dog', 'prediction3',\n",
       "       'prediction3_conf', 'prediction3_dog'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Tweets beyond Aug 1 2017 on the tweets ID table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: drop all tweets beyond Aug. 1st 2017."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive2=twitter_archive2.query(\"timestamp < '2017-08-01'\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>in_reply_to_status_id</th>\n",
       "      <th>in_reply_to_user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>retweeted_status_id</th>\n",
       "      <th>retweeted_status_user_id</th>\n",
       "      <th>retweeted_status_timestamp</th>\n",
       "      <th>expanded_urls</th>\n",
       "      <th>rating_numerator</th>\n",
       "      <th>rating_denominator</th>\n",
       "      <th>name</th>\n",
       "      <th>doggo</th>\n",
       "      <th>floofer</th>\n",
       "      <th>pupper</th>\n",
       "      <th>puppo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [tweet_id, in_reply_to_status_id, in_reply_to_user_id, timestamp, source, text, retweeted_status_id, retweeted_status_user_id, retweeted_status_timestamp, expanded_urls, rating_numerator, rating_denominator, name, doggo, floofer, pupper, puppo]\n",
       "Index: []"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive2.query(\"timestamp > '2017-08-01'\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidiness issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1. The dog stage being stored with the texts in the text column.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:   Extract the dog stage from the text column  into a new \"stage\" column on the twitter_enhanced  table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive2['stage']=twitter_archive2['text'].str.extract(r'(doggo|puppo|floofer|pupper)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "416        NaN\n",
       "830        NaN\n",
       "690        NaN\n",
       "1879       NaN\n",
       "2202       NaN\n",
       "992      doggo\n",
       "1600    pupper\n",
       "2291       NaN\n",
       "1792       NaN\n",
       "280        NaN\n",
       "Name: stage, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive2.stage.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Ratings is a single variable but it was spread into two columns on the twitter enhanced Table.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: divide the numerator rating by the denominator rating and store in a \"ratings\" column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive2['ratings']= twitter_archive2['rating_numerator']/twitter_archive2['rating_denominator']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "424     1.3\n",
       "924     1.4\n",
       "1366    1.0\n",
       "Name: ratings, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_archive2['ratings'].sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3. 29 columns on the tweets IDs table are not needed because some are repeated columns or they donot help the current analysis in any way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: drop all columns except tweet_ids, like_count and retweet_count."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_ids2 = tweets_ids2[[\"tweet_id\",\"like_count\",\"retweet_count\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2326 entries, 0 to 2325\n",
      "Data columns (total 3 columns):\n",
      "tweet_id         2326 non-null int64\n",
      "like_count       2326 non-null int64\n",
      "retweet_count    2326 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 54.6 KB\n"
     ]
    }
   ],
   "source": [
    "tweets_ids2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 13 columns not neccesary on the archive table(including retweet/reply status id because we wouldnt be making use of retweets/replies rows for this analysis)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: drop all coumns except; tweet_id, timestamp, text, name, stage, ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    " twitter_archive2 = twitter_archive2[[\"tweet_id\", 'timestamp', 'text', 'name', 'stage', 'ratings']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2089 entries, 2 to 2355\n",
      "Data columns (total 6 columns):\n",
      "tweet_id     2089 non-null int64\n",
      "timestamp    2089 non-null datetime64[ns]\n",
      "text         2089 non-null object\n",
      "name         2089 non-null object\n",
      "stage        351 non-null object\n",
      "ratings      2089 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(3)\n",
      "memory usage: 114.2+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_archive2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. three predictions with different confidence levels on the image prediction table.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: write a function that select the breed prediction with the highest confidence level among all three predictions and drop the rest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    " def highest_confidence(row):\n",
    "        ''' \n",
    "        fuction that compares the three confidence levels and returns \n",
    "        the prediction with the highest confidence levels\n",
    "        '''\n",
    "        if row['prediction1_conf'] > row['prediction2_conf'] and row['prediction1_conf'] >row['prediction3_conf']:\n",
    "            return row['prediction1']\n",
    "        elif row['prediction2_conf'] > row['prediction1_conf'] and row['prediction2_conf'] >row['prediction3_conf']:\n",
    "            return row['prediction2']\n",
    "        else:\n",
    "            return  row['prediction3']\n",
    "image_predictions2[\"highest_conf_breed\"] = image_predictions2.apply(highest_confidence, axis=1)"
   ]
  },
  {
   "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",
       "    }\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>jpredictiong_url</th>\n",
       "      <th>img_num</th>\n",
       "      <th>prediction1</th>\n",
       "      <th>prediction1_conf</th>\n",
       "      <th>prediction1_dog</th>\n",
       "      <th>prediction2</th>\n",
       "      <th>prediction2_conf</th>\n",
       "      <th>prediction2_dog</th>\n",
       "      <th>prediction3</th>\n",
       "      <th>prediction3_conf</th>\n",
       "      <th>prediction3_dog</th>\n",
       "      <th>highest_conf_breed</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",
       "      <td>Welsh_springer_spaniel</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",
       "      <td>redbone</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",
       "      <td>German_shepherd</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",
       "      <td>Rhodesian_ridgeback</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",
       "      <td>miniature_pinscher</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id                                 jpredictiong_url  \\\n",
       "0  666020888022790149  https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg   \n",
       "1  666029285002620928  https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg   \n",
       "2  666033412701032449  https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg   \n",
       "3  666044226329800704  https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg   \n",
       "4  666049248165822465  https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg   \n",
       "\n",
       "   img_num             prediction1  prediction1_conf  prediction1_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",
       "\n",
       "          prediction2  prediction2_conf  prediction2_dog          prediction3  \\\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",
       "\n",
       "   prediction3_conf  prediction3_dog      highest_conf_breed  \n",
       "0          0.061428             True  Welsh_springer_spaniel  \n",
       "1          0.072010             True                 redbone  \n",
       "2          0.116197             True         German_shepherd  \n",
       "3          0.222752             True     Rhodesian_ridgeback  \n",
       "4          0.154629             True      miniature_pinscher  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_predictions2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.  Drop unneccesary columns on the image prediction table.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: drop all other columns apart from tweet id and  highest conf breed "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_predictions2 = image_predictions2[[\"tweet_id\", 'highest_conf_breed']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1751 entries, 0 to 2073\n",
      "Data columns (total 2 columns):\n",
      "tweet_id              1751 non-null int64\n",
      "highest_conf_breed    1751 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 41.0+ KB\n"
     ]
    }
   ],
   "source": [
    "image_predictions2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. only one table is neccesary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define: merge  all three tables  using the tweets id column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inspecting the three tables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1751 entries, 0 to 2073\n",
      "Data columns (total 2 columns):\n",
      "tweet_id              1751 non-null int64\n",
      "highest_conf_breed    1751 non-null object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 41.0+ KB\n"
     ]
    }
   ],
   "source": [
    "image_predictions2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2089 entries, 2 to 2355\n",
      "Data columns (total 6 columns):\n",
      "tweet_id     2089 non-null int64\n",
      "timestamp    2089 non-null datetime64[ns]\n",
      "text         2089 non-null object\n",
      "name         2089 non-null object\n",
      "stage        351 non-null object\n",
      "ratings      2089 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(1), int64(1), object(3)\n",
      "memory usage: 114.2+ KB\n"
     ]
    }
   ],
   "source": [
    "twitter_archive2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2326 entries, 0 to 2325\n",
      "Data columns (total 3 columns):\n",
      "tweet_id         2326 non-null int64\n",
      "like_count       2326 non-null int64\n",
      "retweet_count    2326 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 54.6 KB\n"
     ]
    }
   ],
   "source": [
    "tweets_ids2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the first two\n",
    "x_master=pd.merge(tweets_ids2, twitter_archive2, on=\"tweet_id\")\n",
    "#add the third\n",
    "master=pd.merge(x_master, image_predictions2, on=\"tweet_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>like_count</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>text</th>\n",
       "      <th>name</th>\n",
       "      <th>stage</th>\n",
       "      <th>ratings</th>\n",
       "      <th>highest_conf_breed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>739932936087216128</td>\n",
       "      <td>3764</td>\n",
       "      <td>988</td>\n",
       "      <td>2016-06-06 21:32:13</td>\n",
       "      <td>Say hello to Rorie. She's zen af. Just enjoyin...</td>\n",
       "      <td>Rorie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>redbone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>766069199026450432</td>\n",
       "      <td>3953</td>\n",
       "      <td>778</td>\n",
       "      <td>2016-08-18 00:28:24</td>\n",
       "      <td>This is Juno. She can see your future. 12/10 h...</td>\n",
       "      <td>Juno</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>redbone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>719339463458033665</td>\n",
       "      <td>4117</td>\n",
       "      <td>1122</td>\n",
       "      <td>2016-04-11 01:41:07</td>\n",
       "      <td>Say hello to Lucy and Sophie. They think they'...</td>\n",
       "      <td>Lucy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>golden_retriever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>807106840509214720</td>\n",
       "      <td>111766</td>\n",
       "      <td>51728</td>\n",
       "      <td>2016-12-09 06:17:20</td>\n",
       "      <td>This is Stephan. He just wants to help. 13/10 ...</td>\n",
       "      <td>Stephan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Chihuahua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>676811746707918848</td>\n",
       "      <td>1308</td>\n",
       "      <td>379</td>\n",
       "      <td>2015-12-15 17:11:09</td>\n",
       "      <td>Say hello to Penny &amp;amp; Gizmo. They are pract...</td>\n",
       "      <td>Penny</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Chihuahua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>696713835009417216</td>\n",
       "      <td>2184</td>\n",
       "      <td>588</td>\n",
       "      <td>2016-02-08 15:14:57</td>\n",
       "      <td>This is Trevith. He's a Swiss Mountain Roadwoo...</td>\n",
       "      <td>Trevith</td>\n",
       "      <td>pupper</td>\n",
       "      <td>0.9</td>\n",
       "      <td>car_mirror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>844973813909606400</td>\n",
       "      <td>13958</td>\n",
       "      <td>2864</td>\n",
       "      <td>2017-03-23 18:07:10</td>\n",
       "      <td>This is Brady. He's a recovering alcoholic. De...</td>\n",
       "      <td>Brady</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Labrador_retriever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>676430933382295552</td>\n",
       "      <td>1267</td>\n",
       "      <td>302</td>\n",
       "      <td>2015-12-14 15:57:56</td>\n",
       "      <td>Meet Duke. He's an Urban Parmesan. They know h...</td>\n",
       "      <td>Duke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>golden_retriever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>673583129559498752</td>\n",
       "      <td>1069</td>\n",
       "      <td>314</td>\n",
       "      <td>2015-12-06 19:21:47</td>\n",
       "      <td>This is Sandy. She loves her spot by the tree....</td>\n",
       "      <td>Sandy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Arctic_fox</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>808733504066486276</td>\n",
       "      <td>7517</td>\n",
       "      <td>2034</td>\n",
       "      <td>2016-12-13 18:01:07</td>\n",
       "      <td>Here's a pupper in a onesie. Quite pupset abou...</td>\n",
       "      <td>None</td>\n",
       "      <td>pupper</td>\n",
       "      <td>1.2</td>\n",
       "      <td>seat_belt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                tweet_id  like_count  retweet_count           timestamp  \\\n",
       "700   739932936087216128        3764            988 2016-06-06 21:32:13   \n",
       "546   766069199026450432        3953            778 2016-08-18 00:28:24   \n",
       "790   719339463458033665        4117           1122 2016-04-11 01:41:07   \n",
       "355   807106840509214720      111766          51728 2016-12-09 06:17:20   \n",
       "1254  676811746707918848        1308            379 2015-12-15 17:11:09   \n",
       "994   696713835009417216        2184            588 2016-02-08 15:14:57   \n",
       "167   844973813909606400       13958           2864 2017-03-23 18:07:10   \n",
       "1266  676430933382295552        1267            302 2015-12-14 15:57:56   \n",
       "1362  673583129559498752        1069            314 2015-12-06 19:21:47   \n",
       "350   808733504066486276        7517           2034 2016-12-13 18:01:07   \n",
       "\n",
       "                                                   text     name   stage  \\\n",
       "700   Say hello to Rorie. She's zen af. Just enjoyin...    Rorie     NaN   \n",
       "546   This is Juno. She can see your future. 12/10 h...     Juno     NaN   \n",
       "790   Say hello to Lucy and Sophie. They think they'...     Lucy     NaN   \n",
       "355   This is Stephan. He just wants to help. 13/10 ...  Stephan     NaN   \n",
       "1254  Say hello to Penny &amp; Gizmo. They are pract...    Penny     NaN   \n",
       "994   This is Trevith. He's a Swiss Mountain Roadwoo...  Trevith  pupper   \n",
       "167   This is Brady. He's a recovering alcoholic. De...    Brady     NaN   \n",
       "1266  Meet Duke. He's an Urban Parmesan. They know h...     Duke     NaN   \n",
       "1362  This is Sandy. She loves her spot by the tree....    Sandy     NaN   \n",
       "350   Here's a pupper in a onesie. Quite pupset abou...     None  pupper   \n",
       "\n",
       "      ratings  highest_conf_breed  \n",
       "700       1.0             redbone  \n",
       "546       1.2             redbone  \n",
       "790       1.0    golden_retriever  \n",
       "355       1.3           Chihuahua  \n",
       "1254      0.9           Chihuahua  \n",
       "994       0.9          car_mirror  \n",
       "167       1.2  Labrador_retriever  \n",
       "1266      1.0    golden_retriever  \n",
       "1362      1.1          Arctic_fox  \n",
       "350       1.2           seat_belt  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master.sample(10)"
   ]
  },
  {
   "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": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "master.to_csv('twitter_archive_master.csv', index= False)\n"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "**import the twitter_archive_master dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_archive_master=pd.read_csv('twitter_archive_master.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Are certain breeds associated with higher ratings?\n",
    "\n",
    "2. Do higer ratings correspond with higher retweet and like counts?\n",
    "\n",
    "3. What are the characteristics of posts with the least ratings?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Are certain breeds associated with higher ratings?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "highest_conf_breed\n",
       "clumber             2.700000\n",
       "military_uniform    1.400000\n",
       "laptop              1.400000\n",
       "upright             1.300000\n",
       "paper_towel         1.300000\n",
       "canoe               1.300000\n",
       "syringe             1.300000\n",
       "Arabian_camel       1.300000\n",
       "Pomeranian          1.286842\n",
       "Saluki              1.250000\n",
       "Name: ratings, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_breed=twitter_archive_master.groupby('highest_conf_breed')['ratings'].mean().sort_values(ascending=False)\n",
    "sorted_breed.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Do higer ratings correspond with higher retweet and like counts and vice versa?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Analyze this using quantiles**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "75% and above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "highest=twitter_archive_master.query('ratings >= ratings.quantile(.75)')\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9362320"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest.like_count.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14142.477341389727"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest.like_count.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2546832"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest.retweet_count.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3847.1782477341389"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest.retweet_count.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "25% and below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "least=twitter_archive_master.query('ratings <= ratings.quantile(.25)')\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1931930"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "least.like_count.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3032.8571428571427"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "least.like_count.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "641288"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "least.retweet_count.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1006.7315541601256"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "least.retweet_count.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. What are the characteristics of posts with the least ratings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "lowest=twitter_archive_master.query('ratings <= ratings.quantile(.10)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "highest_conf_breed\n",
       "Chihuahua             16\n",
       "pug                   14\n",
       "seat_belt              9\n",
       "Labrador_retriever     8\n",
       "golden_retriever       8\n",
       "dalmatian              6\n",
       "Italian_greyhound      6\n",
       "French_bulldog         6\n",
       "Pembroke               5\n",
       "Shetland_sheepdog      5\n",
       "Name: highest_conf_breed, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lowest_breed=lowest.groupby('highest_conf_breed')['highest_conf_breed'].count().sort_values(ascending=False)\n",
    "lowest_breed.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stage\n",
       "pupper    26\n",
       "doggo      4\n",
       "puppo      1\n",
       "Name: stage, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lowest_stage=lowest.groupby('stage')['stage'].count().sort_values(ascending=False)\n",
    "lowest_stage.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "820.45878136200713"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lowest.retweet_count.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2548.1111111111113"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lowest.like_count.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>stage</th>\n",
       "      <th>s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>doggo</td>\n",
       "      <td>16251.559322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>floofer</td>\n",
       "      <td>19575.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pupper</td>\n",
       "      <td>6483.184358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>puppo</td>\n",
       "      <td>20404.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     stage             s\n",
       "0    doggo  16251.559322\n",
       "1  floofer  19575.000000\n",
       "2   pupper   6483.184358\n",
       "3    puppo  20404.500000"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pie1=twitter_archive_master.groupby('stage')['like_count'].mean()\n",
    "pie1.reset_index(name=\"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f719b71dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mylabels = [\"Doggo\", \"Floofer\", \"Pupper\", \"Puppo\"]\n",
    "colors = ( \"cyan\",\"orange\", \"grey\", \"beige\")\n",
    "\n",
    "plt.pie(pie1, labels = mylabels,colors = colors,\n",
    "                                  startangle = 90,\n",
    "                                   autopct='%1.1f%%')\n",
    "plt.legend(title = \"Dog stage:\",loc =\"center left\",\n",
    "          bbox_to_anchor =(1, 0, 0.5, 1))\n",
    "plt.title(\"percentage of mean like count by Dog stages\")\n",
    "plt.axis(\"square\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "pie2=twitter_archive_master.groupby('stage')['retweet_count'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f719b7151d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.pie(pie2, labels = mylabels,colors = colors,\n",
    "                                  startangle = 90,\n",
    "                                   autopct='%1.1f%%')\n",
    "plt.legend(title = \"Dog stage:\",loc =\"center left\",\n",
    "          bbox_to_anchor =(1, 0, 0.5, 1))\n",
    "plt.title(\"Percentage of mean retweet count by Dog stages\") \n",
    "plt.axis(\"square\")\n",
    "plt.show() \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f719bcc32b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar1=twitter_archive_master.groupby('highest_conf_breed')['like_count'].mean().nlargest(10)\n",
    "bar1.plot(kind=\"bar\", title=\"Top 10 breeds by mean like count\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f719b24bcf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar2=twitter_archive_master.groupby('highest_conf_breed')['retweet_count'].mean().nlargest(10)\n",
    "bar2.plot(kind=\"bar\", title=\"Top 10 breeds by mean retweet count\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "extensions": {
   "jupyter_dashboards": {
    "activeView": "report_default",
    "version": 1,
    "views": {
     "grid_default": {
      "cellMargin": 10,
      "defaultCellHeight": 20,
      "maxColumns": 12,
      "name": "grid",
      "type": "grid"
     },
     "report_default": {
      "name": "report",
      "type": "report"
     }
    }
   }
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}